Artificial intelligence in infrastructure construction: A critical review

Abstract

Artificial intelligence (AI) has emerged as a promising technological solution for addressing critical infrastructure construction challenges, such as elevated accident rates, suboptimal productivity, and persistent labor shortages. This review aims to thoroughly analyze the contemporary landscape of AI applications in the infrastructure construction sector. We conducted both quantitative and qualitative analyses based on 594 and 91 selected papers, respectively. The results reveal that the primary focus of current AI research in this field centers on safety monitoring and control, as well as process management. Key technologies such as machine learning, computer vision, and natural language processing are prominent, with significant attention given to the development of smart construction sites. Our review also highlights several areas for future research, including broadening the scope of AI applications, exploring the potential of diverse AI technologies, and improving AI applications through standardized data sets and generative AI models. These directions are promising for further advancements in infrastructure construction, offering potential solutions to its significant challenges.

Graphical abstract

Keywords

infrastructure construction / artificial intelligence / literature review / quantitative analysis / qualitative analysis

References

[1] Abioye S O, Oyedele L O, Akanbi L, Ajayi A, Davila Delgado J M, Bilal M, Akinade O O, Ahmed A, (2021). Artificial intelligence in the construction industry: A review of present status, opportunities and future challenges. Journal of Building Engineering, 44: 103299

[2] Ajayi A, Oyedele L, Owolabi H, Akinade O, Bilal M, Davila Delgado J M, Akanbi L, (2020). Deep learning models for health and safety risk prediction in power infrastructure projects. Risk Analysis, 40( 10): 2019–2039

[3] Akanbi T, Zhang J, (2021). Design information extraction from construction specifications to support cost estimation. Automation in Construction, 131: 103835

[4] Alshboul O, Alzubaidi M A, Mamlook R E A, Almasabha G, Almuflih A S, Shehadeh A, (2022). Forecasting liquidated damages via machine learning-based modified regression models for highway construction projects. Sustainability, 14( 10): 5835

[5] Angah O, Chen A Y, (2020). Tracking multiple construction workers through deep learning and the gradient based method with re-matching based on multi-object tracking accuracy. Automation in Construction, 119: 103308

[6] Ashrafian A, Amiri M J T, Masoumi P, Asadi-shiadeh M, Yaghoubi-chenari M, Mosavi A, Nabipour N, (2020). Classification-based regression models for prediction of the mechanical properties of roller-compacted concrete pavement. Applied Sciences, 10: 3707

[7] Awada M, Srour F J, Srour I M, (2021). Data-driven machine learning approach to integrate field submittals in project scheduling. Journal of Management Engineering, 37( 1): 04020104

[8] Azar E R, (2016). Construction equipment identification using marker-based recognition and an active zoom camera. Journal of Computing in Civil Engineering, 30( 3): 04015033

[9] Bhokare S, Goyal L, Ren R, Zhang J, (2022). Smart construction scheduling monitoring using YOLOv3-based activity detection and classification. Journal of Information Technology in Construction, 27: 240–252

[10] Bügler M, Borrmann A, Ogunmakin G, Vela P A, Teizer J, (2017). Fusion of photogrammetry and video analysis for productivity assessment of earthwork processes. Computer-Aided Civil and Infrastructure Engineering, 32( 2): 107–123

[11] Cai S, Ma Z, Skibniewski M J, Bao S, (2019). Construction automation and robotics for high-rise buildings over the past decades: A comprehensive review. Advanced Engineering Informatics, 42: 100989

[12] Chen H P, Ying K C, (2022). Artificial intelligence in the construction industry: Main development trajectories and future outlook. Applied Sciences, 12( 12): 5832

[13] Chen S, Demachi K, (2021). Towards on-site hazards identification of improper use of personal protective equipment using deep learning-based geometric relationships and hierarchical scene graph. Automation in Construction, 125: 103619

