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Historic areas (HAs) are highly vulnerable to natural hazards, including earthquakes, that can cause severe damage, if not total destruction. This paper proposes methods that can be implemented through a geographical information system to assess earthquake-induced physical damages and the resulting impacts on the functions of HAs and to monitor their resilience. For the assessment of damages, making reference to the universally recognised procedure of convoluting hazard, exposure, and vulnerability, this paper proposes (a) a framework for assessing hazard maps of both real and end-user defined earthquakes; (b) a classification of the exposed elements of the built environment; and (c) an index-based seismic vulnerability assessment method for heritage buildings. Moving towards the continuous monitoring of resilience, an index-based assessment method is proposed to quantify how the functions of HAs recover over time. The implementation of the proposed methods in an ad hoc customized WebGIS Decision Support System, referred to as ARCH DSS, is demonstrated in this paper with reference to the historic area of Camerino-San Severino (Italy). Our conclusions show how ARCH DSS can inform and contribute to increasing awareness of the vulnerabilities of HAs and of the severity of the potential impacts, thus supporting effective decision making on mitigation strategies, post-disaster response, and build back better.
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International Journal of
Geo-Information
Article
Assessing Earthquake Impacts and Monitoring Resilience of
Historic Areas: Methods for GIS Tools
Sonia Giovinazzi 1, * , Corinna Marchili 2, Antonio Di Pietro 1, Ludovica Giordano 1, Antonio Costanzo 3,
Luigi La Porta 1, Maurizio Pollino 1, Vittorio Rosato 1, Daniel Lückerath 4, Katharina Milde 4
and Oliver Ullrich 4


Citation: Giovinazzi, S.; Marchili, C.;
Di Pietro, A.; Giordano, L.; Costanzo,
A.; La Porta, L.; Pollino, M.; Rosato,
V.; Lückerath, D.; Milde, K.; et al.
Assessing Earthquake Impacts and
Monitoring Resilience of Historic
Areas: Methods for GIS Tools. ISPRS
Int. J. Geo-Inf. 2021,10, 461.
https://doi.org/10.3390/ijgi10070461
Academic Editors: Wolfgang Kainz
and Himan Shahabi
Received: 13 April 2021
Accepted: 24 June 2021
Published: 6 July 2021
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Copyright: © 2021 by the authors.
Licensee MDPI, Basel, Switzerland.
This article is an open access article
distributed under the terms and
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Attribution (CC BY) license (https://
creativecommons.org/licenses/by/
4.0/).
1Laboratory for the Analysis and Protection of Critical Infrastructures, Casaccia Research Centre, ENEA,
National Agency for New Technologies Energy and Sustainable Economic Development, Via Anguillarese,
301, 00123 Rome, Italy; antonio.dipietro@enea.it (A.D.P.); ludovica.giordano@enea.it (L.G.);
luigi.laporta@enea.it (L.L.P.); maurizio.pollino@enea.it (M.P.); vittorio.rosato@enea.it (V.R.)
2Department of Computer Science and Engineering, Bologna University, Mura Anteo Zamboni, 7,
40126 Bologna, Italy; corinna.marchili@studio.unibo.it
3
Cultural Heritage Lab., INGV, National Institute of Geophysics and Volcanology, Remote Sensing Unit–ONT,
Via P. Bucci-Cube 30/C-7th Floor, 87036 Rende, Italy; antonio.costanzo@ingv.it
4IAIS, Fraunhofer Institute for Intelligent Analysis and Information Systems, Schloss Birlinghoven,
53757 Sankt Augustin, Germany; Daniel.Lueckerath@iais.fraunhofer.de (D.L.);
katharina.milde@iais.fraunhofer.de (K.M.); Oliver.Ullrich@iais.fraunhofer.de (O.U.)
*Correspondence: sonia.giovinazzi@enea.it
Abstract:
Historic areas (HAs) are highly vulnerable to natural hazards, including earthquakes,
that can cause severe damage, if not total destruction. This paper proposes methods that can be
implemented through a geographical information system to assess earthquake-induced physical
damages and the resulting impacts on the functions of HAs and to monitor their resilience. For the
assessment of damages, making reference to the universally recognised procedure of convoluting
hazard, exposure, and vulnerability, this paper proposes (a) a framework for assessing hazard maps
of both real and end-user defined earthquakes; (b) a classification of the exposed elements of the
built environment; and (c) an index-based seismic vulnerability assessment method for heritage
buildings. Moving towards the continuous monitoring of resilience, an index-based assessment
method is proposed to quantify how the functions of HAs recover over time. The implementation
of the proposed methods in an ad hoc customized WebGIS Decision Support System, referred to
as ARCH DSS, is demonstrated in this paper with reference to the historic area of Camerino-San
Severino (Italy). Our conclusions show how ARCH DSS can inform and contribute to increasing
awareness of the vulnerabilities of HAs and of the severity of the potential impacts, thus supporting
effective decision making on mitigation strategies, post-disaster response, and build back better.
Keywords:
geographic information system; historic areas; assessment of earthquake-induced damage
and impact; resilience monitoring
1. Introduction
Historic towns, old urban quarters, villages, and hamlets as well as historic landscapes,
referred to hereafter as historic areas (HAs), play a primary role in community life and
well-being; they are part of the social fabric, the places we live in, and represent our
cultural link with the past, which must be preserved and transmitted to future generations.
HAs hold unique and diverse, tangible and intangible cultural heritage that awakens
curiosity, stimulates creativity, and shapes the identity and thinking of local communities
and visitors [
1
]. HAs also play a significant role in promoting economic development and
the strengthening of social capital as well as cultural diversity [
2
]. Acknowledging that, the
2030 Agenda for Sustainable Development recommends to strengthening efforts to protect
and safeguard the world’s cultural and natural heritage, [
3
] and the Sendai Framework
ISPRS Int. J. Geo-Inf. 2021,10, 461. https://doi.org/10.3390/ijgi10070461 https://www.mdpi.com/journal/ijgi
ISPRS Int. J. Geo-Inf. 2021,10, 461 2 of 27
for Disaster Risk Reduction 2015–2030 states the imperative need to incorporate cultural
heritage in disaster resilience [4].
Recent earthquakes in Italy, Turkey, and Greece (e.g., Umbria-Marche 1997, Izmit and
Duzce 1999, the central Italy sequence of 2016–2017, and Kos 2017, just to mention few of
them) caused the loss of invaluable HAs, pointing to the increasing need for mitigation
actions specifically targeted at preserving cultural heritage and cultural landscapes from
natural disasters [
5
]. Targeted measures are not sufficient unless they are coordinated
into common disaster risk reduction actions and policies that are capable of harmonizing
the contributions of scientists working in different disciplines, policymakers, and a wide
spectrum of groups, e.g., professional groups, public bodies, NGOs, and community
groups. The need for a holistic and participatory approach also applies to the post-disaster
recovery and reconstruction of HAs, as the lack of interdisciplinary approaches might lead
to severe consequences; as an example, post-disaster recovery and reconstruction after the
Calabria (1783), Irpinia (1980), and Belice (1986) earthquakes in Italy ignored the history,
memory, and identity of these places and resulted in the definitive abandonment of several
villages [5].
Geographical information systems (GIS) can be used to inform and support the
complex and interdisciplinary decision-making processes required for the resilience en-
hancement of HAs by providing an environment where knowledge and information can
be shared and accessed easily, thus enabling interactions among different groups and
facilitating interdisciplinary approaches.
This paper presents methods that can be implemented in GIS-based environments
to assess earthquake-induced impacts on HAs and to monitor and compare the resilience
achieved through mitigation strategies in the pre-disaster phase (i.e., in business-as-usual
conditions) or through response and reconstruction strategies in post-disaster circum-
stances. Furthermore, this paper showcases the implementation of the proposed methods
in a WebGIS decision support system (DSS), “Critical Infrastructure Protection Forecasting—
Earthquake Simulator” (CIPCast-ES), which was originally conceived for the protection of
distributed critical infrastructures and has been newly customized for HAs. The use of a
WebGIS decision support system for the analysis of HAs, referred to hereafter as ARCH
DSS, has been defined within the framework of the Horizon 2020 funded research project
known as “Advancing Resilience of Historic Areas Against Climate-related and Other
Hazards” (ARCH) (https://savingculturalheritage.eu/, accessed on 12 May 2021). Some of
the functions of ARCH DSS are demonstrated in this paper through examining the historic
area of the town of Camerino-S.Severino (Italy). This town, among other HAs in central
Italy, was struck by a severe seismic sequence during 2016–2017, the impacts of which
continue to severely affect the well-being and economies of the local communities.
Towards the definition of the ARCH DSS software architecture and of the assessment
methods therein implemented, reference has been made to internationally recognized
GIS-based platforms for seismic risk assessment, e.g., the Global Earthquake Model (GEM)
(https://www.globalquakemodel.org/gem, accessed on 12 May 2021); the HAZUS-MH
platform used by the US Federal Emergency Management Agency (FEMA) [
6
]; the CAPRA
Probabilistic Risk Assessment Platform (https://ecapra.org/, accessed on 12 May 2021),
among several others [
7
16
]. However, none of the existing tools and platforms, to the
authors’ knowledge, specifically target the HAs that are and should be regarded as complex
social–ecological systems where institutional, social, cultural, physical, economic, environ-
mental, and intangible dimensions exist and strongly interact. The research presented in
this paper aims to contribute to the bridging of this gap in line with the recommendations
of the Agenda 2030 [3] and of the Sendai Framework [4].
As far as the resilience monitoring of HAs is concerned, the authors have originally
proposed an approach that, although being computationally simple, aims to be very effec-
tive in informing decision makers of the delays and bottlenecks that impede the functional
recovery of services, organizations, and activities that are essential for the functioning and
thriving of HAs, thus impeding their resilient recovery. The definition of the resilience
ISPRS Int. J. Geo-Inf. 2021,10, 461 3 of 27
monitoring approach proposed in this paper has been built on internationally recognized
literature including the Hyogo Framework for Action 2005–2015 [
17
], the Resilience-based
Earthquake Design (REDi) rating system [
18
], and the PEOPLES framework, which assesses
the resilience of communities by accounting for seven dimensions, namely: population,
environment, organized government services, physical infrastructures, lifestyle, economics,
and social capital [19].
