Conference PaperPDF Available

Usefulness of ambient-vibration measurements for seismic assessment of existing structures


Abstract and Figures

A large number of buildings in regions with low to medium seismic hazard have been designed without considering earthquake actions. Retrofitting of all buildings that fail to meet modern code requirements is economically, technically and environmentally unsustainable. Decision-making regarding retrofitting necessity and prioritization is complex. Ambient vibrations are non-destructive and easy to measure, and thus an attractive data source. However, ambient vibrations have very low amplitudes, which potentially lead to sensitivity to testing conditions and stiffness contributions from non-structural elements. Seismic assessment necessitates non-linear behavior extrapolation from linear measurements, which results in biased model predictions. Error-domain model falsification is a data-interpretation methodology that is robust to multi-source uncertainties with unknown and changing correlation values. In this contribution, static non-linear behavior predictions of an existing building in Lausanne, Switzerland, are presented. Ambient-vibration data has been gathered under changing conditions: from undamaged in-service to gradual removal of non-structural elements. Low sensitivity to non-structural elements are found. A numerical model based on the applied-element method is generated and shows potential utility of linear measurements for decision-making using non-linear models involving EDMF under uncertain conditions.
Content may be subject to copyright.
Originally published for SMAR 2017, fourth International Conference on Smart Monitoring,
Assessment and Rehabilitation of Civil Structures, Zurich, Switzerland, 2017.
Final publication is available in the related proceedings, Paper No. 162.
Usefulness of ambient-vibration measurements for seismic
assessment of existing structures
Yves Reuland1, Abdo Abi Radi Abou Jaoude1, Pierino Lestuzzi1, Ian F.C. Smith1
1 Swiss Federal Institute of Technology Lausanne (EPFL), Switzerland
ABSTRACT: A large number of buildings in regions with low to medium seismic hazard have
been designed without considering earthquake actions. Retrofitting of all buildings that fail to
meet modern code requirements is economically, technically and environmentally
unsustainable. Decision-making regarding retrofitting necessity and prioritization is complex.
Ambient vibrations are non-destructive and easy to measure, and thus an attractive data source.
However, ambient vibrations have very low amplitudes, which potentially lead to sensitivity to
testing conditions and stiffness contributions from non-structural elements. Seismic assessment
necessitates non-linear behavior extrapolation from linear measurements, which results in
biased model predictions. Error-domain model falsification is a data-interpretation methodology
that is robust to multi-source uncertainties with unknown and changing correlation values. In
this contribution, static non-linear behavior predictions of an existing building in Lausanne,
Switzerland, are presented. Ambient-vibration data has been gathered under changing
conditions: from undamaged in-service to gradual removal of non-structural elements. Low
sensitivity to non-structural elements are found. A numerical model based on the applied-
element method is generated and shows potential utility of linear measurements for decision-
making using non-linear models involving EDMF under uncertain conditions.
A large part of the building stock in regions with low to moderate seismic hazard have been
built before thorough seismic considerations have been formulated in building codes. Replacing
all buildings that fail comparisons with modern design specifications is impossible from
economical, technical and environmental standpoints. Therefore, seismic vulnerability
assessment of existing buildings has an important role in prioritizing retrofitting actions.
However, assessing seismic vulnerability of existing structures is often complicated by the
absence of precise building information such as construction drawings and past structural
interventions, and by the large variability of material properties in existing buildings.
Although methodologies exist for rapid assessment at a city-scale level (Lestuzzi et al., 2016),
the evaluation of buildings with a high contribution to the resilience of a city, such as hospitals
and community centers, may require models with higher fidelity. Also, such physics-based
structural models are useful to design targeted and efficient rehabilitation and strengthening.
Model-based structural identification using measurement data is a widespread tool to reduce
uncertainty related to model parameter uncertainty. When buildings are analyzed, ambient
vibration measurements are a well-suited if not the only available non-destructive data source.
Development of economic, sensitive and transportable sensors has led to popularity of ambient-
vibration-based structural identification. However, ambient vibrations are strictly limited to
linear-elastic behavior. The low amplitudes of vibration, typically 10-6 to 10-3 m/s2, do not give
insights into non-linear behavior of buildings (Michel et al., 2011). Also, non-structural
elements, such as separation walls, doors, windows and heavy furniture, potentially contribute
to the building stiffness under very low amplitudes of excitation.
Capacity-based vulnerability estimation of buildings requires non-linear behavior predictions.
