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Seismic vulnerability assessment and risk reduction strategy of low-rise school buildings

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This paper presents a methodology for risk assessment of school building portfolios and risk mitigation programs developed under the World Banks's Global Program for Safer Schools (GPSS). The methodology is based on the Global Library of School Infrastructure (GLOSI) methodologies developed by the authors. It starts with the characterization of the school buildings portfolio, which is developed through a comprehensive taxonomy classification system, including school location and building characteristics. The taxonomy aims to identify the principal characteristics of each building that affect its structural behavior, including structural and non-structural attributes. Once the portfolio is characterized, typical construction typologies are identified in order to define the Index Buildings. These buildings are representative buildings of different typologies in the portfolio and will control the risk assessment metrics. Geometric and structural properties of the Index Buildings are obtained from structural drawings, knowledge of local construction practices, field surveys, experimental tests or literature. In parallel, the development of a stochastic seismic catalog model that includes the effect of the main type of sources and seismic characteristics of the study area should be performed. With this information, numerical nonlinear models are developed using nonlinear pushover analysis to perform seismic performance assessment under a suite of ground motions. Using a cloud approach and probabilistic performance-based assessment assumptions, fragility function for several damage thresholds and corresponding vulnerability functions are derived using either building-or component-based procedures. Such functions are suitable to perform probabilistic risk assessment using the CAPRA platform, providing risk financial metrics such as Probable Maximum Losses and Expected Annual Losses. With this information and the identification of the Index Buildings deficiencies, structural retrofitting measures can be proposed. The paper shows how the above risk assessment process can be conducted for existing and retrofitted Index Buildings to identify the risk reduction in the portfolio and evaluate the efficiency of alternative retrofit strategies. A case study using the proposed methodology in El Salvador is presented.
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17th World Conference on Earthquake Engineering, 17WCEE
Sendai, Japan - September 13th to 18th 2020
Paper N° C003206
Registration Code: S-A02414
SEISMIC VULNERABILITY ASSESSMENT AND RISK REDUCTION
STRATEGY OF LOW-RISE SCHOOL BUILDINGS
R.I. Fernández (1), R.K. Adhikari (2), L.E. Yamin (3), D.F. D’Ayala (4), G.A. Fuentes (5)
(1) Ph.D. Student, Universidad de Los Andes, ri.fernandez1110@uniandes.edu.co
(2) Ph.D. Student, University College London, rohit.adhikari.15@ucl.ac.uk
(3) Associate Professor, Universidad de Los Andes, lyamin@uniandes.edu.co
(4) Professor, University College London, d.dayala@ucl.ac.uk
(5) Researcher, Universidad de Los Andes, ga.fuentes10@uniandes.edu.co
Abstract
This paper presents a methodology for risk assessment of school building portfolios and risk mitigation programs
developed under the World Banks’s Global Program for Safer Schools (GPSS). The methodology is based on the Global
Library of School Infrastructure (GLOSI) methodologies developed by the authors. It starts with the characterization of
the school buildings portfolio, which is developed through a comprehensive taxonomy classification system, including
school location and building characteristics. The taxonomy aims to identify the principal characteristics of each building
that affect its structural behavior, including structural and non-structural attributes. Once the portfolio is characterized,
typical construction typologies are identified in order to define the Index Buildings. These buildings are representative
buildings of different typologies in the portfolio and will control the risk assessment metrics. Geometric and structural
properties of the Index Buildings are obtained from structural drawings, knowledge of local construction practices, field
surveys, experimental tests or literature. In parallel, the development of a stochastic seismic catalog model that includes
the effect of the main type of sources and seismic characteristics of the study area should be performed. With this
information, numerical nonlinear models are developed using nonlinear pushover analysis to perform seismic
performance assessment under a suite of ground motions. Using a cloud approach and probabilistic performance-based
assessment assumptions, fragility function for several damage thresholds and corresponding vulnerability functions are
derived using either building- or component-based procedures. Such functions are suitable to perform probabilistic risk
assessment using the CAPRA platform, providing risk financial metrics such as Probable Maximum Losses and Expected
Annual Losses. With this information and the identification of the Index Buildings deficiencies, structural retrofitting
measures can be proposed. The paper shows how the above risk assessment process can be conducted for existing and
retrofitted Index Buildings to identify the risk reduction in the portfolio and evaluate the efficiency of alternative retrofit
strategies. A case study using the proposed methodology in El Salvador is presented.
