Building Damage Estimation Research Articles

Improving the Accuracy of Building Damage Estimation Model Due to Earthquake Using 10 Explanatory Variables

Aiming to support disaster recovery, we have developed a new method to extract damaged buildings by using machine learning that combines 10 explanatory variables obtained from analysis of aerial photographs and observation data. We used site amplification factors, seismic intensities of foreshock and mainshock, distance from faults, estimated building structures and ages, coverage by blue tarps, texture analysis, and digital surface model differences before and after the earthquake as explanatory variables, in addition to convolutional neural network prediction results based on post-earthquake aerial photographs. The random forest method resulted in an overall accuracy of about 81% and an average F-measure of three classes was about 70%, indicating that it can classify possible damage to buildings more accurately than our previous studies.

Influence of local versus national datasets on seismic loss estimates: A case study for residential buildings in the metropolitan area of Montreal, Canada

Assessing seismic losses requires information on ground shaking levels, a comprehensive inventory of elements at risk and their asset values to evaluate repair/reconstruction costs. The uncertainties associated with these parameters are often significant and the available data are often incomplete. This study aims to assess the value and utility of detailed data in refining seismic risk estimates. Over several years, detailed site conditions and residential exposure datasets were compiled for Greater Montreal. These models were used to estimate building damage and associated losses for various seismic scenarios. Recently, Natural Resources Canada developed a national exposure model to provide a National Earthquake Risk Profile using data derived from existing sources such as topographic maps and census data. This paper compares seismic loss projections for residential buildings using the local dataset as a baseline with those from the national model using OpenQuake and the latest Seismic Hazard Model. The local site model is based on in situ data and site measurements, while the global, proxy-based model relies on USGS correlations between slope topography and site classification. Results show that the global model overestimates seismic hazards, resulting in 30% higher residential losses than the local model. Absolute losses are increased by a factor of 3.6–5.6 depending on the building classification when using the national exposure model compared to the local inventory while relative losses are increased by a factor of 1.1–1.5. These results emphasize the need to develop detailed seismic risk assessment databases for accurate risk assessments, and for selecting appropriate risk reduction measures.

Fuzzy-based approach to quantify the downtime of buildings in developing countries

Earthquake is one of the natural disasters that affects the buildings and communities in developing countries. It causes different levels of damages to the buildings, making them uninhabitable for a period of time, called downtime (DT). This paper proposes a Fuzzy Logic hierarchical method to estimate the downtime of residential buildings in developing countries after an earthquake. The use of expert-based systems allows quantifying the indicators involved in the model using descriptive knowledge instead of hard data, accounting also for the uncertainties that may affect the analysis. The applicability of the methodology is illustrated using the information gathered after the 2015 Gorkha, Nepal, earthquake as a case study. On April 25, 2015, Nepal was hit by the Mw 7.8 Gorkha earthquake, which damaged and destroyed more than 500.000 residential buildings. Information obtained from a Rapid Visual Damage Assessment (RVDA) is used through a hierarchical scheme to evaluate the building damageability. Sensitivity analysis based on Sobol method is implemented to evaluate the importance of parameters gathered in the RVDA for building damage estimation. The findings of this work may be used to estimate the restoration time of damaged buildings in developing countries and to plan preventive safety measures.

City-scale buildings damage estimation based on broadband physics-based ground motion simulation of 2021 Ms 6.4 Yangbi, China, earthquake

This article proposed a computational workflow for city-scale building damage estimation based on broadband physics-based ground motion simulation. The dynamic stiffness matrix method in frequency-wavenumber domain is employed for broadband physics-based ground motion simulation of finite fault along with site response correction using equivalent-linear site response analysis approach. With simulated ground motion, seismic response of buildings, using multi-degree of freedom model, is simulated using nonlinear time history analysis, and building damage is evaluated based on a trilinear backbone curve of story’s nonlinear force-drift model. Main advantage of the framework lies in the employed dynamic stiffness matrix method for ground motion simulation, which can efficiently generate broadband ground motions in perfect parallel and naturally capture the physics of finite fault source, propagation path, and spatial variability characteristics of ground motion. To demonstrate the salient features of proposed workflow, the 2021 Ms 6.4 Yangbi earthquake is simulated with frequency resolution up to 20 Hz, and seismic damage of buildings in Yangbi county are estimated. Results show that most traditional wood-adobe structures suffer moderate to severe damage, and engineered seismic buildings (mostly RC frame) in Yangbi county suffer minor damage, which agrees with field investigation results.