This paper is structured as follows: Section 2presents the proposed materials and
methods for earthquake impact assessment and resilience monitoring as implemented in
the ARCH DSS. As previously mentioned, the same methods can be considered for imple-
mentation in other GIS-based tools in addition to ARCH DSS. After a brief introduction
of essential terms and concepts and the necessary steps to conduct the analysis, details of
the proposed methods are provided as follows: Section 2.1 presents the methods proposed
for the assessment of earthquake-induced physical damage on the built environment of
HAs as a function of hazards (Section 2.1.1), exposure (Section 2.1.2), and the seismic vul-
nerability of the exposed asset (Section 2.1.3); Section 2.2 presents methods to estimate the
functional, social, and economic impacts on HAs induced by the physical damage caused
by an earthquake; Section 2.3 presents methods to monitor the recovery of the essential
functionalities of HAs, i.e., methods to monitor Has’ post-disaster resilience. Section 3
presents the obtained results and in particular, Section 3.1 describes how the software ar-
chitecture of ARCH DSS has been customized to allow for the damage, impact assessment
and resilience monitoring of HAs. Section 3.2 describes the implementation of ARCH DSS
in the historic area of Camerino-S.Severino to showcase some of its functionalities and
the potential of the tool. Potential future research is discussed in the concluding section,
Section 4.
2. Materials and Methods
HAs are considered both in the ARCH project and in this paper as complex and
interconnected systems. HAs are defined as a social–ecological systems that include insti-
tutional, social, cultural, physical, economic, environmental, and intangible dimensions
(Figure 1). This concept is perfectly in line with and expands the historic urban landscape
(HUL) concept and approach defined by UNESCO in 2011 [
1
], which shifts the emphasis
from the sole conservation of monumental architecture to the holistic conservation and
development of HAs. The HUL concept aims to preserve the quality of the human en-
vironment by enhancing the productive and sustainable use of urban spaces, promoting
social and functional diversity, and increasing the economic development of HAs. To the
UNESCO HUL concept and approach, ARCH adds the awareness that climate change and
other natural hazards can severely affect the holistic conservation and development of HAs
and therefore, it is imperative to provide methods and tools for the assessment of their
potential impacts to inform and promote resilience strategies, the effectiveness of which
should be monitored (Figure 1).
The resilience of HAs is defined by the ARCH project as “the sustained ability of a
historic area as a social-ecological system to cope with hazardous events by responding and adapting
in ways that maintain the historic area’s functions and heritage significance, including: Identity,
Integrity, Authenticity”. To support this view on resilience, the ARCH DSS was conceived as
an integrated evaluation tool that allows for the monitoring of the resilience of HAs, and
the informing and enhancing of awareness of the possible damages and impacts that may
be induced by hazardous events, including earthquakes.
Towards that, methods have been implemented in ARCH DSS to:
(Section 2.1) Assess earthquake-induced physical damages to the built environment
of HAs;
(Section 2.2) Assess the resulting impacts on an HA functions;
(Section 2.3) Monitor the recovery of an HA essential functions over time and assess
and compare the resilience achieved.
ISPRS Int. J. Geo-Inf. 2021,10, 461 4 of 27
ISPRS Int. J. Geo-Inf. 2021, 10, x FOR PEER REVIEW 4 of 28
(Section 2.3) Monitor the recovery of an HA essential functions over time and assess
and compare the resilience achieved.
Figure 1. ARCH DSS integrated evaluation tool to support the assessment of earthquake-induced
damage and impacts on HAs’ functions for monitoring resilience.
2.1. Assessing Earthquake-Induced Physical Damage on the Built Environment of Historic Areas
The estimation of seismic-induced physical damages to the built environment is the
first step towards the assessment of the induced impacts on HAs and for the resilience
monitoring. Earthquake-induced damages can be estimated by convoluting the assess-
ment of the hazards that might potentially affect a location, the exposed elements in the
affected location, and the vulnerabilities of the exposed elements to each specific hazard.
This concept can be summarized in the following equation, Equation (1):
Damage Hazard * Exposure * Vulnerability (1)
To assess the earthquake induced physical damage to the built environment of HAs,
we propose the use of the so-called macroseismic-mechanical cross-calibrated method [20,21]
that allows the assessment of damages for different sets of buildings, from groups of
buildings (statistically aggregated in a geographical unit) to single buildings.
Several methods for the seismic vulnerability and risk assessment of both residential
and monumental buildings, including methods specific to HAs where buildings are usu-
ally organized in an aggregated context, exist e.g., [22–28] ,just to provide some examples.
The aforementioned methods are different both in their nature (e.g., observed-based; ex-
pert-based; mechanical-based; or a combination of the previous ones) and in the level of
data and details needed to characterise and assess the vulnerability and risk of the specific
building/set of buildings under analysis.
Reference has been made in this work to [20,21] being a method that can be imple-
mented, starting from a simple typological classification of the exposed asset under anal-
ysis obtained with very basic data and being a method whose reliability can be improved
when more data on the building and/or evidence on the seismic performance of similar
typologies become available. In fact, the proposed method can be defined as a mixed ob-
served-based/expert-based approach; the values proposed for the vulnerability indexes V
and Q have been derived from observed damage data when available or from expert
Figure 1.
ARCH DSS integrated evaluation tool to support the assessment of earthquake-induced damage and impacts on
HAs’ functions for monitoring resilience.
2.1. Assessing Earthquake-Induced Physical Damage on the Built Environment of Historic Areas
The estimation of seismic-induced physical damages to the built environment is the
first step towards the assessment of the induced impacts on HAs and for the resilience
monitoring. Earthquake-induced damages can be estimated by convoluting the assessment
of the hazards that might potentially affect a location, the exposed elements in the affected
location, and the vulnerabilities of the exposed elements to each specific hazard.
This concept can be summarized in the following equation, Equation (1):
Damage Hazard * Exposure * Vulnerability (1)
To assess the earthquake induced physical damage to the built environment of HAs,
we propose the use of the so-called macroseismic-mechanical cross-calibrated method [
20
,
21
]
that allows the assessment of damages for different sets of buildings, from groups of
buildings (statistically aggregated in a geographical unit) to single buildings.
Several methods for the seismic vulnerability and risk assessment of both residential
and monumental buildings, including methods specific to HAs where buildings are usually
organized in an aggregated context, exist e.g., [
22
28
], just to provide some examples. The
aforementioned methods are different both in their nature (e.g., observed-based; expert-
based; mechanical-based; or a combination of the previous ones) and in the level of data
and details needed to characterise and assess the vulnerability and risk of the specific
building/set of buildings under analysis.
Reference has been made in this work to [
20
,
21
] being a method that can be im-
plemented, starting from a simple typological classification of the exposed asset under
analysis obtained with very basic data and being a method whose reliability can be im-
proved when more data on the building and/or evidence on the seismic performance of
similar typologies become available. In fact, the proposed method can be defined as a
mixed observed-based/expert-based approach; the values proposed for the vulnerability
indexes V and Q have been derived from observed damage data when available or from
expert judgement otherwise. Provided the availability of data on observed damage, it is
possible to calibrate and tune the proposed V and Q indexes as deemed appropriate.
ISPRS Int. J. Geo-Inf. 2021,10, 461 5 of 27
Equation (2), proposed by [
20
,
21
], has been therefore implemented in the ARCH DSS
for the assessment of earthquake-induced physical damage in the built environment:
µD=2.51+tanhIEMS98 +αVβ
Q (2)
where
µD
is the expected mean degree of damage for single buildings or set of aggregated
buildings; the seismic hazard is represented using the macroseismic intensity I
EMS-98
,
according to the European Macroseismic Scale EMS-98 [
29
]; the seismic vulnerability of
single or sets of aggregated buildings and structures is assessed in terms of the vulnerability
V and ductility Q indexes;
α
and
β
are coefficients. Values of the coefficients
α
and
β
can
be assumed as
α
= 6.25,
β
= 13.1; these values resulted by deriving Equation (2) from
the damage probability matrices implicitly defined by EMS-98 [
29
] using a combined
probabilistic and fuzzy-logic approach [
20
,
21
]. Some authors, i.e., [
29
,
30
], suggest to use
β
= 12.7 in lieu of
β
= 13.1 after repeating the derivation of Equation (2) under modified
assumptions than the ones at the base of the original derivation of the macroseismic
vulnerability curves [
20
,
21
]. A critical discussion on the resulting differences (that are
indeed minor) while using
β
= 12.7 in lieu of
β
= 13.1 in Equation (2) is out of the scope of
this paper and can be the subject of future research.
The level of damage to each building or group of buildings can be allocated on
the basis of the resulting
µD
according to the EMS-98 physical damage scale [
31
], which
considers five damage levels, i.e., D1, D2, D3, D4, D5, plus the absence of damage, D0,
enabling the qualitative description of the earthquake-induced physical damage to the
structural and non-structural components of buildings (Table 1and Figure 2).
In the following sub-sections, methods for the assessment damage as a function of
hazards, exposure, and vulnerability as for Equation (1) are presented, and are specifically
expressed in terms of the variables needed in Equation (2), namely: hazard assessment in
terms of I
EMS-98
(Section 2.1.1); identification and characterization of the exposed elements
within HAs (Section 2.1.2); and seismic vulnerability assessment of the exposed elements
(Section 2.1.3) in terms of the V and Q indexes.
Table 1. Attribution of damage levels Dk (k = 0–5) based on µDranges resulting from Equation (2).
Dk Damage Levels µDRanges
D0 No damage 0 µD< 0.5
D1 Slight damage, cracking of non-structural elements 0.5 µD< 1
D2 Moderate damage, major damage to non-structural elements
minor damage to load bearing ones
1µD< 2
D3 Heavy damage, significant damage to load bearing elements 2 µD< 3
D4 Very heavy damage, partial structural collapse 3 µD< 4
D5 Destruction, serious destruction of structural and non-structural
elements or total collapse
4µD5
ISPRS Int. J. Geo-Inf. 2021,10, 461 6 of 27
ISPRS Int. J. Geo-Inf. 2021, 10, x FOR PEER REVIEW 6 of 28
Figure 2. Assessment and representation of earthquake-induced physical damage in ARCH DSS.
In the following sub-sections, methods for the assessment damage as a function of
hazards, exposure, and vulnerability as for Equation (1) are presented, and are specifically
expressed in terms of the variables needed in Equation (2), namely: hazard assessment in
terms of I
EMS-98
(Section 2.1.1); identification and characterization of the exposed elements
within HAs (Section 2.1.2); and seismic vulnerability assessment of the exposed elements
(Section 2.1.3) in terms of the V and Q indexes.
2.1.1. Assessing and Representing Seismic Hazard
The hazard component of the ARCH DSS aims to provide an estimation of the ex-
pected ground motion, using either a deterministic or a probabilistic approach (or a com-
bination of the two). Ground motion is the movement of the earth’s surface produced by
the waves that are generated by earthquakes (due to sudden slip or rupture on a fault) or
explosions (due to sudden pressure at the explosive source) travelling through the earth
and along its surface, and can be also amplified by particular soil or morphology condi-
tions of the territory (Figure 3).