Therefore, model extrapolation is needed as non-linear behavior differs from exclusively linear
behavior under ambient vibrations. A structural identification methodology that explicitly
incorporates model uncertainties in a transparent way is used to perform such extrapolation:
Error-domain model falsification (EDMF) (Goulet and Smith, 2013; Pasquier and Smith,
Through a full-scale case study the usefulness of structural identification using ambient
vibration data is assessed. Measurements have been taken on a typical Swiss masonry building
in Lausanne for three building states: initial state and after gradual removing of non-structural
elements such as windows and stair railings. A complex 3D structural model, using the Applied
Element Method (AEM), is used to predict modal properties and non-linear pushover curves of
the studied building.
This paper starts with a short description of the methodologies used to perform ambient-
vibration-based structural identification using an AEM model. Then, on a full-scale case-study,
the reduction in non-linear prediction uncertainty that can be obtained with exclusively linear
vibration measurement data is assessed. Finally, next steps and conclusions are discussed.
2.1 Error-Domain Model Falsification
Model-based structural identification is an inverse engineering task that involves ambiguities.
Even complex 3D models fail to provide an exact representation of full-scale structures under
in-service conditions. Therefore, discrepancies between model predictions and observed
behavior are inevitable. As engineering structures are systems, such model errors are spatially
correlated to an unknown extent (Goulet and Smith, 2013). Also, for approximate engineering
models, uncertainties are rarely zero-mean normal distributions and a limited number of
uncertainty sources undermines the applicability of the Central Limit theorem (Pasquier and
Smith, 2015a).
EDMF is based on the principle of using measurements to falsify inappropriate model instances
instead of optimizing single models. Therefore, measurement and model uncertainties are
combined to calculate thresholds T that bound the domain of acceptance for residuals between
model predictions g(θ) and measured values y for all Nm measured quantities, according to Eq.
𝑖=1,,𝑁!: 𝑇
!!"!,! (1)
Through the transparent incorporation of uncertainties and the threshold-based reasoning, the
applicability of EDMF to tens of full-scale structures and the intuitive understanding by
practicing engineers has been shown (Smith, 2016). Also, through avoiding the definition of
exact uncertainty distributions and by being insensitive to unknown and changing uncertainty
correlations, EDMF results in robust identification and prediction ranges (Pasquier and Smith,
In this application to existing buildings, fundamental frequencies derived from ambient
vibrations are proposed as a measurement source. Ambient vibrations are a time-efficient and
non-destructive measurement source. Measured accelerations are transformed to the frequency
domain and analyzed using the Frequency Domain Decomposition technique (Brincker et al.,
2001), a popular output-only modal identification technique in civil engineering applications.
EDMF has been used in the past with ambient vibration-based modal properties for linear model
identification on bridges (Goulet et al., 2013).
2.2 Applied Element Method
In Switzerland unreinforced masonry buildings make up for the major part of the building stock.
Predictions related to the non-linear behavior of masonry, an orthotropic material composed of
bricks and mortar joints, remain challenging. In this paper, the AEM is used to predict non-
linear pushover curves of a masonry building. AEM is suitable to predict post-yield structural
behavior of masonry structures that are defined by a particularly large range of potential failure
modes (Garofano and Lestuzzi, 2016; Guragain et al., 2012; Karbassi and Nollet, 2013).
In order to capture failure modes that govern masonry structures, such as rocking, joint de-
bonding, sliding or shear diagonal cracking, AEM divides structural components into elements
that are connected with springs at element contact points (around the edge). Pairs of normal and
shear springs localize stresses, strains and deformations (Meguro and Tagel-Din, 2002). Two
types of springs represent masonry behavior: the first type of springs characterizes brick
behavior while the second type of springs merges brick-mortar interface and mortar behavior.
The behavior of bricks and mortar is assumed to be similar to concrete-type behavior models
(Extreme loading® for Structures, 2013). The defined springs are able to capture joint de-
bonding, shear sliding, direct tension and partial connectivity between elements. However,
shear-compression failure due to high axial loads is not taken into consideration.
Usefulness of ambient vibration measurements for structural identification is investigated
through application to a full-scale masonry building. The Villa Marguerite in Lausanne is a
typical Swiss masonry building with 4 floors that has been built in the early 20th century.
Ambient vibrations have been measured on three days, prior to the demolition of the building,
using six tri-axial acceleration sensors. The first set of measurements is representative of the
initial building state under in-service conditions. The second measurement set has been taken
after removal of windows and doors and provisional replacement by wooden panels. The third
set of measurements has been taken after all secondary elements, except non-structural walls
have been removed. Also, changing atmospheric conditions (temperature and humidity) and
solar radiation conditions (changing daytimes) influence the measurement sets (see Table 1).
Results from Frequency Domain Decomposition (FDD) of measurements from the third
measurement set are reported in Figure 2. Two fundamental bending modes and one torsional
mode can be detected.