Keywords: seismic risk, school infrastructure, retrofitting, CAPRA.
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1. Introduction
Disasters affect all types of built environments, but school infrastructure is particularly critical due to the vulnerability of
its occupants. The physical vulnerability of the school buildings is found to be high as evidenced in recent seismic events
worldwide. During the 2015 Gorkha earthquake, about 7000 school buildings were either significantly damaged or
collapsed [1,2]. The same happened in Ecuador in 2016 in the Muisne earthquake where school buildings collapsed in
Manta, Pedernales, and Portoviejo [3] in which more than 875 schools suffered damages, 146 of those with severe
damages [4]. A year later, in the 2017 Puebla, Mexico earthquake, it was reported that more than 280 school buildings
were severely damages [5]. The EERI survey has reported damages to reinforced concrete school buildings due to
earthquakes in India, Indonesia, Peru, Turkey, the United States, and Haiti among others [6]. Taking this into account,
different organizations have been working on reducing the vulnerability of the school buildings to improve the seismic
safety level. In particular, the World Bank launched the Global Program for Safer Schools (GPSS), an initiative to generate
knowledge of school infrastructure, mainly in classification, vulnerability and fragility assessments, risk assessments and
risk mitigation programs which are documented in a library called Global Library of School Infrastructure (GLOSI). The
GPSS program has helped to assess the seismic vulnerability and to apply risk reduction measures in school infrastructure
in different countries worldwide, such as Peru, Colombia, El Salvador, Dominican Republic, Kyrgyzstan among others.
In this context, the main objective of this paper is to present a methodology for developing Risk Mitigation Plans (RMP)
for school infrastructure at the country or regional level. Section 2 presents the overview of the methodological approach,
showing the general methodology for RMP and the characteristics for the risk assessment. This methodology follows the
CAPRA approach, an open platform for multi-hazard risk assessment managed by Universidad de Los Andes. Section 3
presents a case study developed and implemented by the authors in El Salvador within the framework of the GPSS. The
proposed methodology was adopted in the case study, showing the advantages and challenges for developing the RMP.
Lastly, Section 4 presents some conclusions based on the methodology and discussions presented in this paper.
2. Methodological approach
2.1 General methodology
The general methodology for the development of RMP is summarized in Fig. 1. The first step is the data
collection of the school infrastructure. In developing countries information and database on the school
infrastructure are limited and additional information sources are needed. In particular, field inspections and
surveys are often required to collect information to characterize the school portfolio. Once the exposure model
is characterized, the next step is to perform the probabilistic seismic risk assessment in the existing condition.
For this task, is necessary to define a seismic hazard model (subsection 2.2), exposure, and vulnerability
functions, these models are described in the next section. The next step is to identify the critical Index Building
(IB) and design retrofitting alternatives for those. Risk assessment in retrofitted conditions of the hole portfolio
should be performed to identify the most critical school facilities and buildings in order to establish a
prioritization strategy. The objective of the prioritization is to benefit more students with a limited budget. The
risk mitigation plan (RMP) is the final output of the methodology, it is the strategy that will be adopted by the
country of the region that takes into account the limited budget, time and resources of each particular case.