Beyond tsunami fragility functions: experimental assessment for building damage estimation

Tsunami fragility functions (TFF) are statistical models that relate a tsunami intensity measure to a given building damage state, expressed as cumulative probability. Advances in computational and data retrieval speeds, coupled with novel deep learning applications to disaster science, have shifted research focus away from statistical estimators. TFFs offer a “disaster signature” with comparative value, though these models are seldom applied to generate damage estimates. With applicability in mind, we challenge this notion and investigate a portion of TFF literature, selecting three TFFs and two application methodologies to generate a building damage estimation baseline. Further, we propose a simple machine learning method, trained on physical parameters inspired by, but expanded beyond, TFF intensity measures. We test these three methods on the 2011 Ishinomaki dataset after the Great East Japan Earthquake and Tsunami in both binary and multi-class cases. We explore: (1) the quality of building damage estimation using TFF application methods; (2) whether TFF can generalize to out-of-domain building damage datasets; (3) a novel machine learning approach to perform the same task. Our findings suggest that: both TFF methods and our model have the potential to achieve good binary results; TFF methods struggle with multiple classes and out-of-domain tasks, while our proposed method appears to generalize better.

Special Issue on Development of Integrated Expert System for Estimation and Observation of Damage Level of Infrastructure in Lima Metropolitan Area

Earthquakes and tsunami disasters happen suddenly, devastating cities in just a few minutes. For decision makers to manage such disasters, they need current information on the status of their cities, but all information is collected separately and on paper now. All disaster-related information should be integrated to support decision making. To develop such an integrated system, Peru and Japan launched a new SATREPS project, “Development of Integrated Expert System for Estimation and Observation of Damage Level of Infrastructure in Lima Metropolitan Area,” in 2021. The project consists of four groups. Group 1 is related to earthquake and tsunami hazards, estimating the level of shaking and predicting the damage due to tsunami. Group 2 is related to damage detection/estimation, using sensing devices to estimate the level of damage to buildings and other infrastructure. Group 3 is related to understanding the damage over a wide area through satellite images, and also to the integration of all information from Groups 1 to 3. Group 4 is related to developing the capacity to utilize the integrated expert system. This special issue has been organized to present the progress accomplished in the first two years and to share the project’s achievements. It covers the fields of earthquake hazards, building and infrastructure damage estimation, damage estimation with satellite imagery, and human loss estimation.

A Rapid Estimation Method for Post-earthquake Building Losses

Rapid estimation of post-earthquake building damage and loss is very important in urgent response efforts. The current approach leaves much room for improvement in estimating ground motion and correctly incorporating the uncertainty and spatial correlation of the loss. This study proposed a new approach for rapidly estimating post-earthquake building loss with reasonable accuracy. The proposed method interpolates ground motion based on the observed ground motion using the Ground Motion Prediction Equation (GMPE) as the weight. It samples the building seismic loss quantile considering the spatial loss correlation that is expressed by Gaussian copula, and kriging is applied to reduce the dimension of direct sampling for estimation speed. The proposed approach was validated using three historical earthquake events in Japan with actual loss reports, and was then applied to predict the building loss amount for the March 2022 Fukushima Mw7.3 earthquake. The proposed method has high potential in future emergency efforts such as search, rescue, and evacuation planning.

Impact assessment caused by bradyseism phenomena in the Campi Flegrei area

Campi Flegrei (Italy) is among the areas with the greatest volcanic explosive risk in the world due to the dangerousness of the expected hazards, the high exposed value (about 500,000 people will be evacuated during the “alarm phase”), and the vulnerability of the urban settlements under the effect of the volcanic phenomena. The last two dramatic bradyseism phases occurred in 1969–1972 and 1982–1984 when Pozzuoli town was affected by rapid ground inflation, which brought an overall higher level of about 3.5 m and caused numerous earthquakes (M ≤ 4.2), with severe damage to buildings. During 1984, the seismicity was intense, with 33 events with 0.5 < M ≤ 3 and six with 3 < M ≤ 3.8. Subsequently, the Campi Flegrei caldera was characterized by general subsidence for about 20 years until 2005, when a new inflation period started and is still ongoing (∼1 m). The areal distribution of the recent uplift is characterized by the maximum vertical displacement in the town of Pozzuoli, with a radial decrease from the caldera center outwards. The need to better understand Campi Flegrei volcanic activity is fundamental to protecting the population from hazards linked to explosive volcanic eruptions and understanding the role of seismicity as a possible precursor of a potential eruption. In this perspective, as part of the activities of the PLINIVS Study Centre (Centre of Competence of Italian Civil Protection Department for Volcanic Risk), the authors developed a procedure, implemented in a web application, that relates the monitoring of the ground deformation with the behavior of buildings to evaluate the level of progressive damage to the ordinary Phlegraean buildings due to bradyseism in near real time. This study describes the models adopted for the three impact/risk factors (hazard, exposure, and vulnerability) used to estimate building damage.

