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Roles of artificial intelligence in construction engineering and management: A critical review and future trends - ScienceDirect
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## Automation in Construction

# Review  Roles of artificial intelligence in construction engineering and management: A critical review and future trends

Author links open overlay panelYuePan, LimaoZhang

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## Highlights

- •
  A systematic review under both scientometric and qualitative analysis for the topic is provided.

- •
  A scientometric review is performed on 4,473 journal articles published in 1997–2020.

- •
  AI’s main benefits including modeling and pattern detection, prediction, and optimization

- •
  Special concerns have been put on six state-of-the-art applications of AI in CEM.

- •
  Key directions of future studies, like smart robotics, digital twins, and blockchains are provided.

## Abstract

With the extensive adoption of artificial intelligence (AI), construction engineering and management (CEM) is experiencing a rapid digital transformation. Since AI-based solutions in CEM has become the current research focus, it needs to be comprehensively understood. In this regard, this paper presents a systematic review under both scientometric and qualitative analysis to present the current state of AI adoption in the context of CEM and discuss its future research trends. To begin with, a scientometric review is performed to explore the characteristics of keywords, journals, and clusters based on 4,473 journal articles published in 1997–2020. It is found that there has been an explosion of relevant papers especially in the past 10 years along with the change in keyword popularity from expert systems to building information modeling (BIM), digital twins, and others. Then, a brief understanding of CEM is provided, which can be benefited from the emerging trend of AI in terms of automation, risk mitigation, high efficiency, digitalization, and computer vision. Special concerns have been put on six hot research topics that amply the advantage of AI in CEM, including (1) knowledge representation and reasoning, (2) information fusion, (3) computer vision, (4) natural language processing, (5) intelligence optimization, and (6) process mining. The goal of these topics is to model, predict, and optimize issues in a data-driven manner throughout the whole lifecycle of the actual complex project. To further narrow the gap between AI and CEM, six key directions of future researches, such as smart robotics, cloud virtual and augmented reality (cloud VR/AR), Artificial Intelligence of Things (AIoT), digital twins, 4D printing, and blockchains, are highlighted to constantly facilitate the automation and intelligence in CEM.

## Graphical abstract

Unlabelled Image

## Introduction

The construction engineering and management (CEM) inside the scope of the architecture, engineering, and construction (AEC) industry is fraught with its own problems and complications, which covers a set of construction-related activities and processes along with human factors and interactions [97]. Construction, as a large sector of the economy, plays prominent roles in driving economic growth and long-term national development [76]. According to a survey from McKinsey Global Institute in 2017, the global construction industry makes up around 13% of the world’s Gross Domestic Product (GDP) and this number is projected to rise to 15% in 2020. Meanwhile, construction projects create a broad range of job opportunities for 7% of the world’s working population. Despite its economic importance, an obvious issue is poor labor productivity during the construction procedure, negatively leading to the waste of manpower, material resources, and financial resources. Since construction activities contribute a lot to our society economically, it makes the most sense to take proper construction management for the purpose of improving product performance. If the construction productivity is enhanced by as much as 50% to 60% or higher, it is estimated to bring an additional $1.6 trillion into the industry’s value each year and further boost the global GDP [20].

Construction with inherent complexity is regarded as one of the most dangerous industries, which is greatly susceptible to a variety of unpredictable factors, such as participants in different roles, the changeable environment in large uncertainty, struck-by-equipment hazard, and others [172,199]. Therefore, the construction industry tends to cause a small scale of fatal accidents with higher frequency than other domains, which is even responsible for 30-40% of fatalities worldwide [251]. For example, accidents on construction sites have killed more than 26,000 workers in the United States during 1989 – 2013 [243]. There were in total 782 fatal construction accidents in Europe in 2014, and the rate of casualties was about 13 per 100,000 workers [213]. According to Korea’s Ministry of Employment and Labor annual report from 2012 to 2015, the mortality rate of Korea’s construction industry remained the highest among other economic sectors [98]. Construction in China has been regarded as one of the riskiest industries, where the number of fatal accidents exceeds many developed countries without a significant downward trend [234]. Numerous researches have revealed that safety issues are tied up with hazardous working conditions and the lack of supervision, emphasizing the necessity of construction management for safety guarantee and accident prevention [120]. Through identifying, evaluating, and reacting to the potential risk in dynamic and hazardous construction environments at an early stage, it is expected to eliminate safety hazards and then achieve a sustainable reduction in fatalities in the construction industry.

