A Complementary Engineering-Based Building Damage Estimation for Earthquakes in Catastrophe Modeling

Catastrophe modeling for earthquakes is conventionally designed as a probabilistic model to estimate the losses based on risk and vulnerability of a portfolio of exposures for a foreseeable set of events. This approach lacks a physical science of building damage that is linked to ground-shaking characteristics. A proposed engineeringbased building damage estimation model based on established theories of seismic wave propagation and structural resonance is presented to address some of these shortcomings. A damage factor is introduced to provide an indication of the relative extent of damage to buildings.Analysis based on the proposed methodology is carried out using data derived from four case studies: the 2011 Tohoku earthquake; the 2007 Bengkulu earthquake; the 2011 Christchurch earthquake; and the 1999 Chi-Chi earthquake.Results show that the computed damage factors reasonably reflect the extent of actual damage to buildings that was observed in post-earthquake reconnaissance surveys. This indicates that the proposed damage simulation model has a promising future as a complementary assessment tool in building damage estimation in catastrophe modeling.

Estimation of Load-Induced Damage and Repair Cost in Post-Tensioned Concrete Rocking Walls

Post-tensioned concrete rocking walls might be used to avoid severe seismic damage at the base of structural walls, decrease residual drift, and lessen post-earthquake repair costs. The prediction of load-induced damage to the rocking wall resulting from seismic loading can provide an extremely valuable tool to evaluate the status and safety of structural concrete walls following earthquakes. In this study, the behavior and the damage state of monolithic, self-centering, rocking walls, as a new type of concrete rocking wall, are investigated. The nonlinear mechanical behavior of the wall is first modeled numerically, and subsequently the mechanical parameters from the numerical simulation are used to generate the local damage index. The results from the damage index model are compared with the full-scale test results, confirming the viability of the numerically based damage index method for estimating the seismically induced damage in concrete walls. Moreover, the estimated damage can be utilized as a qualitative and quantitative scale to assess the status of the wall following seismic loading events.Finally, an equation is proposed to estimate the repair cost based on the predicted damage state for the studied structural system.

The use of Artificial Neural Networks to estimate seismic damage and derive vulnerability functions for traditional masonry

ABSTRACT This paper discusses the adoption of Artificial Intelligence-based techniques to estimate seismic damage,not with the goal of replacing existing approaches,but as a mean to improve the precision of empirical methods.For such,damage data collected in the aftermath of the 1998 Azores earthquake(Portugal)is used to develop a comparative analysis between damage grades obtained resorting to a classic damage formulation and an innovative approach based on Artificial Neural Networks(ANNs).The analysis is carried out on the basis of a vulnerability index computed with a hybrid seismic vulnerability asssment methodology,which is subsequently used as input to both approaches.The results obtained are then compared with real post-earthquake damage observation and critically discussed taking into account the level of adjustment achieved by each approach.Finally,a computer routine that uses the ANN as an approximation function is developed and applied to derive a new vulnerability curve expression.In general terms,the ANN developed in this study allowed to obtain much better approximations than those achieved with the original vulnerability approach,which has revealed to be quite non-conservative.Similarly,the proposed vulnerability curve expression was found to provide a more accurate damage prediction than the traditional analytical expressions.

Estimation of flea beetle damage in the field using a multistage deep learning-based solution

Estimation of damage in plants is a key issue for crop protection.Currently,experts in the field manually assess the plots.This is a time-consuming task that can be automated thanks to the latest technology in computer vision(CV).The use of image-based systems and recently deep learning-based systems have provided good results in several agricultural applications.These image-based applications outperform expert evaluation in controlled environments,and now they are being progressively included in non-controlled field applications.A novel solution based on deep learning techniques in combination with image processingmethods is proposed to tackle the estimate of plant damage in the field.The proposed solution is a two-stage algorithm.In a first stage,the single plants in the plots are detected by an object detection YOLO based model.Then a regression model is applied to estimate the damage of each individual plant.The solution has been developed and validated in oilseed rape plants to estimate the damage caused by flea beetle.The crop detection model achieves a mean precision average of 91%with a mAP@0.50 of 0.99 and a mAP@0.95 of 0.91 for oilseed rape specifically.The regression model to estimate up to 60%of damage degree in single plants achieves a MAE of 7.11,and R2 of 0.46 in comparison with manual evaluations done plant by plant by experts.Models are deployed in a docker,and with a REST API communication protocol they can be inferred directly for images acquired in the field from a mobile device.

Dynamic modeling and damage analysis of debris cloud fragments produced by hypervelocity impacts viaimage processing

It is always a challenging task to model the trajectory and make an efficient damage estimation of debris clouds produced by hypervelocity impact(HVI)on thin-plates due to the difficulty in obtaining high-quality fragment images from experiments.To improve the damage estimation accuracy of HVIs on a typical double-plate Whipple shield configuration,we investigate the distributive characteristic of debris clouds in successive shadowgraphs using image processing techniques and traditional numerical methods.The aim is to extract the target movement parameters of a debris cloud from the acquired shadowgraphs using image processing techniques and construct a trajectory model to estimate the damage with desirable performance.In HVI experiments,eight successive frames of fragment shadowgraphs are derived from a hypervelocity sequence laser shadowgraph imager,and four representative frames are selected to facilitate the subsequent feature analysis.Then,using image processing techniques,such as denoising and segmentation techniques,special fragment features are extracted from successive images.Based on the extracted information,image matching of debris is conducted and the trajectory of debris clouds is modeled according to the matched debris.A comparison of the results obtained using our method and traditional numerical methods shows that the method of obtaining hypervelocity impact experimental data through image processing will provide critical information for improving numerical simulations.Finally,an improved estimation of damage to the rear wall is presented based on the constructed model.The proposed model is validated by comparing the estimated damage to the actual damage to the rear wall.

Windborne debris damage prediction analysis

Windborne debris is one of the most important causes of the envelop destruction according to the postdamage investigations.The problem of windborne debris damage could be summarized as three parts,including windborne debris risk analysis,debris flying trajectories,and impact resistance of envelope analysis.The method of debris distribution is developed.The flying trajectories of compact and plate-like debris are solved by using a numerical method according to the different aerodynamic characteristics.The impact resistance of the envelopes is also analyzed.Besides,the process of windborne debris damage analysis is described in detail.An example of industrial building is given to demonstrate the whole method by using the observed data of typhoon Chanchu(2006).The method developed in this paper could be applied to risk assessment of windborne debris for structures in wind hazard.