Fig. 1. Framework for RMP methodology
2.2 Probabilistic seismic risk assessment
The risk estimation should follow a prospective approach, anticipating possible events that might occur in the future
according to historic information and the potential of new unrecorded events. For the case of seismic events, seismological
and engineering bases are used to develop earthquake forecasting models that allow the estimation of damages, losses,
and effects as a result of catastrophic events. Due to the high uncertainties inherent to these types of forecasting models,
Data
collection
Risk
assessment in
actual
condition
Building
retrofitting
alternatives
Risk
assessment in
retrofitted
condition
Prioritization
strategy
Risk
Mitigation
Plans (RMP)
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especially in relation to the severity and frequency of occurrence of the most intense events, the risk model should be
based on probabilistic formulations incorporating said uncertainty in the risk evaluation. The probabilistic risk model
(PRM), developed from a sequence of modules and which quantifies the potential losses that may arise from a given
event, includes a hazard module, an exposure module, and a vulnerability module, as shown in Fig. 2. The detailed
methodological approach for the risk assessment has been published by the authors and the reader is referred to additional
references [79] and www.ecapra.org.
Fig. 2. Risk assessment methodology
Hazard model
The seismic hazard model is developed from the geometrical characterization and modeling of active seismic faults in the
region, following the best earthquake engineering practices. The main parameters for the recurrence magnitude model of
each fault need to be based on available previous hazard models in the region, the historical earthquake catalog of events
and the geologic and neotectonic information available for each fault or region. The updated ground motion prediction
equation (GMPE) should be assigned to each seismic source according to its properties and characteristics [10]. A full
stochastic assessment needs to be adopted by means of which a mutually exclusive and collectively exhaustive set of
simulated events are generated for each seismic source modeled. The fully probabilistic assessment is based on the
integration of intensities from each stochastic event considering simultaneously its relative frequency of occurrence.
In addition to the country or regional level seismic hazard model at the rock level, it’s important to consider local soil
amplification effects in order to obtain seismic intensity distribution maps at surface level (including local soil effects)
for each stochastic event. Many methodologies including this effect have been used by the engineering community and
they depend mainly on the available information. When no geological or geotechnical information is available a simplified
methodology can be used. It consists of proposing general zoning of the terrain based on the geologic information,
geomorphology and the mean slope followed by an estimation of the soil amplification effects by means of simplified
soil response models. The details of the methodology are available in the literature [11].
Exposure model
The exposure model is one of the most complex and time-consuming tasks in the risk assessment and in the development
of RMP. The main objective of this model is to understand the geographical distribution of the school buildings and to
identify the vulnerability characteristics that are required to clearly identify a unique expected seismic behavior of each
school building. The GLOSI methodology [12] has developed a classification system and outlines a procedure for the
selection of characteristic IBs representative of different typologies present in the portfolio. The taxonomy parameters
and different tiers for data collection are summarized in Table 1. The GLOSI reports can be found at
www.gpss.worldbank.org.
Table 1 - GLOSI taxonomy parameters
No.
Taxonomy Parameter
Tier for Data Collection
1
Main Structural System
Primary parameters: Tier 1
2
Height Range
3
Seismic Design Level
4
Diaphragm Type
Secondary parameters: Tier 2
5
Structural Irregularity
6
Wall Panel Length/Span Length
7
Wall Openings/Pier Type
Hazard
Exposure
Vulnerability
Economic and human losses
estimation
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No.
Taxonomy Parameter
Tier for Data Collection
8
Foundation Type
9
Seismic Pounding Risk
10
Effective Seismic Retrofitting
11
Structural Health Condition
12
Non-Structural Components
-
Intrinsic parameters (geometric characteristics and material properties)
Intrinsic parameters: Tier 3
The IB is defined primarily by the most probable combinations of the primary parameters (i.e. main structural system,
height range, and seismic design level) which results in most common building types in the school portfolio. The
secondary parameters are selected as representative attributes of the portfolio and usually, the most common and/or most
vulnerable value or a range of values are defined for each particular IBs. For example, if 90% of the roofs are flexible
type diaphragms (which makes a building more vulnerable compared to a stiff diaphragm type building), then only this
IB will be selected for further analysis.