Figure 3. Example of factors that influence the intensity and extent of ground motion generated by
an earthquake: (1) position of the hypocenter (or of the epicenter, i.e., hypocenter projection on the
surface); (2) distance between earthquake source and site of interest (e.g., points A, B, C, D in the
picture); (3) local site conditions (soil type profile and morphology).
Figure 2. Assessment and representation of earthquake-induced physical damage in ARCH DSS.
2.1.1. Assessing and Representing Seismic Hazard
The hazard component of the ARCH DSS aims to provide an estimation of the expected
ground motion, using either a deterministic or a probabilistic approach (or a combination
of the two). Ground motion is the movement of the earth’s surface produced by the waves
that are generated by earthquakes (due to sudden slip or rupture on a fault) or explosions
(due to sudden pressure at the explosive source) travelling through the earth and along
its surface, and can be also amplified by particular soil or morphology conditions of the
territory (Figure 3).
ISPRS Int. J. Geo-Inf. 2021, 10, x FOR PEER REVIEW 6 of 28
Figure 2. Assessment and representation of earthquake-induced physical damage in ARCH DSS.
In the following sub-sections, methods for the assessment damage as a function of
hazards, exposure, and vulnerability as for Equation (1) are presented, and are specifically
expressed in terms of the variables needed in Equation (2), namely: hazard assessment in
terms of I
EMS-98
(Section 2.1.1); identification and characterization of the exposed elements
within HAs (Section 2.1.2); and seismic vulnerability assessment of the exposed elements
(Section 2.1.3) in terms of the V and Q indexes.
2.1.1. Assessing and Representing Seismic Hazard
The hazard component of the ARCH DSS aims to provide an estimation of the ex-
pected ground motion, using either a deterministic or a probabilistic approach (or a com-
bination of the two). Ground motion is the movement of the earth’s surface produced by
the waves that are generated by earthquakes (due to sudden slip or rupture on a fault) or
explosions (due to sudden pressure at the explosive source) travelling through the earth
and along its surface, and can be also amplified by particular soil or morphology condi-
tions of the territory (Figure 3).
Figure 3. Example of factors that influence the intensity and extent of ground motion generated by
an earthquake: (1) position of the hypocenter (or of the epicenter, i.e., hypocenter projection on the
surface); (2) distance between earthquake source and site of interest (e.g., points A, B, C, D in the
picture); (3) local site conditions (soil type profile and morphology).
Figure 3.
Example of factors that influence the intensity and extent of ground motion generated by
an earthquake: (1) position of the hypocenter (or of the epicenter, i.e., hypocenter projection on the
surface); (2) distance between earthquake source and site of interest (e.g., points A, B, C, D in the
picture); (3) local site conditions (soil type profile and morphology).
Deterministic approaches allow for the estimation of the expected ground motion and
its related consequences for any selected seismic event under analysis. On the other hand,
probabilistic approaches allow for the estimation of the probability of occurrence of a certain
ground motion and the relative consequences in a certain time frame due to all of the
possible seismogenic sources that might generate an earthquake in the HA. The selection of
ISPRS Int. J. Geo-Inf. 2021,10, 461 7 of 27
either the probabilistic approach, or the deterministic approach, or both of them depends
on the goal of the individual study [
6
16
]. In the ARCH project reference is made to a
deterministic approach since the goal is to assess damage and impact scenarios that can
support an aware and informed decision making process. A deterministic model able
to represent the geographical and physical extent of the ground motion produced by an
earthquake has been therefore fine-tuned; Figure 4represents the workflow used in the
ARCH DSS to generate deterministic ground motion maps for both real and user-defined
events. The workflow can be summarized in three basic steps:
1.
Characterization of the specific earthquake event under analysis: i.e., hypocenter location
and characteristics of the seismogenic source (i.e., location and characteristics of faults
or fault systems) that has generated the earthquake (for a historic event) or that
might generate the earthquake (for user-defined scenarios): the magnitude of the
energy that has been released (for a real event) or that can potentially be released (for
user-defined events);
2.
Characterization of specific local site conditions that might lead to amplification, i.e., to an
increase in the size of the ground motion;
3.
Selection and implementation of a Ground Motion Prediction Equation (GMPE), i.e., at-
tenuation relations, describing how the earthquake ground motion decreases as the
distance between the earthquake source and the site under analysis increases while
accounting for possible amplification due to the ground propagation properties (point
2, above).
As for Step (1), i.e., the characterization of the specific earthquake event under analysis,
in case of the occurrence of a real event, data are automatically collected and collated by
the ARCH DSS. A web-service, developed through ObsPy toolbox [
32
,
33
], receives the
measured characteristics of the earthquakes in near real-time; characteristics of seismic
events with a magnitude greater than 3 and an epicenter located within (or close to) Eu-
ropean countries are then stored in the ARCH DSS geodatabase. To this end, the system
continuously polls the earthquake catalogues managed by the European Mediterranean
Seismological Centre (EMSC) [
34
] and by the National Institute of Geophysics and Vol-
canology (INGV) [
35
] to obtain information according to the International Federation of
Digital Seismograph Networks (FDSN) standard protocols [36].
For user-defined seismic event scenarios, the ARCH DSS provides end-users with sev-
eral geo-datasets (Figure 4) to support them in appropriately selecting the characteristics of
the simulated earthquake (e.g., the maximum historical event from a pertinent seismogenic
source or the maximum earthquake compatible with the known tectonic framework, etc.).
These datasets, relating to both Italian and European territories, include representations of
historic earthquakes [
37
39
], known seismogenic sources [
40
,
41
], and probabilistic seismic
hazard maps used as a reference in seismic design codes [4244].
As far as the characterization of the specific geological and geomorphological con-
ditions of the territory under analysis is concerned (Step 2), geodatabases reporting the
results of seismic microzoning studies [
45
] that provide spatial information about local
effects related to geological and geotechnical conditions on ground motion (e.g., [
46
,
47
])
and hydrogeological hazards (landslides and floods) [
48
] are embedded in the ARCH
DSS geodatabase. In particular, two different V
S30
maps (i.e. maps representing the av-
erage shear-wave velocity in the uppermost 30 m) are included: worldwide topographic
slopes [
49
,
50
] that are also reported as a regional detail in the world map published by the
US Geological Survey (USGS) [
51
] and the seismic soil classification of Italy obtained by
terrain geomorphological classification integrated with a large amount of data obtained
using a seismic microzoning dataset [52].
Step (3) requires the selection of a GMPE used in seismic hazard analysis to evaluate
the expected level of ground shaking at any given site for the earthquake event defined in
Step (1). The ground shaking can be described in terms of the level of acceleration, velocity,
and displacement of the earth’s surface induced by the earthquake waves. Conventionally
used shaking intensity measures (IMs) include, among others, peak ground acceleration
ISPRS Int. J. Geo-Inf. 2021,10, 461 8 of 27
(PGA); peak ground velocity (PGV); peak ground displacement (PGD); pseudo-spectral
accelerations (PSAs); pseudo-spectral velocities (PSVs); and pseudo-spectral displacements
(PSDs). The values of these IMs are generally provided as median values by GMPEs, with
their associated uncertainty being due to both inherent randomness, referred to as aleatory
variability, and epistemic uncertainty from of a lack of knowledge.
ISPRS Int. J. Geo-Inf. 2021, 10, x FOR PEER REVIEW 8 of 28
Survey (USGS) [51] and the seismic soil classification of Italy obtained by terrain geomor-
phological classification integrated with a large amount of data obtained using a seismic
microzoning dataset [52].
Figure 4. Workflow of ARCH DSS hazard assessment module: assessment of deterministic ground
shaking maps in peak ground acceleration (PGA) using GMPE by [53] and PGA to I
MCS
and I
EMS98
conversion [54]
.
Step (3) requires the selection of a GMPE used in seismic hazard analysis to evaluate
the expected level of ground shaking at any given site for the earthquake event defined in
Step (1). The ground shaking can be described in terms of the level of acceleration, veloc-
ity, and displacement of the earth’s surface induced by the earthquake waves. Conven-
tionally used shaking intensity measures (IMs) include, among others, peak ground acceler-
ation (PGA); peak ground velocity (PGV); peak ground displacement (PGD); pseudo-
spectral accelerations (PSAs); pseudo-spectral velocities (PSVs); and pseudo-spectral dis-
placements (PSDs). The values of these IMs are generally provided as median values by
GMPEs, with their associated uncertainty being due to both inherent randomness, re-
ferred to as aleatory variability, and epistemic uncertainty from of a lack of knowledge.
The calculation of ground shaking maps in terms of different IMs, via GMPEs, de-
pend on different characteristics and parameters (Figure 4 and Equation (3)) including
moment magnitude, (M
w
); style of faulting (SoF), i.e., main focal mechanism—normal
fault, thrust, or strike-slip; distance from the site under analysis to the seismic source R
that can be represented by point-source distance metrics such as hypocentral distance and
Figure 4.
Workflow of ARCH DSS hazard assessment module: assessment of deterministic ground
shaking maps in peak ground acceleration (PGA) using GMPE by [53] and PGA to IMCS and IEMS98
conversion [54].
The calculation of ground shaking maps in terms of different IMs, via GMPEs, de-
pend on different characteristics and parameters (Figure 4and Equation (3)) including
moment magnitude, (M
w
); style of faulting (SoF), i.e., main focal mechanism—normal
fault, thrust, or strike-slip; distance from the site under analysis to the seismic source R
that can be represented by point-source distance metrics such as hypocentral distance and
epicentral distance or the rupture distance, i.e., the closest distance to an extended rupture
of plane fault (R
Rup
), or Joyner–Boore distance (R
JB
), i.e., the closest distance to the surface
projection of an extended fault [
55
]; a simplified term representative of the site effects on
the amplification of the ground motion (A), e.g., the average shear-wave velocity in the
uppermost 30 m (VS30), among others.
IMs = f(Mw, SoF, R, A) (3)
ISPRS Int. J. Geo-Inf. 2021,10, 461 9 of 27
In the current version of the ARCH DSS, the most recent GMPEs for the Italian territory
have been implemented, namely [53,56] among others.
Once ground motion maps are assessed in terms of the shaking intensity measure, IMS
such as PSAs and PGAs, conversion equations are implemented in the ARCH DSS to obtain
Macroseismic Intensity (I) maps. In particular, the relationship proposed by [
54
] allows
us to obtain the Macroseismic Intensity estimation with regard to Mercalli Cancani Siberg
Macroseismic Intensity scale I
MCS
[
57
]. A further correlation [
58
] is applied to convert
I
MCS
to intensities measured according to the European Macroseismic Scale I
EMS-98
[
29
],
which is the official Macroseismic Intensity scale adopted in Europe and is the hazard input
required in Equation (2).
