Post-earthquake assessment of buildings damage using fuzzy logic

Allali, Sid Ahmed; Abed, Mohamed; Mébarki, Ahmed

doi:10.1016/j.engstruct.2018.03.055

Cited by 52 publications

(15 citation statements)

References 23 publications

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“…To this end, De Stefano et al (1999) used ANN and Bayesian classification to predict seismic damage mechanisms of historic churches. Fuzzy logic models have been utilized in a collection of studies that transform physical descriptions of seismic damage into mathematical model parameters (Allali et al, 2018; Alvanitopoulos et al, 2010; Carreño et al, 2010; Demartinos and Dritsos, 2006; Elwood and Corotis, 2015; Silva and Garcia, 2001). Recently, the linguistic damage records from the 2014 South Napa earthquake have been used to develop a DL-based method that classifies the building damage (Mangalathu and Burton, 2019).…”

Section: System Identification and Damage Detectionmentioning

confidence: 99%

The promise of implementing machine learning in earthquake engineering: A state-of-the-art review

et al. 2020

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Machine learning (ML) has evolved rapidly over recent years with the promise to substantially alter and enhance the role of data science in a variety of disciplines. Compared with traditional approaches, ML offers advantages to handle complex problems, provide computational efficiency, propagate and treat uncertainties, and facilitate decision making. Also, the maturing of ML has led to significant advances in not only the main-stream artificial intelligence (AI) research but also other science and engineering fields, such as material science, bioengineering, construction management, and transportation engineering. This study conducts a comprehensive review of the progress and challenges of implementing ML in the earthquake engineering domain. A hierarchical attribute matrix is adopted to categorize the existing literature based on four traits identified in the field, such as ML method, topic area, data resource, and scale of analysis. The state-of-the-art review indicates to what extent ML has been applied in four topic areas of earthquake engineering, including seismic hazard analysis, system identification and damage detection, seismic fragility assessment, and structural control for earthquake mitigation. Moreover, research challenges and the associated future research needs are discussed, which include embracing the next generation of data sharing and sensor technologies, implementing more advanced ML techniques, and developing physics-guided ML models.

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Section: System Identification and Damage Detectionmentioning

confidence: 99%

The promise of implementing machine learning in earthquake engineering: A state-of-the-art review

et al. 2020

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“…The study covered the uncertainties of this approach by inducing an enhanced hierarchical structural model, through formulating the interval type of the fuzzy logic analysis. Moreover, Allali et al [26] introduced an assessment model for post-earthquake analysis using the fuzzy logic approach. It was assessed based on technical reports written by trained staff and modelled using a genetic algorithm to evaluate and optimize the global structural damage parameters.…”

Section: Research Backgroundmentioning

confidence: 99%

Assessment of Seismic Building Vulnerability Using Rapid Visual Screening Method through Web-Based Application for Malaysia

et al. 2021

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Rapid visual screening is a quick and simple approach often used by researchers to estimate the seismic vulnerability of buildings in an area. In this study, preliminary seismic vulnerability assessment of 500 buildings situated at Northern and Eastern George Town, Malaysia, was carried out by utilizing a modified FEMA-154 (2002) method that suits Malaysian conditions. Data were collected from online sources via Google Maps and Google Earth instead of traditional surveying data collection through street screening. The seismic assessment analysis of this study was based on the RVS performance score and the damage state classification for each building typology. This approach generates, for each building, a final performance score based on governing parameters such as structural resisting system, height, structural irregularities, building age, and soil type. The findings revealed the immediate need for effective seismic mitigation strategies, as 90% of the studied buildings required a further detailed analyses to pinpoint their exact seismic vulnerability performance. Most of the surveyed buildings were predicted to experience moderate-to-substantial damage, with 220 out of 500 being classed as damage state 2 (D2) and damage state 3 (D3). A GIS map, “RVS Malaysian Form-George Town Area”, was generated via ArcGIS and shared with the public to provide vital information for further research.

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“…Some examples of written ifthen rules can be seen in Figure 11. The inference method follows Mamdani inference system (Allali et al, 2018). The result of applying fuzzy rules on the fuzzy factor maps is a vulnerability map.…”

Section: Figure 10 Damage Membership Functionmentioning

confidence: 99%

Public Participation in Gis via Mobile Applications for Crisis Management Process: A Case Study of an Earthquake, Tehran, Iran

Farhadpour

Hosseinali

2019

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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Abstract. The effects of earthquakes on societies are greater than what most people think. These effects range from structural damages to economic impacts and fatalities. An earthquake only lasts for a few seconds and the aftershocks may continue for days, but the damage does continue for years. The lack of integrated and automated tools for estimating resources needed in the aftermath of an earthquake during the disaster response phase motivate the design and implementation of a comprehensive, interactive, and user-friendly model. The objective of this research is to use geospatial analyses for examining vulnerable areas and evaluating the hazard of building damage by earthquake. Hence, the effective parameters of damage are identified and by using the fuzzy inference, the degree of damage in the study area is determined. Subsequently, with the provision of internet service in a troubled region, it is possible to collect voluntary information from people in the region using their mobile smartphones. For this purpose, an app in the Android Studio environment has been developed for convenience and sending the exact location of the affected sites. Moreover, the result of this research identified areas with a high priority for relief and optimal use of time, especially in the early hours of the crisis.

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Post-earthquake assessment of buildings damage using fuzzy logic

Cited by 52 publications

References 23 publications

The promise of implementing machine learning in earthquake engineering: A state-of-the-art review

The promise of implementing machine learning in earthquake engineering: A state-of-the-art review

Assessment of Seismic Building Vulnerability Using Rapid Visual Screening Method through Web-Based Application for Malaysia

Public Participation in Gis via Mobile Applications for Crisis Management Process: A Case Study of an Earthquake, Tehran, Iran

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