Vulnerability model
For the IBs identified (subsection 2.2.2), the GLOSI analytical vulnerability approach (www.gpss.worldbank.org) can be
adopted, as it allows for an unbiased and consistent assessment applicable worldwide, independently of historic seismic
damage data and local expertise on specific typological building performance [13]. The analytical methods allow
vulnerability functions to be updated, complemented and modified as more refined data on exposure or refined analytical
approaches become available. Notwithstanding its generic essential quality, the analytical approach also allows the
consideration of the structural modeling and hazard specification, the local geographical and seismic conditions as well
as characteristics of each IB, in order to generate specific vulnerability curves that are region-dependent. For each IB,
pushover curves are derived for both principal directions (longitudinal and transverse) of the building in order to identify
the weakest direction. Because specific strengthening strategies are dependent on a full 3D analysis of seismic capacity
considering the improvement in the bi-directional performance level, damage initiation, propagation, and modes of
ultimate collapse mechanisms at global building/element level are documented, for each IB, from the result of non-linear
pushover analysis.
Although more advanced nonlinear time history methods are available in the literature, the non-linear static approach
based on the latest version of the N2 method [13,14] is proposed in the GLOSI methodology so that wider users can
understand the simplicity of the approach, without compromising much on the reliability. For each pushover curve, the
thresholds of discretized damage states represented by the roof drift are determined in terms of a specific structural
element and global damage indicators. The definition of damage states and associated threshold limits can be selected for
specific IB. For each IB, hundreds of performance points are obtained with the application of the N2 method under several
ground motions. For the derivation of fragility and vulnerability functions for each IB, either building-based or
component-based methodology can be followed which are discussed in [9,15].
2.3 Risk mitigation program
The final optimal decision should be the mitigation strategy with the lowest implementation costs, considering the
budgetary restrictions and limitations, the operational restrictions, and the uncertainty of the risk assessment and the
impact in the school infrastructure. The optimal solution could under some circumstances be one different from that of
lowest implementation costs, depending on the prioritization scheme and the preference for the decision-maker. Different
schemes can be analyzed to evaluate the possible variations of results depending on these parameters of the optimization
model. The following main objectives are usually part of the RMP:
1. Reduce the risk of death or injury of the educational community due to seismic events (maximize the number of
students benefited).
2. Reduce damage to the infrastructure, contents, installations and protect the property in case an extreme natural
event occurs.
3. Reduce the interruption in the educational service due to damage that originated in catastrophic events.
4. Improve the quality of the school infrastructure to offer a better educational service and allow the implementation
of new academic programs and other initiatives.
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The stock of buildings can be divided into smaller groups to facilitate the intervention. The following groups of buildings
should be identified:
Buildings with high collapse risk
Buildings with high potential of suffering damage
Buildings with e good expected behavior.
For each one of the groups, the RMP may include different interventions in combination such as:
- Structural interventions
- Non-structural interventions.
- Relocation of critical infrastructure located in high hazard zones.
- The demolition and replacement of some types of buildings that can’t be retrofitted or that the retrofitting would
be extremely expensive.
Once the specific programs for intervention are selected, the next phase is to define the prioritization of the interventions
with the following objectives:
Initiate the intervention programs with the most critical school buildings according to the decision-making
model.
Maximize in time the number of students benefited from the interventions.
Dimensioning each program depending on the available budget.
Measure the advances and effectiveness of the program during the implementation phase.
The prioritization shall be established based on an efficiency-cost indicator such as the following:
     
  
Where  refers to the expected annual losses for the current state of buildings,  the expected
annual losses for the retrofitted conditions and the interest rate.
3. Application example: case study of El Salvador
Under the World Banks’s Global Program for Safer Schools (GPPS) is carried out an alliance between World Bank,
Universidad de Los Andes (Uniandes) and University College London (UCL) with the main objective of developing a
basis for a seismic RMP for El Salvador’s school infrastructure. The development of the RMP was conducted following
the methodology proposed in this study.
