System For Damage Assessment Research Articles

Systematic assessment method for post-earthquake damage of regional buildings using adaptive-network-based fuzzy inference system

Rapid damage assessment of regional buildings is critical in post-earthquake rescue and victim resettlement. In this study, a novel rapid damage assessment method for post-earthquake buildings based on multi-types of damage features was proposed. First, the roof-related damage features are extracted from UAV tilt photography with the method of multiscale segmentation and object-oriented classification. Then, the normalized digital surface model was introduced to determine the height-related damage features. Meanwhile, a lightweight CNN model was employed to realize the preliminary evaluation of building facade conditions. Finally, all obtained damage features were taken as inputs to an adaptive-network-based fuzzy inference system for damage assessment. The proposed method was characterized by the capability to comprehensively cover the damage features of regional buildings involved in the evaluation, and thus be able to obtain more accurate judgments than current methods based on a single feature. At the same time, the effectiveness and robustness of the proposed method were evaluated and demonstrated by the field test on the world's largest earthquake site in Beichuan, China, and the prediction of damage level on the testing set achieved an accuracy of 87.1%.

Artificial Neural Network–Based Multisensor Monitoring System for Collision Damage Assessment of Lithium‐Ion Battery Cells

The sales of electric vehicles (EVs) have seen a significant upward trend in recent years. The occurrence of severe collision accidents has raised awareness concerning safety issues of lithium‐ion batteries (LIBs) which are the core components of EV energy storage systems. When collision accidents of vehicles occur, in most cases, the battery cells will immediately burn and even explode, whereas in some cases, despite not directly showing visual damages, the battery cells may lead to delayed catastrophic failures. Herein, the assessment of the battery cells collision damage based on sensor signal data is focused on by identifying potentially unsafe cells. A campaign of collision experimental tests and a series of electrical performance tests are conducted on single‐cell specimens. A cell damage characterization procedure is proposed and implemented on the battery cells after the collision tests. The impact force and z‐axis acceleration signal features and respective damage classes are input to an artificial neural network (ANN) pattern classifier to train a model to assess the battery cell collision damage.

Knowledge-based system for damage assessment after earthquake: Algerian buildings case

Damage assessment is a very important task after an earthquake. On the basis of the experience of the past Algerian earthquakes, it is important to recognize that often the number of professional experts is insufficient, therefore, non-expert professionals should be included in the execution of the damage assessment task. In addition, the use of houses immediately after earthquake is a very common fact among the inhabitants. For that matter, initiatives are multiplying in Algeria aiming at setting up sophisticated and rapid tools dedicated to estimating the earthquake consequences. Thus, this study focuses on the development of a multifunctional Knowledge-Based System (KBS) called post-seismic damage inspection tool (EDPS: Evaluation des Dommages Post-Sismique), a powerful tool which allows the evaluation, the processing and the archiving of the collected data stock after earthquakes. EDPS can be operated by two user types; an ordinary user for the damage visual inspection and an administrative user for updating the knowledge base and/or for adding or removing the expert type user. The tool consists essentially of four modules: interface, help, explanation and knowledge acquisition. The knowledge acquisition is driven by a hierarchical knowledge model. The Information from damage investigation reports and those acquired through feedback from expert/engineer questionnaires are part of the Knowledge Base (KB). Finally, an evaluation test with EDPS was conducted by means of the expert and proprietary interface.

Phosphorylated histone 2AX foci determination in capillary blood mononuclear cells

Background: One of the most severe variants of DNA lesions is the DNA double-strand break (DSB). Generally, non-physiological DSBs occur as a result of ionizing radiation, oxidative or mechanical stress and chemical effects. DSBs can be detected by phosphorylated histone 2AX (γH2AX) foci analysis using indirect immunofluorescence assay (IFA). Peripheral blood mononuclear cells (PBMCs) obtained by venipuncture and density gradient centrifugation have mainly been used for DSB assessment applying IFA but capillary blood mononuclear cells (CBMCs) could be an alternative facilitating DSB analysis. Methods: CBMCs and PBMCs were isolated from capillary and venous blood of ten human volunteers. Nuclear characteristics and DSBs induced by etoposide were determined by IFA using an automated microscopy interpretation system for DNA damage assessment. Results: The number of isolated PBMCs and CBMCs, their nuclear characteristics as well as the number and characteristics of etoposide-induced DSBs were not significantly different in capillary and venous cells (P>0.05). Conclusions: CBMCs isolated from capillary finger stick blood can be used for DSB analysis by γH2AX foci assessment instead of PBMCs obtained by venipuncture. The use of CBMCs could enhance the implementation of γH2AX as a biomarker for individual diagnostics in precision medicine.

