(AI) in Infrastructure Projects—Gap Study

Abstract

Infrastructure projects are usually complicated, expensive, long-term mega projects; accordingly, they are the type of projects that most need optimization in the design, construction and operation stages. A great deal of earlier research was carried out to optimize the performance of infrastructure projects using traditional management techniques. Recently, artificial intelligence (AI) techniques were implemented in infrastructure projects to improve their performance and efficiency due to their ability to deal with fuzzy, incomplete, inaccurate and distorted data. The aim of this research is to collect, classify, analyze and review all of the available previous research related to implementing AI techniques in infrastructure projects to figure out the gaps in the previous studies and the recent trends in this research area. A total of 159 studies were collected since the beginning of the 1990s until the end of 2021. This database was classified based on publishing date, infrastructure subject and the used AI technique. The results of this study show that implementing AI techniques in infrastructure projects is rapidly increasing. They also indicate that transportation is the first and the most AI-using project and that both artificial neural networks (ANN) and particle swarm optimization (PSO) are the most implemented techniques in infrastructure projects. Finally, the study presented some opportunities for farther research, especially in natural gas projects.

Machine learning base models to predict the punching shear capacity of posttensioned UHPC flat slabs

“…Each model on the three developed models was based on a different approach (evolutionary approach for GP, mimicking biological neurons for ANN and optimized mathematical regression technique for EPR). These techniques were selected as they are the most suitable (AI) techniques for regression applications 32 . However, for all developed models, prediction accuracy was evaluated in terms of the Sum of Squared Errors (SSE).…”

Adaptive-Width Generalized Correntropy Diffusion Algorithm for Robust Control Strategy of Microgrid Autonomous Operation

“…[18]. Furthermore, numerous optimization approaches, such as genetic algorithms [19]- [21], chaotic map-based chameleon Swarm Algorithm [22], the Sunflower (SFO) algorithm [23], Artificial Rabbits Optimizer [24], the Cuttlefish optimization algorithm [25], the particle swarm optimization (PSO) [26]- [29], particle swarm optimizationgravitational search algorithm [30], Artificial Fish Swarm Algorithm [31], and improved Mutation particle swarm optimization [32]. Many of these approaches possess both advantages and challenges [33].…”