In the context of “Industry 4.0”, CEM is going through constant innovations towards digitalization and intelligence, in order to realize a considerable boost in automation, productivity, and reliability. That is to say, the construction industry is reshaping itself along the whole construction value chain, including the planning, construction, operation, and maintenance (O&M). For the purpose of launching the real digital strategies in CEM, artificial intelligence (AI) acts as the backbone to change the way a construction project performs. As a branch of computer science, AI drives computers to sense and learn inputs like human-being for perception, knowledge representation, reasoning, problem-solving, and planning, which can deal with complicated and ill-defined problems in an intentional, intelligent, and adaptive manner. The investment in AI is undergoing rapid growth, in which machine learning particularly accounts for a major proportion to learn sufficiently robust data from multiple sources and then act on the insights of data to make smart decisions adaptively. According to a report from Accenture company [168], AI is already altering every walk of life, which heralds dramatic potential to boost labor efficiency by 40% and double annual economic growth rates in 2035. To make AI live up to expectations, more and more companies are actively investing in various AI technologies, which put AI into a sharper focus and extend its application scope [46]. When AI talents continue to mature, it is believed that AI methods will become the next digital frontier to easily transform massive data into useful knowledge, leading to a high degree of automation and intelligence in both industry and commerce. Although a considerable amount of engineering data increases unprecedently in the construction project, the adoption of AI techniques still lags behind the process in other industries. Therefore, there is immense interest in implementing a variety of AI methods in the CEM domain to seize the valuable opportunity of digital evolution for better performance and profitability.

Due to the remarkable growth of AI applications in civil engineering, some reviews about this topic have been published. However, most of them only highlight the value of AI on a specific sub-area, such as structural engineering [175], building information modeling (BIM) [252], automated construction manufacturing [86], computer vision [66], and others. That is to say, they only offer a narrowed perspective rather than a general view of AI implementation within CEM. In the meanwhile, they largely depend on the manual review and appraisal, possibly leading to biased findings [87]. Moreover, Darko et al. [50] conducted a review on AI in the AEC industry using scientometric analysis, in order to raise the awareness of AI in AEC. But it does not provide a comprehensive introduction of AI techniques and practical AI applications in CEM. Only two future research directions, namely the robotic automation and convolutional neural networks, are identified, which are not sufficient. Yan et al. [219] reviewed the literature specifically concentrating on data mining in the construction industry. However, data mining is a subset of AI to automatically process data and retrieve useful insights. Many promising techniques beyond data mining also deserve some attention, which are capable of providing value-added services in CEM, such as the performance prediction and optimization, process mining, visual analytics, energy management, and others [22].

To tackle these existing limitations of current reviews, we aim to present a broader and more systemic review to capture the evolution and application of AI in the domain of CEM incorporating both the scientometric analysis and qualitative analysis. To sum up, the main objective of this review is to: (1) search the academic publications within the topic and perform scientometric analysis (Section 2); (2) summarize the activities and characteristics of CEM and highlight the benefits of AI in CEM (Section 3);(3) report several hot research topics of the state-of-the-art AI techniques for CEM improvement (Section 4); and (4)identify the signposts for the future researches for digitalizing CEM (Section 5).

## Section snippets

## Analysis of publications

In the beginning, relevant papers within the domain of CEM are retrieved to prepare our database for review. The following three criteria are adopted to guide the search of peer-reviewed papers: (1) Web of Science (WOS), Scopus, American Society of Civil Engineers (ASCE) Library, Wiley Online Library, and IEEE library are defined as the adopted academic databases for selecting targeted publications. (2) Selected keywords focus on the main concepts of this review, which can be simply divided

## Understanding of CEM

Through reviewing the relevant and latest publications, we summarize the related activities and characteristics of CEM to provide an in-depth understanding of the construction industry. It should be noted that large volumes of heterogeneous data are collected at every stage of the project especially with the advent of the BIM and wireless sensor network (WSN) to make CEM a data-intensive field. Hence, it is reasonable to perform various AI techniques to take full advantage of such data in a

## Research topics of AI in CEM

Various AI techniques have been developed to make machines mimic human cognitive processes in terms of learning, reasoning, and self-correcting. According to [138], the developed AI techniques are categorized into four major groups, namely the expert system, fuzzy logic, machine learning, and optimization algorithm. To be more specific, an expert system is a straightforward and understandable method towards intelligent decision making, which contains possible expert knowledge and reasoning to

## Future research trends

In the future, CEM will continue to undergo rapid digital transformation. It is evident that more and more advanced technologies inspired by AI will be implemented and spread to the entire lifecycle of the project (i.e., design, construction, O&M). As outlined in Fig. 6, we focus on six important directions to further tackle a diversity of existing issues in laborious, complex, or even dangerous tasks within the CEM domain. For one thing, the first three topics, namely smart robotics, cloud

## Conclusions

Recent decades have seen the rapid development of digital technology and the growth of big data in the old construction industry. In particular, the adoption of AI has gained significant attention, which tries to equip machines with human-like intelligent behavior and reasoning. It is found that various AI techniques have created tremendous value in revolutionizing the construction industry, leading to a more reliable, automated, self-modifying, time-saving, and cost-effective process of CEM.

## Declaration of competing interest

We wish to confirm that there are no known conflicts of interest associated with this publication and there has been no significant financial support for this work that could have influenced its outcome.

All of the sources of funding for the work described in this publication are acknowledged.

We confirm that we have given due consideration to the protection of intellectual property associated with this work and that there are no impediments to publication, including the timing of publication,

## Acknowledgment

The Ministry of Education Tier 1 Grants, Singapore (No. 04MNP000279C120; No. 04MNP002126C120) and the Start-Up Grant at Nanyang Technological University, Singapore (No. 04INS000423C120) are acknowledged for their financial support of this research.

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