2.1.2. Classifying the Exposure in the Built Environment
The exposure assessment deals with the characterizations (e.g., identification of
type/category), quantifications (e.g., number of surface occupied, number of users, etc.),
and geolocation of the items exposed to a certain hazard. Critical elements of the built
environment of HAs exposed to the seismic hazard include, among others, cultural heritage
buildings, their content and the intangible values of culture that they represent.
The concept of cultural heritage (CH) has varied considerably over time, with the
current definition including both tangible and intangible dimensions. The ARCH project
adopts the classification proposed by UNESCO [
59
] that was later revised by the ICOMOS
Climate Change and Cultural Heritage Working Group in 2019 [
60
], where CH elements
are classified according to six main categories: (1) moveable heritage, (2) archaeological
sites, (3) buildings and structures, (4) cultural landscapes, (5) associated and traditional
communities, and (6) intangible heritage.
Building on the above proposed categories [
59
,
60
], as well as on further ones sourced
from key references [
61
,
62
], the ARCH classification proposed for CH items is provided in
Table 2, where for each category, different types and examples are presented. For the CH
type “Architecture (historic and monumental buildings” under the category “Buildings
and structures”, further examples are provided in Table 3by both sourcing and combining
examples from [6163].
Table 2. ARCH Classification for CH included in HAs: Categories; Types and Examples.
CH Categories CH Types CH Examples
Moveable
heritage Works of monumental sculpture and painting Paintings, sculptures, furniture, wall paints
Archaeological
resources
Archaeological finds Pottery, artefacts, inscriptions
Archaeological materials Bones, textiles, ceramic
Archaeological sites Tombs, caves
Archaeological monuments Sacred places, temples, burial sites
Stratigraphic elements Stratigraphic tests and finds
Buildings and
structures
Architecture (historic and monumental buildings) Castles, theatres, churches, cathedrals *
Groups of separate or connected buildings Streets, warehouse complexes, harbours
Historical nuclei Historic centres of towns and cities
Cultural
landscapes
Parks/gardens Parks, cemeteries, botanical gardens
Combined works of nature and humankind Agricultural landscapes, mining landscapes
Associated and
traditional
communities Traditional groups, communities and individuals Indigenous peoples
Intangible
heritage
Oral traditions and expressions Proverbs, poems, tales
Performing arts Theatre, music, dances
Social practices, rituals, festive events Festivals, religious rituals, ceremonies
Traditional craftsmanship (knowledge and skills) Crafts, traditional agricultural techniques, masonry
Knowledge and practices concerning nature and
universe Traditional ecological wisdom, traditional healing
systems
* Further examples area provided in Table 3.
ISPRS Int. J. Geo-Inf. 2021,10, 461 10 of 27
2.1.3. Assessing the Seismic Vulnerability of Cultural Heritage Buildings
The ARCH DSS assesses the seismic vulnerability of CH items within an HA according
to the so-called Macroseismic-Mechanical cross-calibrated Method [
20
,
21
], which allows for the
seismic vulnerability assessment of different sets of buildings, from group of buildings,
statistically aggregated in a geographical unit, to single buildings. According to [
20
,
21
], the
seismic vulnerability of single buildings or group of buildings is measured in terms of both
avulnerability index V and a ductility index Q. As far as single buildings are concerned,
the vulnerability index V is attributed by considering how different building types (e.g.,
masonry buildings, reinforced concrete building, or timber) might have a different seismic
performance and how further constructive or geometric peculiarities of the buildings
(such as the state of maintenance, plane, and vertical regularity the presence of specific
constructive, such features like tie-roads) might modify it. The ductility index Q accounts
for the rate of damage progression with the increase of the ground shaking intensity. The
background methodology and the operational steps for its implementation on residential
and ordinary buildings can be found in [20,21].
The focus of this paper is on how to implement the approach for the CH category
Buildings and Structures” (Table 2). Examples of types of CH buildings and structures of
the specific European architecture are listed in Table 3; buildings and structures recognized
in each type can be considered to have a similar seismic vulnerability. Based on this
assumption, values for the V and Q indexes can be attributed to each CH type identified in
Table 3; these values have been deduced from observed damage data when available and
applying an expert judgement procedure otherwise [
59
62
]. In particular, some of the V
and Q indexes reported in Table 3were already reported in [
59
62
] and already used for
the evaluation of the seismic risk on UNESCO CH in Europe [
63
]. The V and Q indexes
reported in italics in Table 3(i.e., for columns, temples, triliths, canopies, chapels, stadiums,
synagogues, storage tanks) are introduced in the present work.
Table 3. Typological vulnerability V and ductility Q indexes for ARCH building and structures.
Building and
Structures V0QBuilding and
Structures V0Q
Arch bridges 0.46 2.3 Towers 0.78 2.0
Castles 0.54 2.0 Triliths 0.58 3.0
Churches 0.89 3.0 Triumphal arches 0.58 2.6
Columns 0.74 3.0 Canopy 0.46 2.0
Monasteries 0.74 2.3 Chapel 0.62 3.0
Mosques 0.81 2.6 Lighthouse 0.74 3.0
Obelisks 0.74 3.0 Stadium 0.54 2.0
Palaces 0.62 2.3 Synagogue 0.81 2.6
Temples 0.62 2.3 Storage Tanks 0.74 3.0
It is worth highlighting that a vulnerability assessment based on a typological clas-
sification has to be regarded only as a starting point, as it does not take into account the
distinctiveness of a single building and does not allow for the singling out of the most
vulnerable building/structures within a type. To refine the vulnerability assessment, a
quick survey is recommended aiming to collect, via a proper survey form, further relevant
parameters, such as the state of maintenance, the quality of the constructive material (e.g.,
for stone, brick, mortar from both a chemical and mechanical point of view), the structural
regularity (in plan and in elevation), the size and slenderness of relevant structural ele-
ments, the interaction with adjacent structures, the presence of retrofitting interventions,
and/or of vernacular aseismic devices.
ISPRS Int. J. Geo-Inf. 2021,10, 461 11 of 27
This concept is summarized in Equation (4), where the vulnerability index Vis defined
as the sum of the typological vulnerability index V
0
attributed according to the build-
ing type (Table 3) plus the sum of the vulnerability index modifiers V
k
attributed to each
constructive, geometric, or conservation peculiarity of building (Table 4).
V=V0+ΣVk(4)
Table 4.
Values of vulnerability index modifiers V
k
to be applied at both single and aggregated buildings and V
k
peculiar
for aggregated building only.
Vulnerability Factor Vk
Both isolated and aggregated/connected buildings
State of Maintenance Very bad
+0.08
Bad
+0.04
Medium
0
Good
0.04
Quality of Materials Bad
+0.04
Medium
0
Good
0.04
Planimetric
Configuration
Irregular
+0.04
Regular
0
Symmetrical
0.04
Elevation Configuration Irregular
+0.02
Regular
0.02
Interventions on
structural system
Disruptive 1
+0.08
Effective retrofitting
0.08
Anti-seismic
vernacular devices
Effective devices 2
0.08
Site morphology Ridge
+0.08
Slope
+0.04
Flat
0
For aggregated/connected buildings only
Position of the building in
the aggregate
Header
+0.06
Corner
+0.04
Isolated
0
Included
0.04
Height difference with
adjacent buildings
Higher on both sides
+0.04
Higher on one side
+0.02
Lower on both sides
0.02
Lower on one
side
0.04
Discontinuity between
adjacent building
Typological
discontinuity
+0.03
Staggered floors
+0.02
Anti-seismic vernacular in
aggregated context
Effective devices 3
0.04
1
Examples of disruptive interventions on buildings might include superimposed floor, partial or total demolition of internal/external
bearing walls, structural discontinuity caused by enlargement or creation of a new opening, addition of building annexes, and merging of a
portion of adjacent buildings.
2
Example of effective anti-seismic vernacular devices might include tie-roads, effective connections between
perpendicular walls and between walls and horizontal floor, buttresses as well as connecting elements (headers) in multi-leaf masonry
walls [64]. 3Example of effective anti-seismic vernacular devices in an aggregated context might include counter thrust arches.
To evaluate the vulnerability of buildings in an aggregate context, it is first necessary
to clearly identify each single structural unit within the larger, aggregated context with the
help of a GIS map representing the building aggregate plan and with the help of a front
view of the building aggregate. For the vulnerability assessment, all V
k
representing the
peculiarities of the single building (upper part of Table 4) as well as those representing
the aggregated context in which the building is inserted (lower part of Table 4) need to
be calculated.
For some architecture types, such as churches and palaces, more complex indicator-
based approaches can be implemented by filling out specific survey forms [6568].
ISPRS Int. J. Geo-Inf. 2021,10, 461 12 of 27
For the ARCH category Moveable Heritage (MH) (Table 2), the vulnerability assessment
should consider whether the MH is anchored to a wall or a support (Adopted in ARCH
from the STORM project, http://www.storm-project.eu/, accessed on 12 May 2021); in
absence of a proper anchoring system, the MH item is very vulnerable to falls and breaks
if subjected to seismic action. In the presence of an effective anchoring system, the same
vulnerability of the building and therefore the same damage level can be attributed to
the MH items. In the absence of an effective anchoring system, a higher vulnerability
can be assumed and therefore, an increase in one damage-level can be assumed (e.g., if
damage level D3 has been estimated for a building subjected to a certain earthquake, a
damage level D4 can be assumed for the non-anchored MH items therein contained). If the
information on the presence and effectiveness of such anchoring devices is not available,
it is reasonable to assume the worst-case scenario, i.e., absence of the anchoring system.
As far as the ARCH categories “Cultural Landscape” and “Archaeological resources” are
concerned (Table 2), the vulnerability can be assessed as a function of the potentiality for
seismic-induced geotechnical hazards (e.g., liquefaction, faulting, rock fall, and landslides).
This topic will be the subject of a future report.
2.2. Assessing Resulting Impacts on Functions of Historic Areas
Once physical damages to the built environment of HAs are assessed, the potential
impacts on the social and economic dimensions of the HAs can be assessed starting from
the assessment of the “downtime” of the essential services/activities, i.e., the time during
which services and activities that are considered essential for the functioning and thriving
of HAs are out of action or unavailable for use. Further to the services that are usually
considered essential for the functioning of urban areas (such as critical infrastructures,
health systems, schools among others), the functionality of the services and activities that
contribute to social cohesion and identity building as identified in Table 5along with
the local productive and commercial activities and craftsmanship activities considered
essential for HAs.
Table 5.
Activities and services identified as essential to maintain the functionality of HAs and the
well-being and economies of local communities (adapted from Agenda|Culture 2030).