[14] Cheng C F, Rashidi A, Davenport M A, Anderson D V, (2017). Activity analysis of construction equipment using audio signals and support vector machines. Automation in Construction, 81: 240–253

[15] Cheng J P, Wong P K Y, Luo H, Wang M, Leung P H, (2022). Vision-based monitoring of site safety compliance based on worker re-identification and personal protective equipment classification. Automation in Construction, 139: 104312

[16] Cheng M Y, Chang N W, (2019). Dynamic construction material layout planning optimization model by integrating 4D BIM. Engineering with Computers, 35( 2): 703–720

[17] Cheng M Y, Chang Y H, Korir D, (2019). Novel approach to estimating schedule to completion in construction projects using sequence and nonsequence learning. Journal of Construction Engineering and Management, 145( 11): 04019072

[18] Cheng M Y, Hoang N D, (2014). Interval estimation of construction cost at completion using least squares support vector machine. Journal of Civil Engineering and Management, 20( 2): 223–236

[19] Cheng M Y, Tsai H C, Liu C L, (2009). Artificial intelligence approaches to achieve strategic control over project cash flows. Automation in Construction, 18( 4): 386–393

[20] Choo H, Lee B, Kim H, Choi B, (2023). Automated detection of construction work at heights and deployment of safety hooks using IMU with a barometer. Automation in Construction, 147: 104714

[21] Cui X, Wang Q, Dai J, Zhang R, Li S, (2021). Intelligent recognition of erosion damage to concrete based on improved YOLO-v3. Materials Letters, 302: 130363

[22] Darko A, Chan A P, Adabre M A, Edwards D J, Hosseini M R, Ameyaw E E, (2020). Artificial intelligence in the AEC industry: Scientometric analysis and visualization of research activities. Automation in Construction, 112: 103081

[23] Delhi V S K, Sankarlal R, Thomas A, (2020). Detection of personal protective equipment (PPE) compliance on construction site using computer vision based deep learning techniques. Frontiers in Built Environment, 6: 136

[24] DiligentiMRoychowdhurySGoriM (2017). Integrating prior knowledge into deep learning. In: Proceedings of the 16th IEEE international conference on machine learning and applications (ICMLA). Cancun: IEEE, 920–923

[25] Dimitrov A, Golparvar-Fard M, (2014). Vision-based material recognition for automated monitoring of construction progress and generating building information modeling from unordered site image collections. Advanced Engineering Informatics, 28( 1): 37–49

[26] Ding L, Fang W, Luo H, Love P E, Zhong B, Ouyang X, (2018). A deep hybrid learning model to detect unsafe behavior: Integrating convolution neural networks and long short-term memory. Automation in Construction, 86: 118–124

[27] DumrakJZarghamiS A (2023). The role of artificial intelligence in lean construction management. Engineering, Construction, and Architectural Management, in press

[28] Dung C V, Anh L D, (2019). Autonomous concrete crack detection using deep fully convolutional neural network. Automation in Construction, 99: 52–58

[29] Ezzeddine A, Shehab L, Lucko G, Hamzeh F, (2022). Forecasting construction project performance with momentum using singularity functions in LPS. Journal of Construction Engineering and Management, 148( 8): 04022063

[30] Fang W, Ding L, Luo H, Love P E, (2018a). Falls from heights: A computer vision-based approach for safety harness detection. Automation in Construction, 91: 53–61

[31] Fang W, Ding L, Zhong B, Love P E, Luo H, (2018b). Automated detection of workers and heavy equipment on construction sites: A convolutional neural network approach. Advanced Engineering Informatics, 37: 139–149

[32] Fang W, Zhong B, Zhao N, Love P E, Luo H, Xue J, Xu S, (2019). A deep learning-based approach for mitigating falls from height with computer vision: Convolutional neural network. Advanced Engineering Informatics, 39: 170–177

