A Comparative Study of MCDM Methods Integrated with Rapid Visual Seismic Vulnerability Assessment of Existing RC Structures

Abstract

Recently, the demand for residence and usage of urban infrastructure has been increased, thereby resulting in the elevation of risk levels of human lives over natural calamities. The occupancy demand has rapidly increased the construction rate, whereas the inadequate design of structures prone to more vulnerability. Buildings constructed before the development of seismic codes have an additional susceptibility to earthquake vibrations. The structural collapse causes an economic loss as well as setbacks for human lives. An application of different theoretical methods to analyze the structural behavior is expensive and time-consuming. Therefore, introducing a rapid vulnerability assessment method to check structural performances is necessary for future developments. The process, as mentioned earlier, is known as Rapid Visual Screening (RVS). This technique has been generated to identify, inventory, and screen structures that are potentially hazardous. Sometimes, poor construction quality does not provide some of the required parameters; in this case, the RVS process turns into a tedious scenario. Hence, to tackle such a situation, multiple-criteria decision-making (MCDM) methods for the seismic vulnerability assessment opens a new gateway. The different parameters required by RVS can be taken in MCDM. MCDM evaluates multiple conflicting criteria in decision making in several fields. This paper has aimed to bridge the gap between RVS and MCDM. Furthermore, to define the correlation between these techniques, implementation of the methodologies from Indian, Turkish, and Federal Emergency Management Agency (FEMA) codes has been done. The effects of seismic vulnerability of structures have been observed and compared.

A Machine Learning Framework for Assessing Seismic Hazard Safety of Reinforced Concrete Buildings

“…As macro-averaging is the average of model performance for each class, Ecuador shows the least macro-average percentage (76%) with an efficiency of only 53% in classifying test samples belonging to the class 3. The proposed method results show a significant improvement in RVS methods such as RVS based on multi-criteria decision-making [19] or Multi-Layer Perceptron [13] where the accuracies were around 37% and 52%, respectively. Additionally, the area under the curve (AUC) is also used for model performance evaluation as a summarized intelligence model.…”

“…A substantial literature has been published in the attempts of integrating several methods from various domains with RVS, for instance, statistical methods [11,12], Artificial Neural Network (ANN) [13][14][15][16][17], multi-criteria decision making [18,19], and type-1 [20][21][22][23] and type-2 [24,25] fuzzy logic systems are frequently assimilated within RVS for increasing the interface and efficacy of seismic vulnerability screening. However, there are methods developed to evaluate the damage and change detection of buildings by remote sensing and image analysis [26,27].…”

Uptake and Dissemination of Multi-Criteria Decision Support Methods in Civil Engineering—Lessons from the Literature

“…While a wide range of applications were covered in the articles, as described below, the most cited application areas were (a) the choice of methods for retrofitting buildings or bridges to improve their earthquake resilience, (b) choosing strategies for restoring transportation networks after earthquakes and (c) seismic risk mapping, mainly for structures in urban areas. A recent article reviewed and compared some MCDA methods applied to the seismic vulnerability of reinforced concrete buildings in Turkey [57]. By comparing the MCDM methods results with actual damaged experienced by the buildings, the study recommended TOPSIS.…”

A Synthesized Study Based on Machine Learning Approaches for Rapid Classifying Earthquake Damage Grades to RC Buildings

“…Yang and Goettel [4] have developed an augmented approach to RVS called E-EVS by using the RVS scores for the initial prioritization of public educational buildings in Oregon, USA. However, the RVS method has been developed and used in different countries like Turkey, Greece, Canada, Japan, New Zealand, and India with local parametric variations depending on the construction method, structural materials used, the structural design philosophies, and the seismic hazard zones [5].…”