The evolution of the fundamental bending frequencies in the two directions is reported in Table
1. Only small changes that do not exceed the levels of measurement uncertainties (sensor
sensitivity, cable losses, digitizer-losses and time-domain to frequency-domain transformation
are estimated to result in a zero-mean normal distribution with a standard deviation of 0.2 Hz)
are observed. Modal characteristics from ambient-vibration measurements are thus insensitive
to small changes in atmospheric conditions and non-structural elements such as windows and
furniture. As a consequence, ambient vibrations can be measured under in-service conditions.
A physics-based model of the structure is developed using AEM (see Figure 1) with
approximatively 26’000 elements connected using 1’900’000 non-linear springs. Although
AEM allows high-fidelity representation of the structural system, some assumptions are
inevitable at the modelling stage: soil-structure interaction is ignored as the base is modelled to
have fixed supports; non-bearing separation walls are omitted; and the slab and roof structure
are idealized to be isotropic elements with an equivalent stiffness and an equivalent density that
include non-structural covering.
Figure 1. Figure of the Villa Marguerite, Lausanne (Photo Credit IMAC EPFL) and view of the AEM
model of the Building.
Figure 2. Spectral analysis of vibration measurement on the building. Singular values are calculated from
the Correlation Power Spectral Density (CPSD) matrix to identify global structural modes.
Nine parameters are estimated to have an influence on the linear and non-linear model
predictions (see Table 2). Slab parameters (density and stiffness), equivalent roof stiffness and
brick parameters are assumed to have an influence on linear and non-linear behaviour
predictions of the structure, while mortar properties (compressive and tensile strength, friction
coefficient and separation strain) mainly govern the predicted non-linear behavior range. Initial
parameter ranges are derived using engineering judgement and existing literature.
In order to bypass important simulation time and guided by the needs of a preliminary model
class evaluation, a Box-Behnken sampling scheme is used. Thus, a total of 121 parameter
combinations sampled from initial parameter ranges shown in Table 2 are simulated. The Box-
Behnken design divides the initial parameter ranges by picking extreme points of the range as
well as the center point. In cases where a thorough structural identification of all the parameters
is useful, the 121 parameter combinations can be used to derive a surrogate model.
Table 1. Natural frequency derived from ambient vibration measurements for changing building states
defined by gradual removal of non-structural elements. Changing environmental conditions and non-
structural elements have little influence on observed natural frequencies.
Building state
Date of
frequency [Hz]
frequency [Hz]
26th June, 2015
After removal of some
secondary elements
14th July, 2015
5.7 (-)
5.7 (-2%)
After removal of all
secondary elements (except
separation walls)
15th July, 2015
5.5 (-3.5%)
5.8 (-)
Table 2. Initial and identified parameter ranges for chosen material properties of the structural model.
Parameter identification reduces the range of linear parameters only.
Initial range
Identified values
0.1 0.3
750 – 2500
750 – 1625
2.0 – 4.5
3.25 – 4.5
0.5 2.5
0.5 2.5
5.0 20.0
5.0 20.0
0.55 – 0.95
0.55 – 0.95
0.05 0.15
0.05 0.15
500 1500
1000 1500
In addition to the measurement error N(0,0.2Hz), three sources of model uncertainties are
identified: model error due to element size and secondary parameters (uniform between -7.5%
and +7.5%); omission of non-structural walls (with a thickness below 10 cm) and irregular
boundary conditions as well as simplification of the roof structure (between -10% and 10%);
and omission of soil-structure interaction by idealizing fixed boundary conditions (-15% to 0%).
According to Eq. (2), a negative model error, εmodel, corresponds to a model that results in
overestimating structural responses. Given boundary conditions cannot be stiffer than fixed, the
uncertainty is biased towards overestimated frequencies. EDMF allows engineers to define such
biased uncertainties.
𝑇𝑟𝑢𝑡=𝑔𝜽+𝜀!"#$% (2)
Frequencies that are predicted using the AEM model (see Fig. 1) are reported in Figure 3 for the
bending modes alongside measured frequencies and EDMF thresholds. Compared to the
measured frequency, the frequency predictions are shown to be overestimated. This observation
is in agreement with the biased model uncertainty estimation.
Unsurprisingly, the reduction in parametric uncertainty that is achieved using linear
measurements is restricted to linear material properties. As can be seen in Table 1, the highest
reduction in parametric uncertainty is achieved for Young’s modulus of masonry bricks. As a
Box Behnken design is used to sample the parameter space, an upper limit of potential
parameter identification using EDMF is obtained.
