Artificial intelligence in the AEC industry: Scientometric analysis and visualization of research activities

Abstract The Architecture, Engineering and Construction (AEC) industry is fraught with complex and difficult problems. Artificial intelligence (AI) represents a powerful tool to assist in addressing these problems. Therefore, over the years, researchers have been conducting research on AI in the AEC industry (AI-in-the-AECI). In this paper, the first comprehensive scientometric study appraising the state-of-the-art of research on AI-in-the-AECI is presented. The science mapping method was used to systematically and quantitatively analyze 41,827 related bibliographic records retrieved from Scopus. The results indicated that genetic algorithms, neural networks, fuzzy logic, fuzzy sets, and machine learning have been the most widely used AI methods in AEC. Optimization, simulation, uncertainty, project management, and bridges have been the most commonly addressed topics/issues using AI methods/concepts. The primary value and uniqueness of this study lies in it being the first in providing an up-to-date inclusive, big picture of the literature on AI-in-the-AECI. This study adds value to the AEC literature through visualizing and understanding trends and patterns, identifying main research interests, journals, institutions, and countries, and how these are linked within now-available studies on AI-in-the-AECI. The findings bring to light the deficiencies in the current research and provide paths for future research, where they indicated that future research opportunities lie in applying robotic automation and convolutional neural networks to AEC problems. For the world of practice, the study offers a readily-available point of reference for practitioners, policy makers, and research and development (R&D) bodies. This study therefore raises the level of awareness of AI and facilitates building the intellectual wealth of the AI area in the AEC industry.

[1]  Zahir Irani,et al.  Intelligent Systems Research in the Construction Industry , 2014, Expert Syst. Appl..

[2]  Hamed Kashani,et al.  Forecasting Construction Material Prices Using Vector Error Correction Model , 2018, Journal of Construction Engineering and Management.

[3]  Jianhua Hou,et al.  The structure and dynamics of cocitation clusters: A multiple-perspective cocitation analysis , 2010 .

[4]  Ting Liu,et al.  Recent advances in convolutional neural networks , 2015, Pattern Recognit..

[5]  Alexander Serenko,et al.  The Development of an AI Journal Ranking List Based on the Revealed Preference Approach , 2010, AMCIS.

[6]  E. Zavadskas,et al.  Critical evaluation of off-site construction research: a scientometric analysis , 2018 .

[7]  ChaYoung-Jin,et al.  Deep Learning-Based Crack Damage Detection Using Convolutional Neural Networks , 2017 .

[8]  Geoffrey E. Hinton,et al.  Deep Learning , 2015, Nature.

[9]  Enrique López Droguett,et al.  Convolutional neural networks for automated damage recognition and damage type identification , 2018, Structural Control and Health Monitoring.

[10]  Ming Yang,et al.  3D Convolutional Neural Networks for Human Action Recognition , 2010, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[11]  Haipeng Shen,et al.  Artificial intelligence in healthcare: past, present and future , 2017, Stroke and Vascular Neurology.

[12]  Wei Li,et al.  Detection of sealed and unsealed cracks with complex backgrounds using deep convolutional neural network , 2019, Automation in Construction.

[13]  Huchang Liao,et al.  Visualization and quantitative research on intuitionistic fuzzy studies , 2016, J. Intell. Fuzzy Syst..

[14]  Mohamed Al-Hussein,et al.  Building information modelling for off-site construction: Review and future directions , 2019, Automation in Construction.

[15]  Yang Yang,et al.  Construction Accidents in a Large-Scale Public Infrastructure Project: Severity and Prevention , 2018, Journal of Construction Engineering and Management.

[16]  Antonio Grilo,et al.  Bibliometric analysis and review of Building Information Modelling literature published between 2005 and 2015 , 2017 .

[17]  Tarek Hegazy,et al.  Neural networks as tools in construction , 1991 .

[18]  Yi Chen,et al.  Building material prices forecasting based on least square support vector machine and improved particle swarm optimization , 2018, Architectural Engineering and Design Management.

[19]  Mohsen Khatibinia,et al.  Probabilistic Performance-Based Optimum Seismic Design of RC Structures Considering Soil–Structure Interaction Effects , 2017 .

[20]  Chaomei Chen,et al.  Science Mapping: A Systematic Review of the Literature , 2017, J. Data Inf. Sci..

[21]  Ioannis Brilakis,et al.  Neurofuzzy Genetic System for Selection of Construction Project Managers , 2011 .

[22]  Lee Margetts,et al.  Use of gaming technology to bring bridge inspection to the office , 2019, Structure and Infrastructure Engineering.

[23]  Peter E.D. Love,et al.  Falls from heights: A computer vision-based approach for safety harness detection , 2018, Automation in Construction.

[24]  Yi Yang,et al.  Fostering linguistic decision-making under uncertainty: A proportional interval type-2 hesitant fuzzy TOPSIS approach based on Hamacher aggregation operators and andness optimization models , 2019, Inf. Sci..

[25]  Mehrdad Arashpour,et al.  Analysis of citation networks in building information modeling research , 2018 .

[26]  Francisco Herrera,et al.  Science mapping software tools: Review, analysis, and cooperative study among tools , 2011, J. Assoc. Inf. Sci. Technol..

[27]  Tarek Zayed,et al.  MOSCOPEA: Multi-objective construction scheduling optimization using elitist non-dominated sorting genetic algorithm , 2016 .