[33] Golparvar-Fard M, Pena-Mora F, Savarese S, (2015). Automated progress monitoring using unordered daily construction photographs and IFC-based building information models. Journal of Computing in Civil Engineering, 29( 1): 04014025

[34] Gondia A, Siam A, El-Dakhakhni W, Nassar A H, (2020). Machine learning algorithms for construction projects delay risk prediction. Journal of Construction Engineering and Management, 146( 1): 04019085

[35] Guo D, Onstein E, Rosa A D L, (2021). A semantic approach for automated rule compliance checking in construction industry. IEEE Access: Practical Innovations, Open Solutions, 9: 129648–129660

[36] Guo H, Zhang Z, Yu R, Sun Y, Li H, (2023). Action recognition based on 3D skeleton and LSTM for the monitoring of construction workers’ safety harness usage. Journal of Construction Engineering and Management, 149( 4): 04023015

[37] GuptaH DShengV S (2020). A roadmap to domain knowledge integration in machine learning. In: Proceedings of the 2020 IEEE International Conference on Knowledge Graph (ICKG). Nanjing: IEEE, 145–151

[38] Huang L, Fu Q, He M, Jiang D, Hao Z, (2021). Detection algorithm of safety helmet wearing based on deep learning. Concurrency and Computation: Practice and Experience, 33( 13): e6234

[39] Hussein M, Zayed T, (2021). Crane operations and planning in modular integrated construction: Mixed review of literature. Automation in Construction, 122: 103466

[40] Jiang H, Qiang M, Lin P, (2016). Assessment of online public opinions on large infrastructure projects: A case study of the Three Gorges Project in China. Environmental Impact Assessment Review, 61: 38–51

[41] Juszczyk M, Zima K, Lelek W, (2019). Forecasting of sports fields construction costs aided by ensembles of neural networks. Journal of Civil Engineering and Management, 25( 7): 715–729

[42] Kaplan A, Haenlein M, (2019). Siri, Siri, in my hand: Who’s the fairest in the land? On the interpretations, illustrations, and implications of artificial intelligence. Business Horizons, 62( 1): 15–25

[43] Karakas K, Dikmen I, Birgonul M T, (2013). Multiagent system to simulate risk-allocation and cost-sharing processes in construction projects. Journal of Computing in Civil Engineering, 27( 3): 307–319

[44] Kardovskyi Y, Moon S, (2021). Artificial intelligence quality inspection of steel bars installation by integrating mask R-CNN and stereo vision. Automation in Construction, 130: 103850

[45] Kim H, Ham Y, Kim W, Park S, Kim H, (2019). Vision-based nonintrusive context documentation for earthmoving productivity simulation. Automation in Construction, 102: 135–147

[46] Kim H, Kim K, Kim H, (2016). Vision-based object-centric safety assessment using fuzzy inference: Monitoring struck-by accidents with moving objects. Journal of Computing in Civil Engineering, 30( 4): 04015075

[47] Kim J, Chi S, (2019). Action recognition of earthmoving excavators based on sequential pattern analysis of visual features and operation cycles. Automation in Construction, 104: 255–264

[48] Kim K, Cho Y K, (2020). Effective inertial sensor quantity and locations on a body for deep learning-based worker’s motion recognition. Automation in Construction, 113: 103126

[49] Kolar Z, Chen H, Luo X, (2018). Transfer learning and deep convolutional neural networks for safety guardrail detection in 2D images. Automation in Construction, 89: 58–70

[50] Lei L, Zhou Y, Luo H, Love P E, (2019). A CNN-based 3D patch registration approach for integrating sequential models in support of progress monitoring. Advanced Engineering Informatics, 41: 100923

[51] Li S, Cai H, Kamat V R, (2016). Integrating natural language processing and spatial reasoning for utility compliance checking. Journal of Construction Engineering and Management, 142( 12): 04016074

[52] Lien L C, Cheng M Y, (2014). Particle bee algorithm for tower crane layout with material quantity supply and demand optimization. Automation in Construction, 45: 25–32

