Artificial intelligence in the construction industry: A review of present status, opportunities and future challenges

被引:398
作者
Abioye, Sofiat O. [1 ]
Oyedele, Lukumon O. [1 ]
Akanbi, Lukman [1 ,2 ]
Ajayi, Anuoluwapo [1 ]
Delgado, Juan Manuel Davila [1 ]
Bilal, Muhammad [1 ]
Akinade, Olugbenga O. [1 ]
Ahmed, Ashraf [3 ]
机构
[1] Univ West England, Bristol Business Sch, Big Data Enterprise & Artificial Intelligence Lab, Bristol, Avon, England
[2] Obafemi Awolowo Univ, Dept Comp Sci & Engn, Ife, Nigeria
[3] Brunel Univ, Coll Engn Design & Phys Sci, Uxbridge, Middx, England
基金
英国工程与自然科学研究理事会;
关键词
Artificial intelligence; Machine learning; AI challenges; AI opportunities; Construction industry; Robotics; BIG DATA; BUILDING-MATERIALS; AUGMENTED REALITY; WASTE MANAGEMENT; ANALYTICS; PERFORMANCE; PREDICTION; FRAMEWORK; ADOPTION; MODEL;
D O I
10.1016/j.jobe.2021.103299
中图分类号
TU [建筑科学];
学科分类号
0813 ;
摘要
The growth of the construction industry is severely limited by the myriad complex challenges it faces such as cost and time overruns, health and safety, productivity and labour shortages. Also, construction industry is one the least digitized industries in the world, which has made it difficult for it to tackle the problems it currently faces. An advanced digital technology, Artificial Intelligence (AI), is currently revolutionising industries such as manufacturing, retail, and telecommunications. The subfields of AI such as machine learning, knowledge-based systems, computer vision, robotics and optimisation have successfully been applied in other industries to achieve increased profitability, efficiency, safety and security. While acknowledging the benefits of AI applications, numerous challenges which are relevant to AI still exist in the construction industry. This study aims to unravel AI applications, examine AI techniques being used and identify opportunites and challenges for AI applications in the construction industry. A critical review of available literature on AI applications in the construction industry such as activity monitoring, risk management, resource and waste optimisation was conducted. Furthermore, the opportunities and challenges of AI applications in construction were identified and presented in this study. This study provides insights into key AI applications as it applies to construction-specific challenges, as well as the pathway to realise the acrueable benefits of AI in the construction industry.
引用
收藏
页数:13
相关论文
共 140 条
[71]   Visual Data and Predictive Analytics for Proactive Project Controls on Construction Sites [J].
Lin, Jacob J. ;
Golparvar-Fard, Mani .
ADVANCED COMPUTING STRATEGIES FOR ENGINEERING, PT I, 2018, 10863 :412-430
[72]   Temporal image analytics for abnormal construction activity identification [J].
Lin, Zi-Hao ;
Chen, Albert Y. ;
Hsieh, Shang-Hsien .
AUTOMATION IN CONSTRUCTION, 2021, 124
[73]   A BIM-aided construction waste minimisation framework [J].
Liu, Zhen ;
Osniani, Mohamed ;
Demian, Peter ;
Baldwin, Andrew .
AUTOMATION IN CONSTRUCTION, 2015, 59 :1-23
[74]   Alexa vs. Siri vs. Cortana vs. Google Assistant: A Comparison of Speech-Based Natural User Interfaces [J].
Lopez, Gustavo ;
Quesada, Luis ;
Guerrero, Luis A. .
ADVANCES IN HUMAN FACTORS AND SYSTEMS INTERACTION, 2018, 592 :241-250
[75]   Integrating IoT into operational workflows for real-time and automated decision-making in repetitive construction operations [J].
Louis, Joseph ;
Dunston, Phillip S. .
AUTOMATION IN CONSTRUCTION, 2018, 94 :317-327
[76]   BIG DATA IN CONSTRUCTION WASTE MANAGEMENT: PROSPECTS AND CHALLENGES [J].
Lu, Weisheng ;
Webster, Chris ;
Peng, Yi ;
Chen, Xi ;
Chen, Ke .
DETRITUS, 2018, 4 :129-139
[77]   Development of an IoT-based big data platform for day-ahead prediction of building heating and cooling demands [J].
Luo, X. J. ;
Oyedele, Lukumon O. ;
Ajayi, Anuoluwapo O. ;
Monyei, Chukwuka G. ;
Akinade, Olugbenga O. ;
Akanbi, Lukman A. .
ADVANCED ENGINEERING INFORMATICS, 2019, 41
[78]   Recognizing Diverse Construction Activities in Site Images via Relevance Networks of Construction-Related Objects Detected by Convolutional Neural Networks [J].
Luo, Xiaochun ;
Li, Heng ;
Cao, Dongping ;
Dai, Fei ;
Seo, JoonOh ;
Lee, SangHyun .
JOURNAL OF COMPUTING IN CIVIL ENGINEERING, 2018, 32 (03)
[79]   FINDING FACTS VS BROWSING KNOWLEDGE IN HYPERTEXT SYSTEMS [J].
MARCHIONINI, G ;
SHNEIDERMAN, B .
COMPUTER, 1988, 21 (01) :70-80
[80]  
McCarthy John, 2007, What is Artificial Intelligence?