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

Abstract

Although averting a seismic disturbance and its physical, social, and economic disruption is practically impossible, using the advancements in computational science and numerical modeling shall equip humanity to predict its severity, understand the outcomes, and equip for post-disaster management. Many buildings exist amidst the developed metropolitan areas, which are senile and still in service. These buildings were also designed before establishing national seismic codes or without the introduction of construction regulations. In that case, risk reduction is significant for developing alternatives and designing suitable models to enhance the existing structure’s performance. Such models will be able to classify risks and casualties related to possible earthquakes through emergency preparation. Thus, it is crucial to recognize structures that are susceptible to earthquake vibrations and need to be prioritized for retrofitting. However, each building’s behavior under seismic actions cannot be studied through performing structural analysis, as it might be unrealistic because of the rigorous computations, long period, and substantial expenditure. Therefore, it calls for a simple, reliable, and accurate process known as Rapid Visual Screening (RVS), which serves as a primary screening platform, including an optimum number of seismic parameters and predetermined performance damage conditions for structures. In this study, the damage classification technique was studied, and the efficacy of the Machine Learning (ML) method in damage prediction via a Support Vector Machine (SVM) model was explored. The ML model is trained and tested separately on damage data from four different earthquakes, namely Ecuador, Haiti, Nepal, and South Korea. Each dataset consists of varying numbers of input data and eight performance modifiers. Based on the study and the results, the ML model using SVM classifies the given input data into the belonging classes and accomplishes the performance on hazard safety evaluation of buildings.

Transferability of Convolutional Neural Network Models for Identifying Damaged Buildings Due to Earthquake

“…However, the traditional classification methods usually need to establish a classification rule set [31], but human subjectivity has a great influence on the establishment of rule sets, and the selection of a large number of parameters is also time-consuming. Shallow machine learning methods such as Random Forest (RF) [36] and Support Vector Machine (SVM) [37] can achieve relatively high accuracy without setting a large number of parameters, so lots of attempts have been conducted in earthquake damage assessment [32][33][34][35][38][39][40][41]. However, the extraction of manual features from VHR images is time-consuming and requires a high level of prior knowledge.…”

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

“…Extensive research has been published to integrate scientific methods from several domains to blend them into the scope of RVS and propose a smarter resultant approach; for instance, statistical methods [12,13], ANN and ML techniques [14][15][16][17][18][19][20], multi-criteria decision-making [5,21], deep learning classification [22], and type-1 [23][24][25][26] and type-2 [27,28] fuzzy logic systems. Within the framework of statistical methodology and regression analysis, multiple linear regression analysis [12] is the most commonly used technique for damage state classification in the RVS domain [2], preceded by other approaches such as discriminant analysis proposed in [29].…”

Modeling of Compressive Strength of Self-Compacting Rubberized Concrete Using Machine Learning

“…Therefore, in this article, an effort was made to model the compressive strength of SCRC by adopting one of several machine learning methods. So far, metaheuristic methods, and especially neural networks, have been successfully applied in various fields, such as in the control and optimization of processes, economics, medicine, and engineering [ 6 , 7 , 8 , 9 , 10 ]. They have also been used to model the properties of concrete in fresh or solid state [ 11 , 12 , 13 , 14 , 15 ], but much less in concrete with the addition of rubber [ 16 , 17 , 18 ].…”