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.

The primary goal of Structural Health Monitoring (SHM) is to assess performance and determine the physical state of the structures. Technological improvements and the ubiquitous accessibility of Wi-Fi networks have enhanced real-time SHM based on the Internet of Things (IoT). Extensive structural health evaluation can be conducted using real-time test data collected from various IoT sensors on civil infrastructures. These sensors monitor multiple structural health parameters, and the data is accessible via cloud-based storage platforms. This paper presents an overview of IoT technologies and provides an extensive literature review of IoT applications for civil infrastructures, highlighting associated challenges.