Social-Cohesion Activities Identity-Building Activities
movie/cinema/film festival national or local festival participation
theatre representation or dance show celebrations of cultural/historic events
live musical performance community rites/events/ceremonies
historical/cultural park or a heritage site
amateur cultural practices, community practices *
museum, an art gallery or a crafts exposition
membership of cultural associations
* of youth culture, popular culture, ethnic culture, etc.
Downtime can be roughly associated with the physical damage level Dk as proposed
in Table 6, adapted from the U.S. Resiliency Council (USRC) Building Rating System.
Table 6
also reports the correlation between the loss of building usability and downtime
with the impact on the intangible heritage associated with the building itself.
Instead of the rough correspondence between Dk and downtime proposed in Table 6,
downtime functions can be defined for monitoring the functional recovery of a building in
a more precise way, depending on its particular function and occupancy, based on either
available data, expert opinion, or both. In residential buildings, functional recovery is
related to regaining occupant comfort and livable conditions—the lights are on, water flows,
and heating and air conditioning are operating; [
18
] proposes very detailed downtime
functions for residential buildings. For non-residential buildings, the functional recovery
implies the resumption of the building’s specific functions (e.g., emergency services and
typical services in hospitals, business activity in offices and retail, social-cohesion and
identity building activities in HA buildings).
ISPRS Int. J. Geo-Inf. 2021,10, 461 13 of 27
Table 6.
Possible correlation between earthquake-induced physical Damage level, residual building
usability and downtime, and long-term impact on intangible heritage attribute.
Dk Impact on Building
Usability Downtime Impact to Intangible
Heritage Attributes 1
D5 No longer usable N/A Highly
significant/total Loss
D4 Non usable long term Within years to decades Very Significant Loss
D3 Non usable long term Within month to a year Significant Loss
D2 Temporally non usable for
inspections and propping Within weeks to months Partial/Moderate loss in
heritage value
D1 Temporally non usable
for inspections Within days to weeks Minor loss in
heritage value
D0 Usable Immediate to days
No loss in heritage values
1Adopted in ARCH from the STORM project (http://www.storm-project.eu/, accessed on 12 May 2021).
Based on the functional impact on the building and time required for its functional
recovery, it is possible to estimate socio-economic impacts in terms of key performance
indicators (KPIs). To this end, effective KPIs for monitoring socio-economic consequences
due to the loss of functionality of HA buildings have been extracted from the 2030 Agenda
goals, targets, and indicators (https://unstats.un.org/sdgs/indicators/indicators-list/,
accessed on 12 May 2021) and from the Culture|2030 Indicators (https://whc.unesco.org/
en/culture2030indicators/, accessed on 12 May 2021).
KPI for monitoring the impacts of the loss of functionality of HA buildings on social-
cohesion and identity building activities have been included in the ARCH DSS, being
adapted as proposed in Agenda|Culture 2030:
KPI
SCA
, the ratio of citizens within the total local population engaged in social-
cohesion activities (SCA);
KPI
IBA
, the ratio of citizens within the total local population engaged in identity
building activities (IBA).
KPI
SCA
and KPI
IBA
have to be estimated for each one of the items listed in Table 6
either in an aggregated or disaggregated way and have to be estimated during business-
as-usual times (referred hereafter with the subscript t
0
), KPI
t0
, as a baseline reference
measure to allow for impact assessment and recovery/resilience monitoring in post-disaster
circumstances (as explained in Section 2.3).
KPI for monitoring the impacts on the local economy have been included in the ARCH
DSS, being adapted as proposed in Agenda|Culture 2030:
KPI
CGDP
, monitoring the Contribution of Cultural Gross Domestic Product (CGDP) from
cultural activities or from the culture sector in general, Equation (5);
KPI
CEP
, monitoring the Cultural Employment (CEP), i.e., the ratio of citizens engaged in
cultural and creative employment occupations within the total employed population
in a given site, Equation (6).
KP ICGDP =n
1GVACA isic
GDP (5)
where GVA
CA isic
is the gross value added from cultural activities identified based on the
International Standard Industrial Classification of Economic Activities (ISIC), and GDP is
the gross domestic product in a given country or in the HA.
KP ICE P =n
1CEC A isco
EP (6)
where CE
CA isco
is the total number of the persons employed in cultural occupations identi-
fied based on the International Standard Classification of Occupations (ISCO)
ISPRS Int. J. Geo-Inf. 2021,10, 461 14 of 27
(https://www.ilo.org/public/english/bureau/stat/isco/ accessed on 5 July 2021) and EP
is the total number of the employed population.
2.3. Monitoring Resilience of Historic Areas
Once relevant KPIs are identified and assessed, it is possible to monitor the resilience
of the HA system for each specific KPI. In the international literature [
19
,
64
,
69
,
70
], resilience
is essentially measured as the total functionality lost over time, and the functionality is
defined as a piecewise function that captures the reduction in system performance from
0% (total loss of system functionality) to 100% (no reduction in system functionality). The
work of [
71
] introduces the idea of using function-based metrics to measure resilience.
Valuable frameworks and approaches for assessing and monitoring the seismic resilience
of cities and local communities are available in the literature, e.g., [
72
79
], among others.
Building on the international literature, the present work originally proposes different
resilience metrics, Equation (7), to be assessed either as individual metrics or as a combined
metric, aiming to quantifying the effectiveness of recovery, response, and built-back-better
interventions in HAs after an earthquake event.
R=f(RO,RRi,RA)(7)
where:
R
O
is the resilience origin, i.e., the starting point after the crises event, providing a
measure of the extent of the KPI loss to be recovered, Equation (8);
R
Ri
is the resilience rate, providing a measure of the effectiveness of each istepwise
intervention, Equation (9);
R
A
is the resilience area, providing the overall quantification of the effectiveness of the
interventions to the recovery of the KPI over time, Equation (11).
From the aforementioned resilience metrics, a combined Rmetric can be assessed,
either as a mean value of R
O
,R
Ri
, and R
A
, as the minimum or maximum value among
them, or by combining them as deemed more appropriate (e.g., normalized sum, weighted
normalized sum, etc.).
The R
O
,resilience origin, Equation (8), is assessed as a function of the value assumed
by the monitored KPI after the earthquake (or other crises events), in comparison to the
baseline value, KPI
t0
, assessed pre-event during business-as-usual time. The target KPI
T
can be either assumed equal to the baseline KPI
T
=KPI
t0
or higher than that if the crisis
event is regarded as an opportunity to build back better, i.e., KPIT>KPIt0.
RO=1KP ITKPIt0
KP IT
(8)
The R
Ri
the resilience rate, Equation (9), is the slope of the recovery of the KPI over time
and measures the effectiveness of each istepwise intervention
RRi =Ri+1Ri
ti+1Ri
(9)
where Ri, Equation (10), is measured as a function of the value of the KPI
ti
measured at
time t
i
when a variation of policies, available resources, etc., is causing a change in the
rate of the KPI recovery (which can be either positive, i.e., increasing the resilience rate, or
negative, i.e., decreasing the resilience rate).
Ri=1KP ITKPIti
KP IT
(10)
ISPRS Int. J. Geo-Inf. 2021,10, 461 15 of 27
Finally, the resilience area, Equation (11), is assessed as the overall area of the polygon
identified by the recovery path over time.
RA=
T
i=0((1Ri)+(1Ri+1))·(ti+1ti)
2(11)
To perform the resilience assessment according to the proposed resilience metrics, it is
necessary to fix an observation time range, referred to as target time t
T
(e.g., a few months,
one year, a decade, or any other amount of time) and assume it as a unit value tT=1. The
time from the seismic event t
0
=0 to the target time t
T
= 1 will therefore be measured as a
proper fraction (e.g., if the monitoring time is a decade t
T
=1=10y, a check made after one
year will be at time t1=0.1 = 1y).
In this way, a 10-level resilience scale, from 0 to 1 (Table 7) can be used to judge the
resilience efficiency class achieved with different interventions during the monitoring time;
here, the idea is to use the same class system usually adopted to judge energy efficiency,
i.e., from A4 (i.e., A+++), greatest efficiency, to G, lowest efficiency.
Table 7.
10 level resilience scale from 0 to 1 proposed to attribute a resilience class as a function of the values resulting from
the resilience metrics R,RO, and RA.
Resilience Class A4 A3 A2 A1 B C D E F G
R,RO,Ri,RA0–0.1 0.1–0.2 0.2–0.3 0.3–0.4 0.4–0.5 0.5–0.6 0.6–0.7 0.7–0.8 0.8–0.9 0.9–1
Figure 5showcases how the proposed resilience metrics RO,RI, and RAcan be easily
assessed by plotting them on a time–resilience, t-R, plot, where the resilience target R
T
and
time target t
T
are assumed as a unitary value. We can imagine that the plots are referring
to a KPI related to the usability of a building damaged after an earthquake. On the left
side, after a stall time, repair work starts and the usability is brought back to normal before
the target time; on the right side, although the loss in usability is less than the previous
example, the stall time is longer and the rate of recovery proceeds at a lower rate compared
to example (a) and is further delayed, starting from time t1.
It is worth underlying that if the KPI could not be fully recovered during the time
range selected for the observation (i.e., from t = 0 to t
T
), the value of the last observed R
i
result will be less than the targeted resilience, i.e., Ri< RT.
ISPRS Int. J. Geo-Inf. 2021, 10, x FOR PEER REVIEW 16 of 28
example, the stall time is longer and the rate of recovery proceeds at a lower rate com-
pared to example (a) and is further delayed, starting from time t
1
.
Figure 5. Examples of time–resilience, t-R, plots showcasing the use of the proposed resilience metrics R
O
, R
Ri
, and R
A.
It is worth underlying that if the KPI could not be fully recovered during the time
range selected for the observation (i.e., from t = 0 to t
T
), the value of the last observed R
i
result will be less than the targeted resilience, i.e., R
i
<
R
T.
3. Results
3.1. ARCH DSS Software Architecture
The ARCH DSS was developed by customizing the CIPCast DSS, a GIS-based DSS
developed as part of the EU-funded FP7 project CIPRNet (Critical Infrastructures Prepar-
edness and Resilience Research Network, [80,81]) for real-time and operational (24/7)
monitoring and risk analysis of built and natural assets, with special focus on the analysis
of interdependent critical infrastructures such as electric power, water, telecommunica-
tion, road networks, and strategic buildings [82–85]. CIPCast DSS built as a combination
of free/open-source software environments, detailed presentation and explanation of
which is provided in [80,81].