Based on the AEM model (see Fig. 1), which has been employed to predict natural frequencies,
non-linear transversal push-over curves are predicted. A linearly increasing displacement
distribution along the building elevation is used to derive pushover curves. Predicted base shear
as a function of displacement of the upper slab is reported in Figure 4.
Through a reduction in the parametric uncertainty related to linear material properties (see Table
1), non-linear prediction uncertainty is reduced. Although predictions of the maximum force
that the building can sustain remain scattered, the displacement capacity of the building is
predicted with higher precision. This is an encouraging finding for structural identification of
non-linear structures with linear measurement data.
Figure 3. Predicted frequencies related to the fundamental bending modes in the longitudinal and
transversal direction. Predictions are biased with regard to the measured frequencies.
Figure 4. Predicted push-over curves in the transversal direction. Prediction uncertainty can be reduced,
however the prediction range resulting from candidate models remains large.
Comparison between model predictions and measured behavior shows a systematic
overestimation of natural frequencies. Model assumptions such as fixed boundary conditions
lead to overestimated results. Future work includes estimating the influence of non-fixed
boundary conditions (soil-structure interaction) on the predicted natural frequencies in order to
reduce the combined uncertainty. EDMF allows the engineer to gradually include knowledge
and increase model fidelity by adapting uncertainties in a transparent way.
In such a perspective of gradual knowledge acquisition, tensile strength of mortar has the
highest influence on candidate pushover curve predictions. Therefore, further reduction in the
prediction of maximum base shear necessitates knowledge acquisition regarding mortar tensile
strength. However, current technology requires laboratory tests to deduce material strength.
Surrogate models are needed to perform a thorough identification of parameter values, which is
an important step if, for instance, retrofitting is to be designed. A low number of samples has
been used for the Latin Hypercube Sampling in order to get a first evaluation of the model class
and the capacity to reduce parametric prediction uncertainty in the non-linear range.
If predictions that involve extrapolation are performed, the prediction uncertainty differs from
the identification uncertainty. An exact quantification of prediction uncertainty is needed to
provide the decision-maker with robust prediction ranges.
The non-linear predictions that are used to verify the usefulness of linear measurements for non-
linear parameter identification are static non-linear pushover curves. In case dynamic non-linear
time histories are predicted, the uncertainty reduction might be more important, given the
influence of the fundamental modes on dynamic building behavior.
Structural identification of non-linear behavior models using linear measurements is presented.
The following conclusions are drawn from the application of vulnerability predictions using a
non-linear AEM model and linear vibration measurements:
- Although ambient vibration measurements are characterized by low amplitudes, low
sensitivity to non-structural elements such as windows and furniture is observed. This is
an essential feature for robust model identification using physics-based models.
- Reducing the parametric uncertainty of linear properties can reduce the behavior
uncertainty in the non-linear range. Additional case studies are needed to confirm this
finding. In addition, the costs of measurement acquisition and especially of complex
non-linear structural models may not justify the application of the methodology in cases
for which the reduction in prediction uncertainty is low.
- EDMF allows the engineer to combine various uncertainty sources in a transparent and
intuitive way. In addition, in presence of scarce numbers of measurements and model
predictions, EDMF helps to indicate subsequent steps to take.
This work was funded by the Swiss National Science Foundation under Contract No. 200020-
169026. Costs of the measurement system were partially covered by the Swiss National Science
Foundation under grant No. 150785. The authors thank the Real Estate and Infrastructures
Department of EPFL for the access to the building and A. Herzog for documenting the tests.
Applied Science International, Extreme loading® for Structures (Version 3.1), Theoretical Manual, 2013.
Brincker, R., Zhang, L., Andersen, P., 2001. Modal identification of output-only systems using frequency
domain decomposition. Smart Mater. Struct. 10, 441. doi:10.1088/0964-1726/10/3/303
Garofano, A., Lestuzzi, P., 2016. Seismic Assessment of a Historical Masonry Building in Switzerland:
The “Ancien Hôpital De Sion.” International Journal of Architectural Heritage 10, 975992.
Goulet, J.-A., Michel, C., Smith, I.F.C., 2013. Hybrid probabilities and error-domain structural
identification using ambient vibration monitoring. Mech. Syst. and Sign. Proc. 37, 199212.
Goulet, J.-A., Smith, I.F.C., 2013. Structural identification with systematic errors and unknown
uncertainty dependencies. Computers & Structures 128, 251258.
Guragain, R., Dixit, A., Meguro, K., 2012. Development of fragility functions for low strength masonry
buildings in Nepal using applied element methods, in: 15th World Conference of Earthquake
Engineering, Lisbon, Portugal.
