Abstract Machine learning (ML) has become the most successful branch of artificial
intelligence (AI). It provides a unique opportunity to make structural engineering more …

Conventional damage detection techniques are gradually being replaced by state-of-the-art
smart monitoring and decision-making solutions. Near real-time and online damage …

Extreme learning machine (ELM) is a training algorithm for single hidden layer feedforward
neural network (SLFN), which converges much faster than traditional methods and yields …

Abstract Machine learning (ML) algorithms have been gradually used in predicting tunneling-
induced settlement, but there is no uniform process for establishing ML models and even …

This review identifies current machine-learning algorithms implemented in building
structural health monitoring systems and their success in determining the level of damage in …

Time series analysis and novelty detection are effective and promising methods for data-
driven structural health monitoring (SHM) based on the statistical pattern recognition …

In this paper, we introduce a novel hybrid model for predicting the compressive strength of
concrete using Ultrasonic Pulse Velocity (UPV) and Rebound Number (RN). First, we collect …

Concrete surface cracks are one of the primary indicators of structural deterioration; thus,
crack analysis is crucial to maintain the intact serviceability of the structural components …

It is often computationally expensive to monitor structural health using computer models.
This time-consuming process can be relieved using surrogate models, which provide cheap …

Spent nuclear fuel (SNF) assemblies (FAs) contain high-level radioactive waste from
operation of nuclear power plants (NPPs). Their safe storage in dry casks is critical for …