The CIPCast DSS is based on a multi-tier architecture including distinct layers orga-
nized according to a hierarchical order: the presentation layer, service layer, middleware
layer, and persistence layer. In the CIPCast DSS, the Web-GIS application represents a
fundamental tool for decision-making and monitoring processes as a specific interface of
the CIPCast DSS. The basic geospatial information, the considered assets, and the pro-
cessed maps and scenarios can be visualized and queried via the Internet, through a com-
mon browser. Geographical and spatial elements can be selected both graphically and
spatially, using topologically defined spatial relationships (such as contiguity, adjacency,
intersection, etc.) or using specific descriptive attributes (qualitative/quantitative).
CIPCast-ES (Earthquake Simulator) is an extension of the CIPCast DSS that is specif-
ically aimed at simulating seismic hazard maps and assessing earthquake-induced phys-
ical damage and impacts. In this case, the Web-GIS application operates as the geographic
interface of the CIPCast-ES simulator for (a) the geographic visualization, (b) the param-
eter input and selection of the simulation approach (e.g., GMPE), (c) the execution of an
earthquake simulation, (d) the visualization of the damage scenario on the assets of inter-
est, and (e) the interactive consultation of the results.
To build the ARCH DSS, the existing functionalities of the CIPCast DSS and CIPCast-
ES have been exploited and extended to allow for the implementation of approaches that
have been specifically identified for the vulnerability, physical damage, and impact as-
sessment and resilience monitoring of HA.
Figure 5. Examples of time–resilience, t-R, plots showcasing the use of the proposed resilience metrics RO,RRi, and RA.
3. Results
3.1. ARCH DSS Software Architecture
The ARCH DSS was developed by customizing the CIPCast DSS, a GIS-based DSS
developed as part of the EU-funded FP7 project CIPRNet (Critical Infrastructures Pre-
ISPRS Int. J. Geo-Inf. 2021,10, 461 16 of 27
paredness and Resilience Research Network, [
80
,
81
]) for real-time and operational (24/7)
monitoring and risk analysis of built and natural assets, with special focus on the analysis
of interdependent critical infrastructures such as electric power, water, telecommunication,
road networks, and strategic buildings [
82
85
]. CIPCast DSS built as a combination of
free/open-source software environments, detailed presentation and explanation of which
is provided in [80,81].
The CIPCast DSS is based on a multi-tier architecture including distinct layers orga-
nized according to a hierarchical order: the presentation layer, service layer, middleware
layer, and persistence layer. In the CIPCast DSS, the Web-GIS application represents a
fundamental tool for decision-making and monitoring processes as a specific interface of
the CIPCast DSS. The basic geospatial information, the considered assets, and the processed
maps and scenarios can be visualized and queried via the Internet, through a common
browser. Geographical and spatial elements can be selected both graphically and spatially,
using topologically defined spatial relationships (such as contiguity, adjacency, intersection,
etc.) or using specific descriptive attributes (qualitative/quantitative).
CIPCast-ES (Earthquake Simulator) is an extension of the CIPCast DSS that is specifi-
cally aimed at simulating seismic hazard maps and assessing earthquake-induced physical
damage and impacts. In this case, the Web-GIS application operates as the geographic
interface of the CIPCast-ES simulator for (a) the geographic visualization, (b) the parameter
input and selection of the simulation approach (e.g., GMPE), (c) the execution of an earth-
quake simulation, (d) the visualization of the damage scenario on the assets of interest, and
(e) the interactive consultation of the results.
To build the ARCH DSS, the existing functionalities of the CIPCast DSS and CIPCast-
ES have been exploited and extended to allow for the implementation of approaches
that have been specifically identified for the vulnerability, physical damage, and impact
assessment and resilience monitoring of HA.
The presentation layer (or dashboard) of the ARCH DSS (top layer in Figure 6) includes
different graphical user interfaces (GUI) viewers and a set of GUI widgets:
GUI viewers provide dedicated GIS viewers with different layers including seismic
vulnerability, physical damage, functional impact, social and economic consequences,
and intangible value losses;
GUI widgets allow the user to define the characteristic of the simulation to be run,
e.g., simulations of real events versus user-defined events; deterministic versus prob-
abilistic simulations and definitions of the specific characteristics of the event to be
simulated (e.g., for deterministic events, coordinates of the epicenter, hypocentral
depth, magnitude; for probabilistic events, the selected return period); selection of
the scale of analysis, e.g., territorial versus local; selection of the minimum unit of
analysis, i.e., single building versus census, borough or municipal area; selection of
the specific exposed asset under analysis; selection of the phase to be simulated, such
as pre-event emergency management and post-event recovery and reconstruction.
The service layer of the ARCH DSS (second layer in Figure 6) contains several compo-
nents, namely:
Simulated hazard assessment service: this service allows the assessment of hazards
either in a deterministic way (end-user defined event and/or replication of occurred
events) to be used for impact scenario or in a stochastic way, to be used as risk scenario;
Real time hazard assessment service: this service allows the assessment of hazard
maps in real time for an on-going event, provided the availability of the required
information to characterize the event (e.g., for generating ground-motion maps after
an earthquake, it is necessary to know the coordinates of the earthquake epicenter or
hypocenter and the event magnitude; for generating a pluvial flooding map, mm/h,
digital elevation model, and characteristics of the drainage system);
Physical damage scenario assessment Manager: this service allows the prediction
of (physical) damage scenarios for historic areas based on the hazard outputs from
ISPRS Int. J. Geo-Inf. 2021,10, 461 17 of 27
the hazard assessment services and the vulnerability of exposed objects (example see
Figure 2and Figure 9);
Impact and consequence prediction manager: this service estimates physical functional
(including cascading effects) impacts on historical areas and related systems (e.g.,
ecosystems, social systems, and intangible value systems connected to historic areas)
considering the natural, social, and built environments they are embedded in as well
as (inter)dependencies with critical infrastructures.
The middleware layer of the ARCH DSS contains the following components:
Data access manager: this component implements the procedures necessary to gather
data coming from external sources on a 24/7 basis, such as meteorological data needed
to feed models and simulations;
GeoPlatform interaction service: this service is responsible for login and logout func-
tions and the uploading of user data;
Security and user account manager: this service is responsible for managing user
accounts and controlling access to the data.
The persistence layer of the ARCH DSS contains the following components:
Public GeoPlatform DB: this database stores GIS layers (such as territorial, socio-
economical, and technological infrastructure data) compliant with INfrastructure for
SPatial InfoRmation in Europe (INSPIRE) and Open Geospatial Consortium (OGC)
standards;
Private local DB: this database contains information specific to each historic area.
For each of those, a separate DSS instance will be created. “Local” here refers to the
historic area;
General DB: this database contains data common for all instances.
ISPRS Int. J. Geo-Inf. 2021, 10, x FOR PEER REVIEW 18 of 28
Figure 6. ARCH WebGIS DSS software architecture conceived as a multi-tier architecture including 4 layers: presentation,
service, middleware, and persistence.
3.2. Implementation of ARCH DSS in Camerino-S.Severino HA
The central area of Italy is highly seismically active (Figure. 7). In 2016, this area ex-
perienced one of the most disruptive seismic sequences in recent time. The sequence
started on 24th August 2016, when an earthquake with a moment magnitude Mw = 6.0,
referred to as the “Amatrice earthquake” and a hypocentral depth of 8 km followed by an
Mw = 5.4 aftershock caused 299 deaths and significant destruction of numerous towns
and villages in central Italy. On 26th October, two earthquakes (Mw = 5.4 and Mw = 5.9)
occurred in an adjacent area along the Umbria and Marche region boundaries. The
stronger 26th October event, i.e., Mw = 5.9, is referred to as the “Visso earthquake”. A further
main shock of Mw = 6.5 occurred on October the 30th in the Umbria region, referred to as
the “Norcia earthquake”. The “Visso earthquake” has been chosen as a case study for the seis-
mic simulation and its impact on the cultural heritage in the HA of Camerino-San Sev-
erino. The choice is motivated by the widespread damage caused to the cultural heritage
buildings and cultural sites in the analyzed area. In fact, the Visso earthquake induced the
collapse of the tower of Santa Maria in Via church (already severely damaged by the pre-
vious earthquake events of the sequence), which is one of the focus points selected in the
ARCH project for the Camerino case study.
Figure 6.
ARCH WebGIS DSS software architecture conceived as a multi-tier architecture including 4 layers: presentation,
service, middleware, and persistence.
ISPRS Int. J. Geo-Inf. 2021,10, 461 18 of 27
3.2. Implementation of ARCH DSS in Camerino-S.Severino HA
The central area of Italy is highly seismically active (Figure 7). In 2016, this area
experienced one of the most disruptive seismic sequences in recent time. The sequence
started on 24 August 2016, when an earthquake with a moment magnitude Mw = 6.0,
referred to as the “Amatrice earthquake” and a hypocentral depth of 8 km followed by an
Mw = 5.4 aftershock caused 299 deaths and significant destruction of numerous towns
and villages in central Italy. On 26 October, two earthquakes (Mw = 5.4 and Mw = 5.9)
occurred in an adjacent area along the Umbria and Marche region boundaries. The stronger
26 October event, i.e., Mw = 5.9, is referred to as the “Visso earthquake”. A further main
shock of Mw = 6.5 occurred on October the 30th in the Umbria region, referred to as the
Norcia earthquake”. The “Visso earthquake” has been chosen as a case study for the seismic
simulation and its impact on the cultural heritage in the HA of Camerino-San Severino. The
choice is motivated by the widespread damage caused to the cultural heritage buildings
and cultural sites in the analyzed area. In fact, the Visso earthquake induced the collapse
of the tower of Santa Maria in Via church (already severely damaged by the previous
earthquake events of the sequence), which is one of the focus points selected in the ARCH
project for the Camerino case study.
According to the methods introduced in Section 2.1.1, the ARCH DSS has been used
for the first time to assess the ground shaking map generated by the Visso earthquake
(Figure 8) by specifying the event characteristics (i.e., position of the epicentre, hypocenter
depth, magnitude) in the input widget; by selecting the GMPE; and by accounting for the
possible soil amplification. As reported in Section 2.1.1, simulated ground shake maps can
be substituted and/or integrated with the ground shake maps resulting from national [
86
]
or urban [
87
] accelerometric seismic networks as soon as they are made available by INGV.
Figure 7.
ARCH DSS screenshot representing data from geodatabases of: seismogenic sources; M
W
> 3 earthquake events;
INGV ground shaking map of the 26th October 2016 “Castel Sant’Angelo sul Nera” Mw = 5.4 earthquake.
ISPRS Int. J. Geo-Inf. 2021,10, 461 19 of 27
ISPRS Int. J. Geo-Inf. 2021, 10, x FOR PEER REVIEW 19 of 28
Figure 7. ARCH DSS screenshot representing data from geodatabases of: seismogenic sources; M
W
>
3 earthquake events;
INGV ground shaking map of the 26th October 2016 “Castel Sant’Angelo sul Nera” Mw = 5.4 earthquake.