[53] Luo H, Wang M, Wong P K Y, Cheng J C, (2020). Full body pose estimation of construction equipment using computer vision and deep learning techniques. Automation in Construction, 110: 103016

[54] Luo H, Xiong C, Fang W, Love P E, Zhang B, Ouyang X, (2018a). Convolutional neural networks: Computer vision-based workforce activity assessment in construction. Automation in Construction, 94: 282–289

[55] Luo X, Li H, Cao D, Yu Y, Yang X, Huang T, (2018b). Towards efficient and objective work sampling: Recognizing workers’ activities in site surveillance videos with two-stream convolutional networks. Automation in Construction, 94: 360–370

[56] Memarzadeh M, Golparvar-Fard M, Niebles J C, (2013). Automated 2D detection of construction equipment and workers from site video streams using histograms of oriented gradients and colors. Automation in Construction, 32: 24–37

[57] Mir M, Kabir H D, Nasirzadeh F, Khosravi A, (2021). Neural network-based interval forecasting of construction material prices. Journal of Building Engineering, 39: 102288

[58] Momade M H, Shahid S, Hainin M R B, Nashwan M S, Tahir Umar A, (2022). Modelling labour productivity using SVM and RF: A comparative study on classifiers performance. International Journal of Construction Management, 22( 10): 1924–1934

[59] Mostafavi A, Cha Y J, (2023). Deep learning-based active noise control on construction sites. Automation in Construction, 151: 104885

[60] Pan Y, Zhang L, (2021). Roles of artificial intelligence in construction engineering and management: A critical review and future trends. Automation in Construction, 122: 103517

[61] Piao Y, Xu W, Wang T K, Chen J H, (2021). Dynamic fall risk assessment framework for construction workers based on dynamic Bayesian network and computer vision. Journal of Construction Engineering and Management, 147( 12): 04021171

[62] Podolski M, (2016). Management of resources in multiunit construction projects with the use of a tabu search algorithm. Journal of Civil Engineering and Management, 23( 2): 263–272

[63] Poh C Q, Ubeynarayana C U, Goh Y M, (2018). Safety leading indicators for construction sites: A machine learning approach. Automation in Construction, 93: 375–386

[64] Prieto S A, Mengiste E T, García de Soto B, (2023). Investigating the use of ChatGPT for the scheduling of construction projects. Buildings, 13( 4): 857

[65] Rashid K M, Louis J, (2019). Times-series data augmentation and deep learning for construction equipment activity recognition. Advanced Engineering Informatics, 42: 100944

[66] Ren R, Zhang J, (2021). Semantic rule-based construction procedural information extraction to guide jobsite sensing and monitoring. Journal of Computing in Civil Engineering, 35( 6): 04021026

[67] Rodríguez-López M E, Alcántara-Pilar J M, Del Barrio-García S, Muñoz-Leiva F, (2020). A review of restaurant research in the last two decades: A bibliometric analysis. International Journal of Hospitality Management, 87: 102387

[68] Ryu J, Seo J, Jebelli H, Lee S, (2019). Automated action recognition using an accelerometer-embedded wristband-type activity tracker. Journal of Construction Engineering and Management, 145( 1): 04018114

[69] Sabillon C, Rashidi A, Samanta B, Davenport M A, Anderson D V, (2020). Audio-based Bayesian model for productivity estimation of cyclic construction activities. Journal of Computing in Civil Engineering, 34( 1): 04019048

[70] Shen Q, Wu S, Deng Y, Deng H, Cheng J C, (2022). BIM-based dynamic construction safety rule checking using ontology and natural language processing. Buildings, 12( 5): 564

[71] Soltani M M, Zhu Z, Hammad A, (2017). Skeleton estimation of excavator by detecting its parts. Automation in Construction, 82: 1–15