3.2 Hazard model
El Salvador is characterized by an important seismic activity due to its proximity to the subduction of the Cocos plate
under the Caribbean plate. Additionally, it’s crossed by a Central American volcanic arc that extends from Guatemala to
Costa Rica, and in which a fault zone located known as the Zona de Fallas de El Salvador’ [16]. Other areas that have
an important seismic contribution in the study area are the Motagua and Chixoy-Polochic fault system in Guatemala and
Honduras' depression located in the central part of Honduran territory [17].
A hazard file (“.ame” type format) was generated that contains the acceleration information at the bedrock level for a total
of 24,996 stochastic scenarios that represent the seismicity of a total of 54 failures of which 37 are cortical and 17
subductions. For each of them, maps of seismic intensities (both expected mean values and corresponding variances) were
generated for spectral accelerations corresponding to 23 structural periods ranging from 0.1s to 2s. The approximate
location of the 54 failures considered in the study zone for the generation of stochastic events is presented in Fig. 3.
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(a)
(b)
Fig. 3 - Seismic Sources. (a) Cortical; (b) subduction
In order to characterize each one of the seismogenic sources from the point of view of the recurrence of magnitudes, the
information of the RESIS II project [17] was obtained.
3.3 Exposure model
The exposure model is based on a “Shape” type database in which each component corresponds to a school building and
for each of them, a series of attributes are defined that allow the estimation of the exposed value, student occupation and
the characterization of its seismic vulnerability. The minimum information required for each of the components of the
exposure model consists of the identification of each of the buildings, geographical location, replacement value, estimated
maximum student occupation and constructive typology that allows the assignment of a vulnerability function. The
consulting firm ‘SISMICA’ did the processing of all the information from the database. In addition to consolidating the
information of the ‘MINED’ and the World Bank, they performed the interpretation of the photographs included by the
directors in order to assign, for each of the buildings reported, the taxonomy parameters. The El Salvador school portfolio
consists of 5,178 public and 880 private schools, the RMP focusing only on the public schools. The physical exposure
model has the following general characteristics:
- Total number of schools: 5,178
- Total number of buildings: 14,459
- Total estimated occupation of the sector: 1,223,022 students
- Approximate total built m2: 2,837,200 m2
- Total portfolio valuation (value of landless buildings): US $ 1,276,740,000
- Average value per resulting m2: US $ 450 / m2
- The average number of m2 per person: 2.32 m2 / person
Another important result obtained from the school information and, mainly to the information collected from the field
survey, is the distribution of the main structural system of the school buildings (Fig. 4), where it can be observed that
most of the Buildings are classified into two large groups corresponding to masonry buildings and reinforced concrete
buildings.
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Fig. 4 - Main structural system of the school buildings.
It is observed that low-rise reinforced masonry (RM) and confined (CM) makeup to more than 80% of the portfolio,
followed by reinforced concrete frames with short column problems (RC3) and masonry buildings with light steel frame
(SF1). The IBs that will represent the school portfolio in the exposure model are defined according to the above mentioned
dominant typologies. In Fig. 3, representative photographs of the IBs of the dominant typologies are shown.
(a)
(b)
(c)
(d)
Fig. 5 - Index Buildings. (a) CM - Confined masonry; (b) RM - Reinforced masonry; (c)RC3 - Reinforced concrete with short
column; (d) SF1 Confined/reinforced masonry with Steel frame.
3.4 Vulnerability
The GLOSI analytical vulnerability approach is used in the vulnerability assessment [15]. In the following sections, the
application of this methodology for the development of vulnerability functions of school buildings in El Salvador is
described in detail.
Record selection
The selection of records is made from the seismic catalog shared by the ‘Ministerio de Ambiente y Recursos Naturales’
(MARN) and from databases of international seismic records from Chile, Costa Rica, El Salvador, Japan, Mexico, PEER,
and Peru. From the database of records, characteristics of the magnitude of the event and the focal length are identified
for each. Fig. 6 shows the comparison in the elastic response spectra of the selected records and that corresponding to the
El Salvador resistant earthquake design standard.