According to the methods introduced in Section 2.1.1, the ARCH DSS has been used
for the first time to assess the ground shaking map generated by the Visso earthquake
(Figure 8) by specifying the event characteristics (i.e., position of the epicentre, hypocenter
depth, magnitude) in the input widget; by selecting the GMPE; and by accounting for the
possible soil amplification. As reported in Section 2.1.1, simulated ground shake maps can
be substituted and/or integrated with the ground shake maps resulting from national [86]
or urban [87] accelerometric seismic networks as soon as they are made available by
INGV.
Figure 8.
Ground-shaking map for the Visso Earthquake (PGA in [%g]) simulated using the ARCH DSS (26 October 2016,
Mw = 5.9, Lat 42,91 N, Long 13,13 E).
Concerning the characterization of the exposure, the ARCH geodatabase contains
data from the Sistema Informatico Territoriale Carta del Rischio SIT-CR for the area of interest.
SIT-CR is the example of a shared and accessible CH database, a best practice that should
be adopted internationally. SIT-CR was conceived and realized by Istituto Superiore per la
Conservazione e il Restauro (ISCR, previously Istituto Centrale per il Restauro) under the
Italian Ministry for Cultural and Environmental Heritage (MIBAC) as the main reference
tool for the safeguarding of Italian Cultural Heritage. SIT-CR is a data repository for
heritage buildings and cultural sites, capable of processing data and statistics for each
one of the 8100 Italian municipalities. SIT-CR is available to MIBAC, local and regional
bodies for developing safeguards, conservation, and maintenance interventions as well as
restoration and urban planning measures for cultural heritage. The SIT-CR also proved to be
a very useful tool for supporting post-disaster emergency management and reconstruction.
After recent catastrophic earthquake events in Italy (e.g., Abruzzo 2009, Emilia 2012, the
central Italy seismic sequence 2016–2017), SIT-CR was used to identify the list of heritage
buildings and cultural sites that were located in the area affected by the ground shaking
and as a basis for collecting further information on the affected cultural heritage. The
joint use of SIT-CR and GIS DSS such the ARCH DSS can provide great opportunities for
supporting the planning and prioritizing of emergency and reconstruction interventions as
showcased both later in this paper and in past papers for a different WebGIS DSS [73].
The CH buildings and structures included in the SIT-CR for Camerino-San Severino HA
have been classified according to the ARCH classification for CH (Table 2). Both typological
vulnerability V and ductility Q indexes (Table 3) were attributed to them. By implementing
the method proposed in Section 2.1 of the paper, ARCH DSS allowed us to assess the
physical damage scenario (Figure 9) as a function of the seismic vulnerability implemented
for the different building types and as a function of the ground shake map simulated by
the ARCH DSS for the Visso earthquake (Figure 8).
ISPRS Int. J. Geo-Inf. 2021,10, 461 20 of 27
ISPRS Int. J. Geo-Inf. 2021, 10, x FOR PEER REVIEW 20 of 28
Figure 8. Ground-shaking map for the Visso Earthquake (PGA in [%g]) simulated using the ARCH DSS (26 October 2016,
Mw = 5.9, Lat 42,91 N, Long 13,13 E).
Concerning the characterization of the exposure, the ARCH geodatabase contains
data from the Sistema Informatico Territoriale Carta del Rischio SIT-CR for the area of interest.
SIT-CR is the example of a shared and accessible CH database, a best practice that should
be adopted internationally. SIT-CR was conceived and realized by Istituto Superiore per
la Conservazione e il Restauro (ISCR, previously Istituto Centrale per il Restauro) under
the Italian Ministry for Cultural and Environmental Heritage (MIBAC) as the main refer-
ence tool for the safeguarding of Italian Cultural Heritage. SIT-CR is a data repository for
heritage buildings and cultural sites, capable of processing data and statistics for each one
of the 8,100 Italian municipalities. SIT-CR is available to MIBAC, local and regional bodies
for developing safeguards, conservation, and maintenance interventions as well as resto-
ration and urban planning measures for cultural heritage. The SIT-CR also proved to be a
very useful tool for supporting post-disaster emergency management and reconstruction.
After recent catastrophic earthquake events in Italy (e.g., Abruzzo 2009, Emilia 2012, the
central Italy seismic sequence 2016–2017), SIT-CR was used to identify the list of heritage
buildings and cultural sites that were located in the area affected by the ground shaking
and as a basis for collecting further information on the affected cultural heritage. The joint
use of SIT-CR and GIS DSS such the ARCH DSS can provide great opportunities for sup-
porting the planning and prioritizing of emergency and reconstruction interventions as
showcased both later in this paper and in past papers for a different WebGIS DSS [73].
The CH buildings and structures included in the SIT-CR for Camerino-San Severino
HA have been classified according to the ARCH classification for CH (Table 2). Both ty-
pological vulnerability V and ductility Q indexes (Table 3) were attributed to them. By im-
plementing the method proposed in Section 2.1 of the paper, ARCH DSS allowed us to
assess the physical damage scenario (Figure 9) as a function of the seismic vulnerability
implemented for the different building types and as a function of the ground shake map
simulated by the ARCH DSS for the Visso earthquake (Figure 8).
Figure 9. Assessed damages for the CH of Camerino HA after Visso Earthquake (26 October 2016,
Mw = 5.9).
Figure 9.
Assessed damages for the CH of Camerino HA after Visso Earthquake (26 October 2016,
Mw = 5.9).
Table 8reports the results of the simulation in terms of the estimated number of
buildings and structures damaged at different damage levels, D
k
(k = 0–5) of the EMS-98
damage scale (Table 1).
Table 8.
Number of buildings and structures damaged at different damage levels D
k
(k = 0–5) of the
EMS-98 damage scale resulting from the ARCH DSS simulation of the Visso earthquake.
D0 D1 D2 D3 D4 D5 Total
churches 115 206 369 128 79 0 897
palaces 573 63 55 2 0 0 693
towers 70 51 25 21 5 0 172
chapel 120 0 0 0 0 0 120
arch bridges 67 4 0 0 0 0 71
castles 57 1 1 0 0 0 59
monasteries 18 6 3 3 0 0 30
The resulting damages are definitely in line with the damages and impacts that
were observed to affect CH buildings following the central Italy seismic sequence 2016–
2017 [
88
]. A detailed validation of the results against the observed evidence is out of the
scope of this work and will be the subject of a further publication. However, it is worth
mentioning that what was obtained from the scenario run with ARCH DSS is coherent with
the observed damages and impacts (in terms of residual usability) suffered by masonry
churches after the 2016–2017 Central Italy seismic sequence as reported in [
89
] in terms
of percentages of churches affected at different levels of different ranges of PGA. Once
the damage assessment was performed, the ARCH DSS can be used to assess the impacts
(e.g., in terms of residual usability and functionality of the buildings, possible impacts on
artwork contained in the building, or estimation of the number of artwork in danger of
being evacuated from damaged buildings) and to monitor the resilience of the repair and
recovery phases for the buildings, for the HA as a whole and its the local communities,
ISPRS Int. J. Geo-Inf. 2021,10, 461 21 of 27
their economy, and well-being. Figure 10 shows an example of the monitoring for the KPI
of usability for two iconic churches in Camerino, i.e., San Venanzio and Santa Maria in Via.
They were both unusable after the 26 October 2016 earthquake because of the earthquake-
induced damage to their structural and non-structural elements. Santa Maria in Via Church
is still unusable to date, while San Venanzio is back to being fully operational, and it was
reopened on 15 December 2019 to the public for religious ceremonies and celebrations
(thanks to the commitment of the local community and to the financial support received
from a private donation from the Arvedi Buschini Foundation). Assuming the observation
time range of the resilience assessment from the earthquake event on 26 October 2016 to
date and the implementation of the approach proposed in Section 2.3, the resilience class of
the two iconic churches, i.e San Venanzio and Santa Maria in Via, can be judged as A+ and
G, respectively, according to the resilience scale introduced in Table 7, as far the KPI for
usability is concerned.
ISPRS Int. J. Geo-Inf. 2021, 10, x FOR PEER REVIEW 22 of 28
Figure 10. ARCH DSS exposure GUI showcasing the different types of CH in Camerino HA and pictures of the two iconic
churches, i.e., San Venanzio and Santa Maria in Via.
4. Discussion and Conclusions
The paper presents methods for assessing earthquake-induced physical damages and
the resulting impacts on the functions of HAs as well as methods for monitoring the resil-
ience of HAs. The paper showcases the implementation of the proposed methods within
a WebGIS tool named ARCH DSS. The proposed methods can also be implemented within
other GIS-based platforms and tools. However, the embedment and use of the proposed
methods within an integrated platform such as ARCH DSS is recommended in order to
consider and analyze the interdependencies and cascading effects that might be critical
and cannot be disregarded in complex systems such as HAs where the built-environment,
social, economic, and bio-ecologic dimensions co-exist and interact with the intangible
dimension of values, perceptions, social-cohesion, and sense of belonging that makes his-
torical areas unique.
In particular, this paper focuses on methods for the assessment of earthquake-in-
duced physical damages on the built-environment of HAs and methods for the estimation
of the impacts on vital functions of HAs, both tangible and intangible, resulting from such
physical damages. The paper furthermore proposes methods for estimating how the
loss/reduction of HA functions might lead to economic consequences (e.g., loss/reduction
of revenue from economic activities peculiar and vital for the local economy of the HAs)
and consequences on the social-cohesion and the sense of belonging/identity of local com-
munities. However, HAs are complex social–ecological systems, and further to the pro-
posed methods for assessing how earthquakes might affect the physical properties of HAs
and consequently the social and economic ones, further work is needed to embed valuable
approaches to support building resilience in GIS systems, especially with regard to the
additional properties of HAs, such as the institutional, social, cultural, physical, economic,
environmental, and intangible properties.
The implementation of the proposed methods on the HA of Camerino-San Severino
was undertaken with the aim of demonstrating the potential of the ARCH DSS or similar
GIS systems to inform and support decision-making processes towards the preservation
and thriving of HAs in the different phases of the disaster risk reduction cycle:
Figure 10.
ARCH DSS exposure GUI showcasing the different types of CH in Camerino HA and pictures of the two iconic
churches, i.e., San Venanzio and Santa Maria in Via.
4. Discussion and Conclusions
The paper presents methods for assessing earthquake-induced physical damages
and the resulting impacts on the functions of HAs as well as methods for monitoring the
resilience of HAs. The paper showcases the implementation of the proposed methods
within a WebGIS tool named ARCH DSS. The proposed methods can also be implemented
within other GIS-based platforms and tools. However, the embedment and use of the
proposed methods within an integrated platform such as ARCH DSS is recommended
in order to consider and analyze the interdependencies and cascading effects that might
be critical and cannot be disregarded in complex systems such as HAs where the built-
environment, social, economic, and bio-ecologic dimensions co-exist and interact with the
intangible dimension of values, perceptions, social-cohesion, and sense of belonging that
makes historical areas unique.