[72] Soltani M M, Zhu Z, Hammad A, (2018). Framework for location data fusion and pose estimation of excavators using stereo vision. Journal of Computing in Civil Engineering, 32( 6): 04018045

[73] Son H, Choi H, Seong H, Kim C, (2019). Detection of construction workers under varying poses and changing background in image sequences via very deep residual networks. Automation in Construction, 99: 27–38

[74] StewartRErmonS (2017). Label-free supervision of neural networks with physics and domain knowledge. In: Proceedings of the 31th AAAI Conference on Artificial Intelligence. San Francisco: AAAI, 31(1)

[75] Wang Y, Huang Y, Gu B, Cao S, Fang D, (2023). Identifying mental fatigue of construction workers using EEG and deep learning. Automation in Construction, 151: 104887

[76] Wang Z, Wu Y, Yang L, Thirunavukarasu A, Evison C, Zhao Y, (2021a). Fast personal protective equipment detection for real construction sites using deep learning approaches. Sensors, 21( 10): 3478

[77] Wang Z, Zhang Q, Yang B, Wu T, Lei K, Zhang B, Fang T, (2021b). Vision-based framework for automatic progress monitoring of precast walls by using surveillance videos during the construction phase. Journal of Computing in Civil Engineering, 35( 1): 04020056

[78] Wuni I Y, Shen G Q, Osei-Kyei R, (2019). Scientometric review of global research trends on green buildings in construction journals from 1992 to 2018. Energy and Building, 190: 69–85

[79] Xiao B, Kang S C, (2021). Vision-based method integrating deep learning detection for tracking multiple construction machines. Journal of Computing in Civil Engineering, 35( 2): 04020071

[80] Yao G, Sun Y, Wong M, Lv X, (2021). A real-time detection method for concrete surface cracks based on improved YOLOv4. Symmetry, 13( 9): 1716

[81] You H, Ye Y, Zhou T, Zhu Q, Du J, (2023). Robot-enabled construction assembly with automated sequence planning based on ChatGPT: RoboGPT. Buildings, 13( 7): 1772

[82] Yu Y, Yang X, Li H, Luo X, Guo H, Fang Q, (2019). Joint-level vision-based ergonomic assessment tool for construction workers. Journal of Construction Engineering and Management, 145( 5): 04019025

[83] Yuan L, Guo J, Wang Q, (2020). Automatic classification of common building materials from 3D terrestrial laser scan data. Automation in Construction, 110: 103017

[84] Zermane A, Mohd Tohir M Z, Zermane H, Baharudin M R, Mohamed Yusoff H, (2023). Predicting fatal fall from heights accidents using random forest classification machine learning model. Safety Science, 159: 106023

[85] Zhang C, Lu Y, (2021). Study on artificial intelligence: The state of the art and future prospects. Journal of Industrial Information Integration, 23: 100224

[86] Zhang J, El-Gohary N M, (2015). Automated information transformation for automated regulatory compliance checking in construction. Journal of Computing in Civil Engineering, 29( 4): B4015001

[87] Zhang J, El-Gohary N M, (2017). Integrating semantic NLP and logic reasoning into a unified system for fully-automated code checking. Automation in Construction, 73: 45–57

[88] Zhao J, Obonyo E, (2020). Convolutional long short-term memory model for recognizing construction workers’ postures from wearable inertial measurement units. Advanced Engineering Informatics, 46: 101177

[89] Zhou C, Xu H, Ding L, Wei L, Zhou Y, (2019). Dynamic prediction for attitude and position in shield tunneling: A deep learning method. Automation in Construction, 105: 102840

[90] Zhou X, Liao P C, (2023). Weighing votes in human–machine collaboration for hazard recognition: inferring a hazard-based perceptual threshold and decision confidence from electroencephalogram wavelets. Journal of Construction Engineering and Management, 149( 9): 04023084

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The Author(s). This article is published with open access at link.springer.com and journal.hep.com.cn