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Fig. 6 - Elastic response spectra of the selected records vs. Design spectra.
Modeling parameters Reinforced concrete IB
Full 3-dimensional numerical models are developed for RC school IBs from El Salvador, with a frame model with
plasticity concentrated in the areas where the element will be plasticized, in accordance with the indications of ASCE/SEI
41-17 [18]. For the flexural behavior of the structural elements, the flexural hinges recommended by ASCE/SEI 41-17
standard, presented in Fig. 7a was mainly used. Regarding the shear behavior of the concrete elements, it was defined
from the force-displacement curve presented in Fig. 7b, proposed by [19]. The proposed model considers the maximum
shear capacity of the section and the point of failure, which is considered a fragile type of failure. Finally, the ASCE/SEI
41-17 standard was used primarily to characterize the axial behavior of the infills walls. The version of this approach
codified in the PERFORM-3D® software is used in the present work.
(a)
(b)
Fig. 7 - Structural idealizations of the model. (a) Flexural model; (b) Shear model.
Modeling parameters Masonry IBs
Full 3-dimensional numerical models were developed for LBM school IBs from El Salvador, with an element by element
non-linear modeling approach resulting in a simplified micro-modeling technique, based on the applied element method
(AEM) [20]. In the AEM, masonry is modeled using a simplified micro-modeling technique (Fig. 8b), in which the applied
elements are modeled as rigid elements whereas the joint and the mortar-unit interfaces are sandwiched into zero thickness
joints which are represented by the joint springs. If the units are expected to damage, the units can be divided into several
elements (usually two) by having unit springs in between the applied elements of the units. All the stresses, deformation
and non-linearities in the material behavior are thus represented in the joint springs [20]. Using the AEM, several reliable
analytical studies have been conducted on masonry structures under static and dynamic analysis [12,21,22] as the
complete response of structures from the initiation of cracking to the final collapse can be studied with reasonable
accuracy [20]. The version of this approach codified in the Extreme Loading for Structures® (ELS) software is used in
the present work. However, it is possible to develop the numerical model and perform pushover analysis with any other
methods/software such as macro-element-based approaches (e.g. [23]) or FEM based software (e.g. DIANA FEA).
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(a)
(b)
Fig. 8 - Illustrative structural idealization of the models. (a) Numerical model; (b) Simplified micro-modeling technique.
Demand parameters and vulnerability functions
Seismic demand parameters (PDS) describe the maximum response of the structure submitted to seismic actions. These
parameters are used to estimate the damage of structural and non-structural components. In the present study, in order to
obtain the seismic demand parameters, a nonlinear static analysis (NLS) was conducted, and using the N2 software
developed in the GPSS project were obtained the PDS [15]. To obtain the vulnerability functions, the methodologies
proposed by [9] and [13] were used. The vulnerability functions obtained for each of the IBs of the El Salvador school
portfolio are shown in Fig. 7.
(a)
(b)
(c)
(d)
Fig. 9 - Vulnerability functions. (a) Confined masonry; (b) Reinforced concrete; (c) Reinforced masonry; (d) Confined/Reinforced
masonry with steel frames.
0
20
40
60
80
100
00.5 11.5 2
Relative Repair Cos t - %
Intens ity measure - g
CM_LR_PD
CM_LR_LD
CM_LR_MD
0
20
40
60
80
100
00.5 1 1.5 2
Relative Repair Cos t - %
Intens ity measu re - g
RC3_MR_PD
RC3_MR_LD
RC3_MR_MD
0
20
40
60
80
100
0 0.5 1 1.5 2
Relative Repair Cos t - %
Intens ity measu re - g
RM_LR_PD
RM_LR_LD
RM_LR_MD
0
20
40
60
80
100
00.5 1 1.5 2
Relative Repair Cos t - %
Intens ity measure - g
SF1_LR_PD
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3.5 Risk mitigation program
The probabilistic risk assessment was performed for the portfolio of buildings of the school sector of El Salvador under
the current conditions using the CAPRA platform [24] (www.ecapra.org). Table 2 summarizes the overall results of total
exposed value and expected annual loss. Fig. 10 summarizes the probable maximum losses (PML) for different return
periods. The results are presented in millions of dollars and as a percentage of the total value exposed.