In particular, this paper focuses on methods for the assessment of earthquake-induced
physical damages on the built-environment of HAs and methods for the estimation of
the impacts on vital functions of HAs, both tangible and intangible, resulting from such
physical damages. The paper furthermore proposes methods for estimating how the
ISPRS Int. J. Geo-Inf. 2021,10, 461 22 of 27
loss/reduction of HA functions might lead to economic consequences (e.g., loss/reduction
of revenue from economic activities peculiar and vital for the local economy of the HAs)
and consequences on the social-cohesion and the sense of belonging/identity of local
communities. However, HAs are complex social–ecological systems, and further to the
proposed methods for assessing how earthquakes might affect the physical properties of
HAs and consequently the social and economic ones, further work is needed to embed
valuable approaches to support building resilience in GIS systems, especially with regard
to the additional properties of HAs, such as the institutional, social, cultural, physical,
economic, environmental, and intangible properties.
The implementation of the proposed methods on the HA of Camerino-San Severino
was undertaken with the aim of demonstrating the potential of the ARCH DSS or similar
GIS systems to inform and support decision-making processes towards the preservation
and thriving of HAs in the different phases of the disaster risk reduction cycle:
Before the occurrence of an earthquake event, to plan for mitigation actions aiming to
reduce possible future impacts;
In the aftermath of a seismic event, to inform and support the emergency management
phase by providing first responders the estimation of the location and extent of
damages and impacts for a timely, effective, and efficient allocation of resources;
After an event, to support resilience building and build back better policies
and strategies
.
Further work is planned to enhance the current potentialities of the ARCH DSS tool
both from the perspective of the sophistication and reliability of the methods proposed
and embedded in ARCH DSS and from the perspective of increasing the interactivity and
usefulness of the tool for the users.
As far as the method improvement is concerned, the hazard module will be con-
nected and empowered with the capabilities of the hazard module of the OpenQuake
engine (https://docs.openquake.org/oq-engine/1.2/calculators/hazard.html, accessed
on 26 January 2021). Work for the inclusion of additional vulnerability, damage, impact,
and consequence assessment functions is also planned [
22
28
,
90
]; similarly, the addition of
further frameworks for classifying the exposure [
91
], indicators, and performance metrics
for measuring impacts [92] is envisioned.
As for increasing the potentialities, interactivity, and usefulness of the ARCH DSS tool
for users, the direction of near-future research targets all of the following. First, ARCH
DSS will be extended to support participatory decision-making processes including multi-
criteria analysis. In this way, during business-as-usual times, ARCH DSS is envisioned to
become a tool for promoting the co-design and co-production processes for the identifica-
tion of community-based mitigation and resilience strategies. The aim is to enable different
stakeholders to use the ARCH DSS for a two-step approach: (1) performing what-if analy-
ses based on user-defined scenarios to find data and evidence regarding “what would happen
if an earthquake, stronger than the one already occurred centuries ago in this historic area, would
strike again? Would the cultural patrimony survive? Would the local community be affected? And,
if so, to what extent? What can we do to mitigate the risk for impacts? How can we choose among
different strategies for preserving the quality of the social dimension, and the prosperity of the local
economies?”; and (2) facilitating discussion and reconciliation between the different and
often conflicting needs of the stakeholders during the definition of management plans for
historic areas and cultural heritage, which include risk mitigation and contingency plans.
Second, to support the build back better and resilience enhancement process, ARCH
DSS will be equipped with the capabilities to handle dynamic data (i.e., data that varies
over time) so that it can became a platform where KPIs related to socio-economic and
intangible impacts can be dynamically monitored to check the effectiveness of response
and recovery strategies. Furthermore, ARCH DSS will be integrated with a database of
resilience building measures to support the formulation and comparison of resilience
building strategies [
88
94
]. Additionally, the functionalities of the ARCH DSS will be
integrated in a resilience assessment framework based on the UNDRR Disaster Resilience
Scorecards for cities [
95
] and buildings [
96
] that include further—non-physical—resilience
ISPRS Int. J. Geo-Inf. 2021,10, 461 23 of 27
aspects (e.g., community resilience) to support the formulation of comprehensive resilience
action plans for historic areas in agreement with the urban adaptation cycle [97].
Finally, future research will look at defining and at integrating approaches such as
agent-based modelling and systems dynamics methods within ARCH DSS thus enabling a
more advanced and more realistic representation and assessment of the complex interac-
tions between human decisions, tangible, and intangible dimensions of historic areas and
the representation and simulation of multi-hazard scenarios and of their cascading effects.
Future research will also look at the possibility to connect ARCH DSS to an urban digital
twin model to support the resilience of historic areas and local communities in a smart and
holistic perspective. A prototype of an urban digital twin has been proposed to support
urban planning decision making processes [
98
]; the same idea can certainly be explored in
the context of decision support systems for mitigating risks from natural hazards and for
building resilience.
Author Contributions:
Conceptualization, Sonia Giovinazzi, Ludovica Giordano, Antonio Costanzo,
Vittorio Rosato and Daniel Lückerath; software, Antonio Di Pietro, Luigi La Porta; validation, Corinna
Marchili, Antonio Di Pietro, Ludovica Giordano, Antonio Costanzo and Maurizio Pollino; formal anal-
ysis, Sonia Giovinazzi, Corinna Marchili, Antonio Di Pietro, Ludovica Giordano, Antonio Costanzo
and Maurizio Pollino; resources, Ludovica Giordano, Antonio Costanzo; cata curation, Corinna
Marchili, Antonio Di Pietro, Ludovica Giordano, Antonio Costanzo, Luigi La Porta and Maurizio
Pollino; writing—Review and Editing, Sonia Giovinazzi, Ludovica Giordano, Antonio Costanzo,
Maurizio Pollino, Daniel Lückerath, Katharina Milde and Oliver Ullrich; visualization, Sonia Giov-
inazzi, Antonio Costanzo and Maurizio Pollino; supervision, Sonia Giovinazzi; project administration,
Daniel Lückerath; cunding acquisition, Sonia Giovinazzi, Vittorio Rosato, Daniel Lückerath and
Oliver Ullrich. All authors have read and agreed to the published version of the manuscript.
Funding:
The main features of the ARCH DSS come from the exploitation of a DSS realized within
the EU FP7 project “CIPRNet”, which has allowed the design and the realization of the core structure
of the CIPCast DSS. Subsequently, the basic CIPCast DSS has been improved by using funds provided
by the Italian Ministry for University and Research through PON project RAFAEL (“System for Risk
Analysis and Forecast for Critical Infrastructures in the AppenninEs dorsaL Regions”, grant agree-
ment no. ARS01_00305). Previous activities made in PON Project RoMA (Resilience enhancement of
Metropolitan Areas, project nr. SCN_00064) have provided the basic technologies on which the ARCH
DSS has been built upon. These funding agencies are greatly acknowledged. The research activities
and the results that are the subject of the present work have been funded by the European project
“ARCH—Advancing Resilience of historic areas against Climate-related and other Hazards” funded
by the European Union’s Horizon 2020 research and innovation programme under grant agreement
no. 820999. The sole responsibility for the content of this publication lies with the authors. It does
not necessarily represent the opinion of the European Union. Neither the EASME nor the European
Commission are responsible for any use that may be made of the information contained therein.
Acknowledgments:
CH data for the Camerino-San Severino area were provided by the Superior
Institute for Conservation and Restoration (ISCR) of the Italian Ministry for Cultural and Environ-
mental Heritage (MIBAC) and are those included in the Vincoli in rete—Carta del rischio project
http://vincoliinrete.beniculturali.it/VincoliInRete/vir/utente/login, on 26 January 2021. Authors
warmly acknowledge interesting discussions and useful suggestions provided by several colleagues
involved in the ARCH project, in particular, the University of Camerino team lead by Andrea
Dell’Asta and the Municipality of Camerino team. Authors thank the anonymous reviewers whose
constructive and professional comments helped in improving the clarify of the manuscript.
Conflicts of Interest: The authors declare no conflict of interest.
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... accessed on 20 April 2022) of the INGV [18]. In this work, we want to introduce the first Urban Seismic Network developed in central Italy, in particular in Camerino, a town in the ARCH project [19]. First, the principles of urban seimsic monitoring network are explained, including topology and the seismic stations technology (Sections 2 and 5). ...
... In the post-processing of data, the Geographic Information System (GIS), also implemented on web platforms [48], is a common choice for the spatial representation of damage and vulnerability data at both the urban (see, e.g., [22,23,43,49]) and regional scales. Examples of the application of such a system are the Global Earthquake Model (GEM) [50], the HAZUS-MH platform [51], the CAPRA Probabilistic Risk Assessment Platform [52], and the ARCH DSS WebGIS Decision Supporting System [53]. Web mapping platforms can also support the assessment through simplified evaluations based on visual inspections, provided the accuracy of the observations and the dimension of the inventory comply with the evaluation purpose [54]. ...
Article
Full-text available
Empirical data on the seismic behavior of masonry buildings are collected by technicians through rapid visual assessment procedures, i.e., by filling in forms that organize information in short answers or ticks. The resulting empirical database serves as a basis for prevention strategies, but the archiving and the post-processing of data are always a potential cause of losses and misinterpretations. New technologies are nowadays entering seismic risk analyses as a support to the usual visual approach. This paper proposes a new application for Android mobile devices that digitalizes an assessment form (MUSE-DV) able to collect damage and vulnerability data of masonry buildings in seismic areas, including information on strengthening interventions applied to buildings in the past. The client–server architecture of the app considers local devices communicating over the web with a central unit; data processing is split between them to reduce network and resource needs. This approach is convenient with thin devices, such as smartphones, and in post-disaster situations, when the mobile network may not be available. Data collected onsite can be stored in remote archives and therefore shared among technicians without affecting the integrity and consistency of the database. The whole dataset can be extracted and processed by a dedicated software for statistical and spatial analysis. The MUSE-DV procedure was validated in the area struck by the 2016 Central Italy earthquake and the app presented here was preliminarily tested onsite on the buildings of Castelsantangelo sul Nera (Macerata district); the results contributed to damage and vulnerability analyses by the means of thematic maps.
... accessed on 20 April 2022) of the INGV [18]. In this work, we want to introduce the first Urban Seismic Network developed in central Italy, in particular in Camerino, a town in the ARCH project [19]. First, the principles of urban seimsic monitoring network are explained, including topology and the seismic stations technology (Sections 2 and 5). ...
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