Empirical estimation based on remote sensing images of insured typhoon-induced economic losses from building damage

Rapid and cost-effective economic loss estimation for buildings in non-life insurance is an important issue for insurance industries in order to provide immediate financial supports to residents affected by natural disasters. This study introduces an empirical approach for economic loss estimation of typhoon-induced building damage from post-disaster remote sensing (RS) images based on insurance records obtained in Osaka and Chiba, Japan affected by the 2018 Typhoon Jebi and the 2019 Typhoon Faxai, respectively. From the insurance records and the analysis of the RS images, we found that area-based loss rates (ALRs) defined as ratio of amount of loss to amount of insured values within a mesh were proportional to building damage ratios (BDRs) identified from number of damaged buildings in the RS images and existing building inventory data, whereas it was still challenging to accurately estimate loss rate for building-by-building even from very high-resolution images. A linear regression function was developed from the relationship between the ALRs and BDRs obtained in this study. We confirmed that the regression function provided a good approximation of the insured losses from the typhoon disasters. The result indicates that typhoon-induced insured losses can be rapidly estimated from the insurance inventory and the analysis of post-disaster RS images without field investigations.

First Level Pre- and Post-Earthquake Building Seismic Assessment Protocol Based on Dynamic Characteristics Extracted In Situ

The present work is concerned with the introduction of a new first level pre- and post-earthquake seismic assessment protocol for buildings that relies on the use of recorded structural response. As earthquakes represent a constant and unpredictable threat for the building stock around the globe, the protocols already in use for assessing the risk should be revised and should also take into account the information hidden in data recorded in the field. Nowadays, data collection does not require expensive equipment and over-qualified personnel. In this direction, the proposed seismic assessment protocol aims to illustrate the ease of widely adopting Structural Health Monitoring (SHM) equipment (e.g., accelerographs), based on the work that has been carried out over the past years on subjects related to earthquake risk estimation. Building taxonomy and damage estimation, like those found in Hazus®–MH and other hazard assessment tools, can be enriched and modified properly to distinguish and classify the very earthquake-prone buildings from the others, and tag them for further assessment and rehabilitation as seismic codes suggest.

Effect of Residential Building Wind Retrofits on Social and Economic Community-Level Resilience Metrics

Tornadoes occur at a high frequency in the United States compared with other natural hazards but have a substantially small footprint. A single high-intensity tornado can result in high casualty rates and catastrophic economic and social consequences, particularly for small- to medium-size communities. Comprehensive community resilience assessment and improvement requires the analyst to develop a model of interacting physical, social, and economic systems and to measure outcomes that result from specific decisions made. These outcomes often are in the form of metrics such as the number of people injured or the number of households and/or businesses without water, but it has been recognized that most community resilience metrics have socioeconomic characteristics. In this study, for the first time, a fully quantitative interacting model is used to examine the effect of a tornado damaging physical infrastructure (buildings and electrical power network) and the effects on the population and the local economy for a real community. Then, three residential building retrofit strategies are considered as alternatives to improve community resilience, and the metrics for the physical, economic, and social sectors are computed. An illustrative example is presented for the 2011 Joplin tornado in a new open-source Interdependent Networked Community Resilience Modeling Environment (IN-CORE), with a computable general equilibrium (CGE) economics model that computes household income, employment, and domestic supply before and after the tornado. Detailed demographic data was allocated to each structure to enable the calculation of resilience metrics related to population dislocation impacts from the tornado. The results of these analyses stemming from building damage estimation have a logical trend, but the substantial contribution of this work is that, for the first time, the effect of retrofit strategies for tornado loading can be quantified in terms of their effects on socioeconomic metrics.