Karbassi, A., Nollet, M.-J., 2013. Performance-Based Seismic Vulnerability Evaluation of Masonry
Buildings Using Applied Element Method in a Nonlinear Dynamic-Based Analytical Procedure.
Earthquake Spectra 29, 399426.
Lestuzzi, P., Podestà, S., Luchini, C., Garofano, A., Kazantzidou-Firtinidou, D., Bozzano, C., Bischof, P.,
Haffter, A., Rouiller, J.-D., 2016. Seismic vulnerability assessment at urban scale for two typical
Swiss cities using Risk-UE methodology. Natural Hazards 84, 249269.
Meguro, K., Tagel-Din, H.S., 2002. Applied element method used for large displacement structural
analysis. Journal of Natural Disaster Science 24, 2534.
Michel, C., Zapico, B., Lestuzzi, P., Molina, F.J., Weber, F., 2011. Quantification of fundamental
frequency drop for unreinforced masonry buildings from dynamic tests. Earthquake Engineering
& Structural Dynamics 40, 12831296.
Pasquier, R., Smith, I.F., 2015a. Sources and forms of modelling uncertainties for structural
identification, in: 7th International Conference on Structural Health Monitoring of Intelligent
Infrastructure (SHMII).
Pasquier, R., Smith, I.F.C., 2015b. Robust system identification and model predictions in the presence of
systematic uncertainty. Advanced Engineering Informatics 29, 10961109.
Smith, I.F., 2016. Studies of Sensor-Data Interpretation for Asset Management of the Built Environment.
Frontiers in Built Environment 2, 8.
... From the sensing point of view, structural monitoring solutions include single sensor solutions (Williams et al., 2022), accelerometer arrays distributed along the vertical as well as inplane (Hart and Rojahn, 1979;Clinton et al., 2006;Nayeri et al., 2008;Reuland et al., 2017;Martakis et al., 2022), embedded optical fibers for strain measurements (Glisic et al., 2005), tilt meters, and displacement tracking by means of Global Navigation Satellite Systems (GNSS; Çelebi and Sanli, 2002;Park et al., 2004;Chatzi and Fuggini, 2012). The deployment configuration is dependent on the purpose of the monitoring in terms of the definition of dynamic building properties. ...
... Although modal frequencies can be derived by a single, properly placed sensor, modal shapes typically require monitoring arrays of-most commonly-accelerometer sensors. Under low-amplitude excitation, the recording of the ambient vibration of the structure can be used to derive estimates of structural eigenfrequencies and mode shapes, which are characteristic of the stiffness distribution within a structure and can serve as indicators of existing condition (Reuland et al., 2017;Limongelli and Giordano, 2020). An alternative to ambient vibration monitoring has been suggested recently on the basis of monitoring of buildings under controlled demolition; a process that serves to offer insights into response lying beyond the linear range (Martakis et al., 2022). ...
Full-text available
In this article, we demonstrate that a single station can be used to measure the dynamic properties of a structure. The station includes a collocated accelerometer and rotational sensor, hence, can record both three-component translation and three-component rotation and is referred to as the 6C-station within this study. The key advantage of this approach is to provide a fast and simple path to a comprehensive structural health monitoring characterization that is comparable to the use of a traditional approach using a horizontal array of three-component accelerometers. The deployment of newly developed high-quality rotational sensors allows the direct measurement of structural rotations, facilitating the extraction of structural mode shapes. In this work, we show how an established system identification tool, stochastic subspace identification, can be applied to the 6C-station data and characterize modal properties and structural response. Our results are verified and contrasted against standard horizontal and vertical array configurations. The Prime Tower, a high-rise structure in Zürich, serves as a case study. A structural characterization of this building is presented for the first time. We show that a 6C-station is capable of defining the frequencies of this stiff high-rise building with a fidelity that is on par with a five-sensor horizontal array. The mode shapes of the roof can be precisely determined with a confidence margin that is comparable to conventional sensing array solutions. However, the effectiveness of using only a 6C-station is determined by the noise level of the sensors—in particular, the rotational seismometer needs to be of high quality. The results indicate that, owing to the collocation measurement of translation and rotation, a 6C-station can deliver a comprehensive structural monitoring solution with minimum time, effort, and footprint.
... With this software, it is possible to study the behavior of structures through the continuum phase and the discrete phase of loading, which is of the utmost importance when the structure collapses. Seismic assessment of existing structures using ELS software was done by several researchers [27,[91][92][93]. ...
... through the continuum phase and the discrete phase of loading, which is of the utmost importance when the structure collapses. Seismic assessment of existing structures using ELS software was done by several researchers [27,[91][92][93]. ...
