Seismic damage prediction of RC buildings using machine learning

Abstract

Decision‐makers and stakeholders require a rapid assessment of potential damage after earthquake events in order to develop and implement disaster risk reduction strategies and to respond systematically in post‐disaster situations. The damage investigated manually after an earthquake are complicated, labor‐intensive, time‐consuming, and error prone process. The development of fragility curves is time consuming and unable to predict the damage for wide classes of structures since it considers few structural properties and only one seismic characteristic. Furthermore, the nonlinear finite element method cannot be utilized for numerous buildings because it involves more time and money. This paper presents the machine learning (ML)‐based seismic damage prediction of RC buildings. It is found that some of the research works only considered seismic parameters or structural parameters to train the ML models and predict the structural damage assessment. However, these ML models may not fully reveal the underlying complexity of the relationship between input parameters and building performance. As a result, their applicability will be limited. This paper evaluates the feasibility of using ML techniques such as K‐nearest neighbor, random forest, decision tree, support vector machine, and artificial neural network to rapidly predict earthquake‐induced reinforced concrete building damage considering both the structural properties and ground motion characteristics. The machine learning models are trained using the simulation results. Due to lack of real earthquake damage datasets or limited access, most of the research works used Scikit Learn train_test_split function to randomly split the entire datasets into training and testing datasets and the performance of the proposed ML technique are evaluated using the testing datasets. However, in this study, the performances of different ML models are evaluated using real earthquake damage datasets of RC buildings collected after 2015 Nepal earthquake. The overall accuracy on testing datasets suggests the capability of machine learning algorithms in successfully predicting the seismic damage of reinforced concrete buildings in quick time with reasonable accuracy. This study is beneficial in emergency response and recovery planning after an earthquake.

A Three-Dimensional Seismic Damage Assessment Method for RC Structures Based on Multi-Mode Damage Model

“…However, among these seismic damage models, seldom do they considered three-dimensional seismic damage, especially for torsional damage. The assessment of reinforced concrete buildings for three-dimensional seismic damage is a challenging structural problem, and it is also a key issue for disaster mitigation and resilience [1,2]. Previous studies have recognized the importance of considering torsional damage in three-dimensional structures, but few have established quantitative evaluation models specifically for torsional damage.…”

Multiclass seismic damage detection of buildings using quantum convolutional neural network

“…With the rapid enhancement in computational capability in recent times, the use of artificial intelligence (AI) has increased significantly for rapid inspection and damage assessment of structures (Bhatta & Dang, 2023; Rafiei & Adeli, 2017b). Deep learning (DL)‐based convolutional neural network (CNN) has gained significant attention across all disciplines due to its improved detection accuracy and speed.…”

Multiple Unmanned Aerial Vehicle Autonomous Path Planning Algorithm Based on Whale-Inspired Deep Q-Network

“…Due to the continuous development of multi-sensor data fusion technology and autonomous flight control technology, Unmanned Aerial Vehicles (UAVs) have become an important tool in the field of emergency rescue. Particularly, in the face of natural disasters and emergencies, UAVs are widely used in search and rescue operations [1], for example, the 9.0-magnitude earthquake in Japan in 2011 [2], the 8.1-magnitude earthquake in Nepal in 2015 [3], and Hurricane Harvey in Texas in 2017 [4], which caused massive damage and casualties. UAVs were used to assist rescue teams to provide necessary rescue services for trapped people in danger.…”