Regional Vulnerability Assessment Based on Building Damage Estimation duet to a Huge Earthquake in Yangon

Regional Vulnerability Assessment Based on Building Damage Estimation duet to a Huge Earthquake in Yangon

Estimation of building damage caused by adjacent deep excavation in clay

Building damage induced by the excavation conducted for the three-storey basement of the Kerry Center – a modernised business centre comprising large-scale departmental stores, business offices, ap...

Estimation of building damage in a 3D distorting structure to tunnel and underground excavation-induced ground movements

Estimating building damage due to ground movements induced by tunneling or open cutting is very important for planning, design, and construction. Such efforts can minimize any possible damage in adjacent structures and help in choosing the most efficient protection measures if needed. This study extends a damage estimation criterion based on the 2D state of strain (Son and Cording, 2005) to estimate building damage and distortion in 3D structural distortion conditions. The 3D state-of-strain concept is used to develop a damage criterion that can be used to estimate building damage and distortion in 3D distorting structural units. The extended 3D criterion is compared with observed field observations as well as numerical test results. The study results can provide a better understanding of building damage estimation in 3D structural distortion conditions in response to tunnel and underground excavation-induced ground movements.

Detection of Collapsed Buildings in Post-Earthquake Remote Sensing Images Based on the Improved YOLOv3

An important and effective method for the preliminary mitigation and relief of an earthquake is the rapid estimation of building damage via high spatial resolution remote sensing technology. Traditional object detection methods only use artificially designed shallow features on post-earthquake remote sensing images, which are uncertain and complex background environment and time-consuming feature selection. The satisfactory results from them are often difficult. Therefore, this study aims to apply the object detection method You Only Look Once (YOLOv3) based on the convolutional neural network (CNN) to locate collapsed buildings from post-earthquake remote sensing images. Moreover, YOLOv3 was improved to obtain more effective detection results. First, we replaced the Darknet53 CNN in YOLOv3 with the lightweight CNN ShuffleNet v2. Second, the prediction box center point, XY loss, and prediction box width and height, WH loss, in the loss function was replaced with the generalized intersection over union (GIoU) loss. Experiments performed using the improved YOLOv3 model, with high spatial resolution aerial remote sensing images at resolutions of 0.5 m after the Yushu and Wenchuan earthquakes, show a significant reduction in the number of parameters, detection speed of up to 29.23 f/s, and target precision of 90.89%. Compared with the general YOLOv3, the detection speed improved by 5.21 f/s and its precision improved by 5.24%. Moreover, the improved model had stronger noise immunity capabilities, which indicates a significant improvement in the model’s generalization. Therefore, this improved YOLOv3 model is effective for the detection of collapsed buildings in post-earthquake high-resolution remote sensing images.

Flood damage calculations via a RESTful API

In Canada and across the globe, flooding has significantly increased in recent decades. Knowledge of the flood hazard alone (extent and frequency) does not provide enough information to the public safety community to make informed decisions regarding potential social and economic losses. While many programs have been developed for flood risk assessment, they require experienced personnel to prepare the data inputs, run and analyse the model outputs. As such, alternative, easy-to-use tools for risk assessment are appealing. We present in this paper a RESTful Application Program Interface (API) for building damage estimation. This API is a web-service which allows access to buildings damage calculations outside of a risk assessment program. It is designed to provide a point of entry for developers to programmatically access the underlying calculation tools to query, calculate and extract damage data directly into a users own software, tools or decision support systems. To validate the accuracy of results the damage data is compared to results from existing software for two communities in Eastern Canada. To illustrate the ease of use of the API, a Python script has been written which reads in a file containing the building information and pre-determined flood depths. This script builds the request for the API and saves the results: damage and exposure.

Prediction of building damage induced by tunnelling through an optimized artificial neural network

Ground surface movement due to tunnelling in urban areas imposes strains to the adjacent buildings through distortion and rotation, and may consequently cause structural damage. The methods of building damage estimation are generally based on a two-stage procedure in which ground movement in the greenfield condition is estimated empirically, and then, a separate method based on structural mechanic principles is used to assess the damage. This paper predicts the building damage based on a model obtained from artificial neural network and a particle swarm optimization algorithm. To develop the model, the input and output parameters were collected from Line No. 2 of the Karaj Urban Railway Project in Iran. Accordingly, two case studies of damaged buildings were used to assess the ability of this model to predict the damage. Comparison with the measured data indicated that the model achieved the satisfactory results.

