Artificial intelligence (AI) is proving to be an efficient alternative approach to classical modeling techniques. AI refers to the branch of computer science that develops machines and software with human-like intelligence. Compared to traditional methods, AI offers advantages to deal with problems associated with uncertainties and is an effective aid to solve such complex problems. In addition, AI-based solutions are good alternatives to determine engineering design parameters when testing is not possible, thus resulting in significant savings in terms of human time and effort spent in experiments. AI is also able to make the process of decision making faster, decrease error rates, and increase computational efficiency. Among the different AI techniques, machine learning (ML), pattern recognition (PR), and deep learning (DL) have recently acquired considerable attention and are establishing themselves as a new class of intelligent methods for use in structural engineering. The objective of this review paper is to summarize techniques concerning applications of the noted AI methods in structural engineering developed over the last decade. First, a general introduction to AI is presented and the importance of AI in structural engineering is described. Thereafter, a review of recent applications of ML, PR, and DL in the field is provided, and the capability of such methods to address the restrictions of conventional models are discussed. Further, the advantages of employing such algorithmic methods are discussed in detail. Finally, potential research avenues and emerging trends for employing ML, PR, and DL are presented, and their limitations are discussed.

Introduction

Civil engineering is fraught with problems that defy solution via traditional computational techniques. However, they can often be solved by an expert with proper training. Classical artificial intelligence (AI) has targeted this class of problems by capturing the essence of human cognition at the highest level. The term “AI” was introduced at a workshop held in Dartmouth college in 1956 [1]. AI is a computational method attempting to simulate human cognition capability through symbol manipulation and symbolically structured knowledge bases to solve engineering problems that defy solution using conventional methods. AI has been developed based on the interaction of various disciplines; namely, computer science, information theory, cybernetics, linguistic, and neurophysiology. Several terms referring to artificial intelligence can be found in the literature, and they need to be identified to further elaborate on the state of the art. One of those terms is machine intelligence (MI). AI and MI are almost identical terms [2,3] and are often used interchangeably. MI is often considered a synonym of AI; yet it deals with different types of intelligent problems, e.g., clustering, classifications, computer vision, etc. In general, MI refers to machines with human-like intelligent behavior and reasoning, while AI refers to a machine’s ability to mimic the cognitive functions of humans to perform tasks in a smart manner. Another important term is cognitive computing (CC), which is inspired by human mind’s capabilities [4]. Cognitive systems are able to solve problems in a form mimicking humans thinking and reasoning. Such systems are based on the ability of machines to measure, reason, and adapt using learned experience [4,5]. The main characteristics of CC systems are their ability to interpret big data, dynamic training and adaptive learning, probabilistic discovery of relevant patterns. Technically, AI refers to computers and machines that can behave intelligently, while CC concentrates on solving the problems using humanlike thinking. The most significant difference between AI and CC can be defined in terms of interacting normally with humans. For any AI system, there is an agent that decides what actions need to be taken.