Intelligent system for cultural objects identification, damage assessment and restoration

Intelligent system for cultural objects identification, damage assessment and restoration

Intelligent system for cultural objects identification, damage assessment and restoration

Cultural objects and art works need ongoing conservation interventions in order to be available for the next generations. The most object-friendly analysis approaches are based on non-destructive techniques (NDTs) that allow both the materials characterisation as well as the decay detection of cultural artefacts. Non-destructive testing and evaluation includes the employment of several methods such as the well-established technique of diffuse reflectance spectroscopy with fibre optics (FORS). Such techniques produce output with multiple series of data for multiple different pigment used in objects. In this work, we present a data management solution that contributes with: 1) a library of known reference pigments/colours; 2) a proposed pattern matching technique that allows the automatic classification of any new pigment. The experimental evaluation results show that the data processing proposed is effective. Feedback is particularly encouraging as it allows automation and therefore radically decreased time for pigment/colour matching and identification.

Complete Coverage Path Planning for a Multi-UAV Response System in Post-Earthquake Assessment

This paper presents a post-earthquake response system for a rapid damage assessment. In this system, multiple Unmanned Aerial Vehicles (UAVs) are deployed to collect the images from the earthquake site and create a response map for extracting useful information. It is an extension of well-known coverage path problem (CPP) that is based on the grid pattern map decomposition. In addition to some linear strengthening techniques, two mathematic formulations, 4-index and 5-index models, are proposed in the approach and coded in GAMS (Cplex solver). They are tested on a number of problems and the results show that the 5-index model outperforms the 4-index model. Moreover, the proposed system could be significantly improved by the solver-generated cuts, additional constraints, and the variable branching priority extensions.

Testing a geographical information system for damage and evacuation assessment during an effusive volcanic crisis

Abstract Using two hypothetical effusive events in the Chaîne des Puys (Auvergne, France), we tested two geographical information systems (GISs) set up to allow loss assessment during an effusive crisis. The first was a local system that drew on all immediately available data for population, land use, communications, utility and building type. The second was an experimental add-on to the Global Disaster Alert and Coordination System (GDACS) global warning system maintained by the Joint Research Centre (JRC) that draws information from open-access global data. After defining lava-flow model source terms (vent location, effusion rate, lava chemistry, temperature, crystallinity and vesicularity), we ran all available lava-flow emplacement models to produce a projection for the likelihood of impact for all pixels within the GIS. Next, inundation maps and damage reports for impacted zones were produced, with those produced by both the local system and by GDACS being in good agreement. The exercise identified several shortcomings of the systems, but also indicated that the generation of a GDACS-type global response system for effusive crises that uses rapid-response model projections for lava inundation driven by real-time satellite hotspot detection – and open-access datasets – is within the current capabilities of the community.

Simulation-based decision support system for flood damage assessment under uncertainty using remote sensing and census block information

The level of damage of flood events does not solely depend on exposure to flood waters. Vulnerabilities due to various socio-economic factors such as population at risk, public awareness, and presence of early warning systems, etc. should also be taken into account. Federal and state agencies, watershed management coalitions, insurance companies, need reliable decision support system to evaluate flood risk, to plan and design flood damage assessment and mitigation systems. In current practice, flood damage evaluations are generally carried out based on results obtained from one dimensional (1D) numerical simulations. In some cases, however, 1D simulation is not able to accurately capture the dynamics of the flood events. The present study describes a decision support system, which is based on 2D flood simulation results obtained with CCHE2D-FLOOD. The 2D computational results are complemented with information from various resources, such as census block layer, detailed survey data, and remote sensing images, to estimate loss of life and direct damages (meso or micro scale) to property under uncertainty. Flood damage calculations consider damages to residential, commercial, and industrial buildings in urban areas, and damages to crops in rural areas. The decision support system takes advantage of fast raster layer operations in a GIS platform to generate flood hazard maps based on various user-defined criteria. Monte Carlo method based on an event tree analysis is introduced to account for uncertainties in various parameters. A case study illustrates the uses of the proposed decision support system. The results show that the proposed decision support system allows stake holders to have a better appreciation of the consequences of the flood. It can also be used for planning, design, and evaluation of future flood mitigation measures.

Acoustic emission diagnosis system and wireless monitoring for damage assessment of concrete structures

Concrete is the most widely used materials for building civil structures. First, this study aims to identify the differences of AE event patterns caused by both actual crack development and the other sources. Secondly, it was focused to develop acoustic emission diagnosis system for assessing the deterioration of concrete structures like a bridge. Thirdly, a feasible study was done for the application of wireless acoustic emission sensor module to concrete structures. In this work, the algorithm for determining the damage status of concrete structures was developed and some criteria for decision making were also suggested. For the future application of wireless monitoring, a compact wireless acoustic emission sensor module was developed and applied to the concrete beam for performance test. Finally, based on the self-developed diagnosis algorithm, new AE system for practical AE diagnosis was demonstrated for assessing the conditions of damage and distress in concrete structures.