An Assessment of Earthquake Risk in Thecho of Kathmandu Valley Nepal: Scenario and Reality

Natural disaster cannot be stopped but its effect can be minimized or avoided by adopting technology and necessary human adjustment. Earthquake is a natural event which occurs without early warning signs. Computer based earthquake scenarios are used worldwide to describe and estimate the damage from potential earthquakes. The current study is an attempt to explore potential risk with respect to physical infrastructure and assess modeled and actual physical damage and human loss caused by different earthquake scenario and actual 2015 earthquake event in Thecho of Kathmandu valley. The earthquake scenario is based on two nearest fault lines. Risk Assessment Tools for the Diagnosis of Urban Seismic Risk (RADIUS) method has been applied for estimation of potential building damage and casualties..The research has adopted integrated approach using secondary and primary data sources such as field observation, key informant survey and building survey through purposive random sampling.The study found that potential building damage estimated by RADIUS for Gorkha 2015 earthquake scenario and North-west (Khokana) are lower than the actual post-earthquake assessment whereas North earthquake scenario resulted higher loss. Actual damage caused by 2015 earthquake compared to modeled damage from RADIUS is found higher because additional damaged were made by successive aftershocks. Spatial distribution of potential building damage for earthquake scenarios and actual 2015 earthquake event is also variable. North-Nuwakot Earthquake Scenario resulted more hazardous than the North-Khokana scenario though the location of epicenter is relatively farther with high intensity. The study concluded that though earthquake occurrence and disaster is still less predictable risk assessment tools like RADIUS and mitigation measures based on such is important for reducing risk of earthquake disaster.The Geographical Journal of NepalVol. 11: 127-136, 2018

Estimated probability of the number of buildings damaged by the floods and the cost of repairs

Flood disasters often cause buildings damaged, for repairs required considerable cost. This paper analyzes estimates of the probability and the number of buildings damaged by the Citarum River flood in the framework of cost recovery planning. The probability estimation of building damage was carried out using a logistic regression model with a genetic algorithm approach. The number of buildings damaged and risk of losses are estimated using the principle of expected value. The analysis shows that the probability estimator of the building damage significantly follows the logistic regression model. Meanwhile, the estimated the building damage is 349, with a loss of IDR67.80 billion. So the related parties (government, financial institutions and homeowners) should reserve the cost of repair at least worth the risk of loss, to face future flood disasters.

An improved model for seismic risk assessment in Portugal

PurposePortugal experienced very destructive earthquakes in the past, such as the well-known “Lisbon earthquake” in 1755. With such in mind, accurate estimates of human and economic losses can play a significant role in providing various societal key players with objective information for response strategies. This paper aims to present the contribution of the most recent study in Portugal (PRISE) concerning comprehensive seismic risk assessment, which can be used as good practice and reproduced in different contexts.Design/methodology/approachPRISE (earthquake loss assessment of the Portuguese building stock) covered three main lines of research, corresponding to the three components typically considered in any seismic risk assessment study: the characterization of the seismic hazard, the identification of the exposure to earthquakes and loss potential and the vulnerability of the exposed assets. Each of these components has been fully characterized through the collection of census and local data (exposure), used to carry out nonlinear analysis (hazard and fragility).FindingsBy involving different research institutions and partners with extensive knowledge and expertise in the earthquake domains, the developed model is capable of producing economic and human earthquake loss estimates in real time (through an innovative Web-based platform) or for specific event scenarios, considering exposed population, residential and industrial buildings. The platform uses open-source tools and hence, it can be reproduced in other countries or contexts.Research limitations/implicationsResearch wise, the hazard, vulnerability and exposure models can still be significantly improved, e.g. by adding critical infrastructure (hospitals, school buildings and bridges) or updating the nonlinear models, for more accurate loss predictions.Practical implicationsThe findings and loss estimates for different earthquake scenarios show that planned interventions are required. Decision-makers and other relevant stakeholders (Civil Protection) can make use of the developed platform to produce specific estimates, to test the effect of different retrofitting interventions or to plan for emergency scenarios.Originality/valueA real-time Web-based framework to estimate building damage and economic/human losses because of seismic events has been developed, aiming to provide the Portuguese Civil Protection and other playmakers with a unique platform for planning and preparing for emergency scenarios.