Table 2- Seismic risk results
Exposed Value
US$ Millon
$1,276
EAL
US$ Millon
$9.45
EAL to thousand
7.40
Probable maximum losses
Return Period
Losses (Millon
US$)
Losses (%)
31
43
3%
225
110
9%
475
138
11%
1,000
164
13%
2,000
196
15%
Fig. 10 - PML Curve
El Salvador’s school portfolio is considered as essential group buildings, the need to retrofit the portfolio emerge not only
to prevent significant damage and reduce economic costs, also to preserve the lives of students. Mentioned this, we
proposed measures to mitigate seismic risk in buildings. The measures taken are summarized in Table 3:
Table 3 - Recommended retrofit
Typology
Recommended retrofit
SF1_PD
Replacement for a new building
RC3_PD
RC walls + infills isolations
RC3_LD & MD
Steel braced frames + infills isolations
CM_PD
Steel plates + roof steel frames
RM_PD
Steel plates + RC ring beams over the walls
CM_LD & MD
Roof steel frames
RM_LD
RC ring beams over the walls
The results in terms of costs are observed in Fig. 9, observed a great reduction of the probable maximum losses (PML)
for different return periods.
Fig. 11 - PML curve, Present Vs Retrofitted.
TR 31
3%
TR 225
9%
TR 475
11%
TR 1,000
13%
0%
5%
10%
15%
20%
0
50
100
150
200
250
300
0 500 1,000 1,500 2,000
relative Losses
Losses - $USD
Return Periods - years
PML - Present
PML - Retrofitted
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On the other hand, an effect is also generated on the performance of the school portfolio buildings, where it is observed that in the
present state for return periods of 475 (design period), a high percentage of portfolio is at risk of collapse and, with the retrofitting
implemented, it ensures that the buildings of the portfolio are in life safety (LS).
(a)
(b)
Fig. 12 - Performance of the school portfolio. (a) present; (b) Retrofitted
4. Conclusions
Considering the high vulnerability of school buildings and the risk to their occupants, developing RMP becomes an
important and urgent matter for each country. A methodology to develop Risk Mitigation Plans was developed, the first
step is to collect information to characterize the school building portfolio, followed by risk assessments in actual and
retrofitted conditions. The risk assessment approach was based on CAPRA developments, an open software managed by
Universidad de Los Andes. With this information, prioritization and RMP can be developed with the objective of reducing
seismic risk with limited resources and time.
A case study application of the proposed methodology to the school infrastructure in El Salvador, a high seismic country
located in Central America, is presented. The case study showed the challenges in information gathering and exposure
portfolio development. The representative IBs were analyzed using non-linear static procedures and the retrofitting
alternative was selected based on the 3-dimensional analysis. The RMP for El Salvador was developed together with local
authorities and considering the economic limitations of the country. The methodology and the example show that
developing an RMP for school infrastructure is feasible and necessary in high seismic countries.
5. Acknowledgments
The work presented in this paper has been funded by the World Bank and developed under the Global Program for Safer
Schools. The authors acknowledge the funding provided by the World Bank.
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... This need is especially higher in developing countries with limited resources. Given the scale of this challenge, it is particularly important to be strategic when implementing large-scale investments to improve school infrastructure Fernández et al., 2020;Figueroa et al., 2020;. Updated and high-quality school infrastructure baseline data is essential to determine which schools are most at risk and prioritize the investment. ...
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