A Split-Based Approach to Unsupervised Change Detection in Large-Size Multitemporal Images: Application to Tsunami-Damage Assessment

This paper presents a split-based approach (SBA) to automatic and unsupervised change detection in large-size multitemporal remote-sensing images. Unlike standard methods that are presented in the literature, the proposed approach can detect in a consistent and reliable way changes in images of large size also when the extension of the changed area is small (and, therefore, the prior probability of the class of changed pixels is very small). The method is based on the following: 1) a split of the large-size image into subimages; 2) an adaptive analysis of each subimage; and 3) an automatic split-based threshold-selection procedure. This general approach is used for defining a system for damage assessment in multitemporal synthetic aperture radar (SAR) images. The proposed system has been developed to properly identify different levels of damages that are induced by tsunamis along coastal areas. Experimental results that are obtained on multitemporal RADARSAT-1 SAR images of the Sumatra Island, Indonesia, confirm the effectiveness of both the proposed SBA and the presented system for tsunami-damage assessment

Knowledge-based expert system for damage assessment and vulnerability analysis of structures subjected to cyclones

The knowledge-based system developed for damage assessment and vulnerability analysis of structures subjected to cyclones is described in this paper. Fuzzy sets are used for handling the uncertainties in the knowledge. Representation of the knowledge is dictated by the hierarchy of dependences of the various parameters that affect the damage and vulnerability of a structure. Knowledge acquisition is driven by a hierarchical knowledge model. The information from damage survey reports, and that acquired through feedback to questionnaires from experts/engineers forms a part of the knowledge basis. The working of the knowledge-based system is demonstrated through example problems of damage assessment.

A Fuzzy Expert System for Damage Assessment Using Genetic Algorithms and Neural Networks

Abstract: An efficient knowledge‐acquisition support method is required for improvement and maintenance of the knowledge base in durability evaluation of an RC bridge deck. Such a method is proposed in this paper to automatically acquire fuzzy production rules. This method makes joint use of genetic algorithms and a neural network. Using a neural network as a subsystem, the evaluation function of genetic algorithms can be provided with the weights of the neural network. Introducing a neural network into genetic algorithms, it is possible to acquire new knowledge so that the method is useful when it is difficult to acquire knowledge in the field.

Toward an expert system for damage assessment of structural concrete elements

AbstractThe assessment of damage of structural concrete elements relies on the engineering judgment of the person in charge of the inspection and evaluation. This paper describes a rule-based prototype expert system developed to assist an engineer engaged in the task of assessing postearthquake damage to structural concrete elements in the task of providing guidance for inspection as well as criteria for evaluation and courses of action to take afterwards. The expert system, called DASE, identifies the most likely failure modes that may be developed by columns or beams of a damaged building, determines the severity of damage and, suggests immediate actions to take afterwards, such as rehabilitation procedures or tests, to maintain an acceptable local safety level. Floor damage classification and restoration guidelines can also be provided, assuming that all of the structural components in a building's floor have been inspected. The Analytic Hierarchy Process has been implemented as the overall framework to determine the severity of damage, since it allows judgments and personal values to be represented in a systematic and rational manner. DASE provides graphics that customize the user interface and an explanation facility. The knowledge acquisition process consisted primarily of the analysis of documented knowledge found through literature research.

An expert system for damage assessment of a reinforced concrete bridge deck

This paper attempts to develop an expert system for assessing damage states of bridge structures, where the focus is put on the reinforced concrete bridge deck, because its failures have occasionally been reported. The expert system consists of an interpreter, data-base and rule-base. All the rules involved are described through production rules with certainty factors. An example is presented to demonstrate the expert developed.

Knowledge-based expert system for damage assessment based on fuzzy reasoning

This paper attempts to develop a knowledge-based expert system for assessing damage states of bridge structure, where the focus is put on reinforced concrete bridge deck, because its failures have been occasionally reported. Similar to the usual expert systems, this system consists of a rule-base, working memory and interpreter. However, this system has such a remarkable feature that it includes a fuzzy set manipulation system which can treat fuzzy sets in the process of data handling, rule representation and inference procedure. Using this system, it is possible to deal with various kinds of uncertainties and ambiguities involved inherently in the data, rules and inference process in a unified and simple manner. An illustrative example is presented to demonstrate the applicability of the system developed herein.

An expert system for structure damage assessment

An expert system called Sperill-II is introduced for the damage assessment of existing structures using the knowledge of experienced structural engineers. Fuzzy sets and Dempster and Shafer's theory are used in this inexact inference method.