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

This paper performed a systematic review of the various machine learning (ML) techniques
applied to assess the health condition of heritage buildings. More robust predictive models …

The artificial neural network (ANN) approach motivated by the biological nervous system is
an inspiring mathematical tool that simulates many complicated engineering applications …

Determining the risk priorities for the building stock in highly seismic-prone regions and
making the final decisions about the buildings is one of the essential precautionary …

Current building codes and design standards classify different structural components
according to their expected structural performance. Such classification is usually based on …

Pipelines are like a lifeline that is vital to a nation's economic sustainability; as such,
pipelines need to be monitored to optimize their performance as well as reduce the product …

This research study uses Artificial Neural Networks (ANNs) to predict occupational accidents
in Sivakasi firework industries. Atmospheric temperature, pressure and humidity are the …

The type of horizontal reinforcement can play a major role in the shear response of partially
grouted (PG) masonry walls, particularly in the damage pattern and the post-peak behavior …

This article describes the experimental results of ten partially grouted reinforced concrete
masonry shear walls (PG-RCMSW) that were subjected to reverse lateral in-plane cyclic …

Connections are crucial zones in steel buildings since they provide interaction between
principal structural components (ie, beams, columns) and provide stability to the entire …