Full-text available
Minarets, tall structures, connected or not to the mosque attract attention due to their specific architectural features. Vulnerability to seismic damage has been witnessed throughout history on tall and slender structures after earthquake ground motions. In that respect, it is of the utmost importance to investigate the dynamic characteristics and resilience of historical stone minarets. This paper aims to provide the results of an on-site dynamic investigation of a stone minaret in Mostar and deliver its seismic assessment. The minaret is part of the Tabačica mosque built at the turn of the 16th and 17th century in the City of Mostar, Bosnia and Herzegovina. The on-site investigation comprised dynamic identification of the minaret by ambient vibration testing and qualitative estimation of the masonry wall by sonic pulse velocity testing. Besides the modal analysis a time-history analysis was performed by using the Applied Element Method (AEM), considered an appropriate tool for assessing the behavior of historic masonry structures. A good match is found between the first natural frequency obtained by the on-site investigation and the modal analysis which is a solid basis for further seismic assessment of the minaret as a slender tower-like structure. The concentration of stresses is observed at the transition zones.
... Although OMA is conducted under very low excitation amplitude, and therefore under an elastic regime, the derived structural properties reduce the uncertainties in the equivalent elastic range. This eventually benefits the assessment of the response in the nonlinear range through nonlinear models [3], or according to formulations that link nonlinear to linear parameters, as available in current standards [4]. Snoj et al. [5] have highlighted, through a parametric study, the importance of the identified elastic properties of existing structures for the accurate assessment of ductility. ...
Conference Paper
p>The loss potential of earthquakes is substantial even for sites of moderate seismicity, due to the disastrous consequences of rare events. Large parts of the existing European building stock, mainly masonry structures, do not fulfil the current seismic standards, while many buildings have long exceeded their design lifespan. Given the inherent uncertainties of masonry as a composite material, unknown effects of ageing, and the corresponding difficulty to estimate the nonlinear response of such structures, data-driven health monitoring provides an efficient way to reduce epistemic uncertainty and to derive damage-sensitive features for structural assessment after strong ground motions. In this study, vibrational recordings during the demolition of a real masonry building have been analyzed. The accumulating damage during demolition provides a valuable insight into the correlation between dynamic response and structural health. The findings shed light on the performance of a typical masonry structure, built in the 19th century, under non-conventional loading and form a step towards the definition of damage-sensitive features based on real data.</p
... The elaboration of data collected concerning building vibrations under the excitation generated by the nearby environment (no need for any form of actuation) allows the determination of some important structural characteristics. For instance, AVM can lead to an estimate of the mode shapes related to natural vibration modes, which can help the engineer to understand the load-bearing system of a building (Reuland et al., 2015) and the quality of construction materials (Reuland et al., 2017a). ...
Among the various natural hazards, earthquakes remain a major source of human casualties and economic losses. It is a well-known fact that earthquakes do not kill people; buildings that collapse during earthquakes kill people. Thus architects, civil engineers and urban planners should work in the way of transforming existing cities into resilient systems that are able to withstand seismic events with the least possible losses. Two main challenges regarding seismic resilience at urban scale are related to determining seismic vulnerability prior to earthquakes as well as real damage suffered and residual capacity following a seismic event. Ambient-vibration measurements (AVM) are a nondestructive data source that, together with standard visual inspections, have potential to improve the efficiency and the accuracy of seismic vulnerability assessment of towns. In the pre-earthquake phase, AVM can help in the detection of the real seismic behavior of structures and in the modeling of existing building classes especially in regions with very limited observational data after significant damaging earthquakes. In the post-earthquake phase, AVM can provide information about the real damage suffered and the future damage in case of an aftershock. A methodology for the up-scaling at urban scale of data collected on the various building classes and their damage assessment is proposed and the applicability of clustering buildings is assessed, showing high potential.
Full-text available
The paper presents the results of the evaluation of the seismic safety of the Ancien Hôpital de Sion, an important Swiss architectural heritage building, sited in the Canton of Valais, the region with the highest seismic hazard in Switzerland. Three-dimensional Applied Element (AEM) modelling of the whole structure has been performed and validated. The adopted modelling strategy, together with non-linear dynamic analysis, was able to represent the actual behaviour and failure mechanisms typical of complex masonry structures, in addition to a good computational efficiency compared to other available numerical approaches. The local collapse mechanisms have been also studied through a kinematic limit analysis based on rigid block rotation. Both linear and non-linear approaches have been followed together with the capacity spectrum method. The results provided by the different methodologies have been compared with the aim to provide possible insights concerning a general procedure for the assessment of the safety of such type of structures.
