A GIS‐based multi‐criteria seismic vulnerability assessment using the integration of granular computing rule extraction and artificial neural networks

Abstract

This study proposes multi-criteria group decision-making to address seismic physical vulnerability assessment. Granular computing rule extraction is combined with a feed forward artificial neural network to form a classifier capable of training a neural network on the basis of the rules provided by granular computing. It provides a transparent structure despite the traditional multi-layer neural networks. It also allows the classifier to be applied on a set of rules for each incoming pattern. Drawbacks of original granular computing (GrC) are covered, where some input patterns remained unclassified. The study was applied to classify seismic vulnerability of the statistical units of the city of Tehran, Iran. Slope, seismic intensity, height and age of the buildings were effective parameters. Experts ranked 150 randomly selected sample statistical units with respect to their degree of seismic physical vulnerability. Inconsistency of the experts' judgments was investigated using the induced ordered weighted averaging (IOWA) operator. Fifty-five classification rules were extracted on which a neural network was based. An overall accuracy of 88%, j 5 0.85 and R 2 5 0.89 was achieved. A comparison with previously implemented methodologies proved the proposed method to be the most accurate solution to the seismic physical vulnerability of Tehran.

An Automatic Monitoring Method of Slope Deformation in Open-pit mine based on BP Neural Network and GIS Technology

“…The most important feature of neural network is that it has nonlinear, adaptive and powerful learning function and error correction function. This is also consistent with the complexity and nonlinear characteristics of slope stability, so the study of slope deformation has a good basis for mathematical model research [17]. As shown in figure 1.…”

Assessment of Enhanced Dempster-Shafer Theory for Uncertainty Modeling in a GIS-Based Seismic Vulnerability Assessment Model, Case Study—Tabriz City

“…However, decision making for estimating building damage involves some uncertainties [22]. Limited number of studies have focused on dealing with intrinsic uncertainties of multiple decision making for assessing seismic vulnerability of buildings ([e.g., References [2,[23][24][25][26]). This paper focuses on studying one of the epistemic uncertainties in assessing seismic vulnerability of buildings namely inconsistency in a multi criteria decision making framework using fuzzy sets and Dempster-Shafer theories implemented on vector datasets.…”

Physical Seismic Vulnerability Assessment of Tehran Using The Integration of Granular Computing and Interval Dempster- Shafer

“…Seismic vulnerability criteria that are considered in this study include topographic slope in percent (slope), intensity of earthquake per unit (MMI), the percentage of poor quality buildings with four floors or less (Buil_Less4), the percentage of poor quality buildings with more than four floors (Buil_more4), the percentage of buildings constructed before 1966 (Bef-66), the percentage of buildings constructed between 1966 and1988 (Bet-66-88). (Aghataher et al, 2005, Silavi et al, 2006, Amiri et al, 2008, Samadi Alinia and Delavar, 2011, Khamespanah et al, 2016, Moradi et al 2017and Sheikhian et al 2017. Since Iranian regulations for building designs have been approved in 1966, buildings constructed before this date are considered as non-standard constructions.…”

“…Khamespanah et al (2016) have investigated Tehran physical seismic vulnerability using the integration of granular computing and rough set theory. Sheikhian et al (2017) have studied Tehran seismic vulnerability using a neural network and granular computing integrated model. Moradi et al (2016) have developed an integrated model of Choquet integral and game theory for Tehran physical vulnerability assessment.…”