Full-text available
This paper contains a seismic assessment at urban scale of the cities of Sion and Martigny in Switzerland. These two cities have been identified for the present research based on their importance regarding size and the characteristics of the building stock for which information was available. Moreover, microzonation investigations are available for both cities. This results in a more accurate characterization of local expected ground shaking, which is expressed through specific response spectra. Sion and Martigny represent, respectively, the capital and second largest city of the canton of Valais. This region is characterized by the highest seismicity within Switzerland. The paper focuses on the assessment using Risk-UE methodology, namely the empirical method LM1 and the mechanical method LM2. The obtained results are compared in order to assess the related accuracy. Firstly, buildings of the two cities were surveyed in order to collect main structural characteristics in a database. Building stock is typical of that region and can be found similar to many other medium-sized Swiss cities. Around half of the buildings are unreinforced masonry buildings, while several others are reinforced concrete buildings with shear walls. Results show the most vulnerable part of the cities regarding earthquake. There are significant differences in global results between LM1 and LM2 methods. The mechanical LM2 method is more pessimistic since it predicts damage grades of about one degree higher than LM1 method. However, the main drawback of the empirical LM1 method is that an a priori determination of an adequate value of the macroseismic intensity is required. Nevertheless, LM2 method may lead to a global overestimation of damage prediction.
Full-text available
In this paper a new frequency domain technique is introduced for the modal identification of output-only systems, i.e. in the case where the modal parameters must be estimated without knowing the input exciting the system. By its user friendliness the technique is closely related to the classical approach where the modal parameters are estimated by simple peak picking. However, by introducing a decomposition of the spectral density function matrix, the response spectra can be separated into a set of single degree of freedom systems, each corresponding to an individual mode. By using this decomposition technique close modes can be identified with high accuracy even in the case of strong noise contamination of the signals. Also, the technique clearly indicates harmonic components in the response signals.
Full-text available
For the assessment of structural behaviour, many approaches are available to compare model predictions with measurements. However, few approaches include uncertainties along with dependencies associated with models and observations. In this paper, an error-domain structural identification approach is proposed using ambient vibration monitoring (AVM) as the input. This approach is based on the principle that in science, data cannot truly validate a hypothesis, it can only be used to falsity it. Error-domain model falsification generates a space of possible model instances (combination of parameters), obtains predictions for each of them and then rejects instances that have unlikely differences (residuals) between predictions and measurements. Models are filtered in a two step process. Firstly a comparison of mode shapes based on MAC criterion ensures that the same modes are compared. Secondly, the frequencies from each model instance are compared with the measurements. The instances for which the difference between the predicted and measured value lie outside threshold bounds are discarded. In order to include “uncertainty of uncertainty” in the identification process, a hybrid probability scheme is also presented. The approach is used for the identification of the Langensand Bridge in Switzerland. It is used to falsify the hypothesis that the bridge was behaving as designed when subjected to ambient vibration inputs, before opening to the traffic. Such small amplitudes may be affected by low-level bearing-device friction. This inadvertently increased the apparent stiffness of the structure by 17%. This observation supports the premiss that ambient vibration surveys should be cross-checked with other information sources, such as numerical models, in order to avoid misinterpreting the data.
Full-text available
The knowledge of fundamental frequency and damping ratio of structures is of uppermost importance in earthquake engineering, especially to estimate the seismic demand. However, elastic and plastic frequency drops and damping variations make their estimation complex. This study quantifies and models the relative frequency drop affecting low-rise modern masonry buildings and discusses the damping variations based on two experimental data sets: Pseudo-dynamic tests at ELSA laboratory in the frame of the ESECMaSE project and in situ forced vibration tests by EMPA and EPFL. The relative structural frequency drop is shown to depend mainly on shaking amplitude, whereas the damping ratio variations could not be explained by the shaking amplitude only. Therefore, the absolute frequency value depends mostly on the frequency at low amplitude level, the amplitude of shaking and the construction material. The decrease in shape does not vary significantly with increasing damage. Hence, this study makes a link between structural dynamic properties, either under ambient vibrations or under strong motions, for low-rise modern masonry buildings. A value of 2/3 of the ambient vibration frequency is found to be relevant for the earthquake engineering assessment for this building type. However, the effect of soil–structure interaction that is shown to also affect these parameters has to be taken into account. Therefore, an analytical methodology is proposed to derive first the fixed-base frequency before using these results. Copyright © 2010 John Wiley & Sons, Ltd.
Sensing in the built environment has the potential to reduce asset management expenditure and contribute to extending useful service life. In the built environment, measurements are usually performed indirectly; effects are measured remote from their causes. Modeling approximations from many sources, such as boundary conditions, geometrical simplifications, and numerical assumptions, result in important systematic uncertainties that modify correlation values between measurement points. In addition, conservative behavior models that were employed – justifiably during the design stage, prior to construction – are generally inadequate when explaining measurements of real behavior. This paper summarizes the special context of sensor data interpretation for asset management in the built environment. Nearly 20 years of research results from several doctoral thesis and 14 full-scale case studies in 4 countries are summarized. Originally inspired from research into model-based diagnosis, work on multiple model identification evolved into a methodology for probabilistic model falsification. Throughout the research, parallel studies developed strategies for measurement system design. Recent comparisons with Bayesian model updating have shown that while traditional applications Bayesian methods are precise and accurate when all is known, they are not robust in the presence of approximate models. Finally, details of the full-scale case studies that have been used to develop model falsification are briefly described. The model-falsification strategy for data interpretation provides engineers with an easy-to-understand tool that is compatible with the context of the built environment.
Model-based data-interpretation techniques are increasingly used to improve the knowledge of complex system behavior. Physics-based models that are identified using measurement data are generally used for extrapolation to predict system behavior under other actions. In order to obtain accurate and reliable extrapolations, model-parameter identification needs to be robust in terms of variations of systematic modeling uncertainty introduced when modeling complex systems. Approaches such as Bayesian inference are widely used for system identification. More recently, error-domain model falsification (EDMF) has been shown to be useful for situations where little information is available to define the probability density function (PDF) of modeling errors. Model falsification is a discrete population methodology that is particularly suited to knowledge intensive tasks in open worlds, where uncertainty cannot be precisely defined. This paper compares conventional uses of approaches such as Bayesian inference and EDMF in terms of parameter-identification robustness and extrapolation accuracy. Using Bayesian inference, three scenarios of conventional assumptions related to inclusion of modeling errors are evaluated for several model classes of a simple beam. These scenarios are compared with results obtained using EDMF. Bayesian model class selection is used to study the benefit of posterior model averaging on the accuracy of extrapolations. Finally, ease of representation and modification of knowledge is illustrated using an example of a full-scale bridge. This study shows that EDMF leads to robust identification and more accurate predictions than conventional applications of Bayesian inference in the presence of systematic uncertainty. These results are illustrated with a full-scale bridge. This example shows that the engineering knowledge necessary to perform parameter identification and remaining-fatigue-life predictions of a complex civil structure is easily represented by the EDMF methodology. Model classes describing complex systems should include two components: (1) unknown physical parameters that are identified using measurements; (2) conservative modeling error estimations that cannot be represented only as uncertainties related to physical parameters. In order to obtain accurate predictions, both components need to be included in the model-class definition. This study indicates that Bayesian model class selection may lead to over-confidence in certain model classes, resulting in biased extrapolation.
A new method, Applied Element Method (AEM) for analysis of structures is introduced. The structure is modeled as an assembly of distinct elements made by dividing the structural elements virtually. These elements are connected by distributed springs in both normal and tangential directions. We introduce a new method by which the total behavior of structures can be accurately simulated with reasonable CPU time. This paper deals with the formulations used for linear elastic structures in small deformation range and for consideration of the effects of Poisson's ratio. Comparing with theoretical results, it is proved that the new method is an efficient tool to follow mechanical behavior of structures in elastic conditions.
Conference Paper
Earthquake risk assessment and preparation of earthquake risk scenario is a strong awareness raising and planning tool for implementing earthquake risk management activities. Use of appropriate fragility functions is one the most critical parameters for the accuracy of earthquake risk assessment. This study computed fragility functions for non-engineered low earthquake resistant masonry buildings in Nepal through non-linear analysis using Applied Element Method (AEM). Key parameters required for analysis were obtained through field test in actual field condition. Results obtained from AEM were compared with shaking table experiment and a good agreement was found. Buildings with different configuration, material strength, the number of stories and mortar type were subjected to numerical simulation and probability of damage exceeding a certain level of damage state is calculated for peak ground acceleration (PGA) starting from 0.05g to 1.0g. Fragility functions for low earthquake resistant masonry buildings for different state of damage are plotted based on numerical simulation results.
When system identification methodologies are used to interpret measurement data taken from structures, uncertainty dependencies are in many cases unknown due to model simplifications and omissions. This paper presents how error-domain model falsification reveals properties of a structure when uncertainty dependencies are unknown and how incorrect assumptions regarding model-class adequacy are detected. An illustrative example is used to compare results with those from a residual minimization technique and Bayesian inference. Error-domain model falsification correctly identifies parameter values in situations where there are systematic errors, and can detect the presence of unrecognized systematic errors.