[28]  Khaled Mahmoud El-Gohary,et al.  Engineering Approach Using ANN to Improve and Predict Construction Labor Productivity under Different Influences , 2017 .

[29]  Yonghan Ahn,et al.  Estimating Compressive Strength of Concrete Using Deep Convolutional Neural Networks with Digital Microscope Images , 2019, J. Comput. Civ. Eng..

[30]  Concepción S. Wilson,et al.  The Literature of Bibliometrics, Scientometrics, and Informetrics , 2001, Scientometrics.

[31]  David Greiner,et al.  Shape design optimization of road acoustic barriers featuring top-edge devices by using genetic algorithms and boundary elements , 2016 .

[32]  Tarek Zayed,et al.  Generic Scheduling Optimization Model for Multiple Construction Projects , 2017, J. Comput. Civ. Eng..

[33]  C. M. Eastman,et al.  Through the looking glass: why no wonderland , 1974 .

[34]  Oral Büyüköztürk,et al.  Autonomous Structural Visual Inspection Using Region‐Based Deep Learning for Detecting Multiple Damage Types , 2018, Comput. Aided Civ. Infrastructure Eng..

[35]  Yousef Hosseinzadeh,et al.  Combining Migration and Differential Evolution Strategies for Optimum Design of Truss Structures with Dynamic Constraints , 2018, Iranian Journal of Science and Technology, Transactions of Civil Engineering.

[36]  Yujie Wei,et al.  A vision and learning-based indoor localization and semantic mapping framework for facility operations and management , 2019, Automation in Construction.

[37]  Telecommunications Board.  Funding a Revolution: Government Support for Computing Research , 1999 .

[38]  L. da F. Costa,et al.  Characterization of complex networks: A survey of measurements , 2005, cond-mat/0505185.

[39]  Romualdas Bausys,et al.  Computational optimization of housing complexes forms to enhance energy efficiency , 2018 .

[40]  Charles M. Eastman.  Through the looking glass - why no wonderland? Computer applications in architecture in the USA , 1993, Comput. Aided Des..

[41]  Jeffrey S. Russell,et al.  A data-driven approach for identifying project manager competency weights , 2018 .

[42]  Shuang Wang,et al.  Structural Damage Detection using Deep Convolutional Neural Network and Transfer Learning , 2019, KSCE Journal of Civil Engineering.

[43]  E. Byington,et al.  Mapping Human Resource Management: Reviewing the field and charting future directions , 2017 .

[44]  Mohamed Marzouk,et al.  BIM-based approach for optimizing life cycle costs of sustainable buildings , 2018, Journal of Cleaner Production.

[45]  P. Fourie,et al.  The particle swarm optimization algorithm in size and shape optimization , 2002 .

[46]  Brayden G. King,et al.  The Practice of Theory Borrowing in Organizational Studies: Current Issues and Future Directions , 2009 .

[47]  Paul Schonfeld,et al.  Maximum Gradient Decision-Making for Railways Based on Convolutional Neural Network , 2019, Journal of Transportation Engineering, Part A: Systems.

[48]  Steven E. Smith,et al.  Artificial intelligence in engineering design , 1983 .

[49]  Soteris A. Kalogirou,et al.  Artificial intelligence techniques for photovoltaic applications: A review , 2008 .

[50]  Rafael Rieder,et al.  Computer vision and artificial intelligence in precision agriculture for grain crops: A systematic review , 2018, Comput. Electron. Agric..

[51]  Ali Nejat,et al.  Automation in construction scheduling: a review of the literature , 2015 .

[52]  Ronald C. Scherer.  Interdisciplinary Research Collaboration , 2005 .

[53]  Devin K. Harris,et al.  Robust Pixel-Level Crack Detection Using Deep Fully Convolutional Neural Networks , 2019, J. Comput. Civ. Eng..

[54]  Charles N. Noussair,et al.  The Nexus between Artificial Intelligence and Economics , 2012 .

[55]  Hadi Salehi,et al.  Emerging artificial intelligence methods in structural engineering , 2018, Engineering Structures.

[56]  Hyeonjoon Moon,et al.  Underground sewer pipe condition assessment based on convolutional neural networks , 2019, Automation in Construction.

[57]  Arto Kiviniemi,et al.  A review of risk management through BIM and BIM-related technologies , 2017 .

[58]  P. Rousseeuw.  Silhouettes: a graphical aid to the interpretation and validation of cluster analysis , 1987 .

[59]  Ying Ding,et al.  Scientific collaboration and endorsement: Network analysis of coauthorship and citation networks , 2011, J. Informetrics.

[60]  Raymond E. Levitt,et al.  Artificial Intelligence Techniques for Generating Construction Project Plans , 1988 .

[61]  Xianbo Zhao,et al.  A bibliometric review of green building research 2000–2016 , 2018, Architectural Science Review.

[62]  Geoffrey E. Hinton.  Deep belief networks , 2009, Scholarpedia.

[63]  Hyo Seon Park,et al.  Convolutional neural network‐based wind‐induced response estimation model for tall buildings , 2019, Comput. Aided Civ. Infrastructure Eng..

[64]  Ajith Abraham,et al.  Engineering applications of artificial intelligence: A bibliometric analysis of 30 years (1988-2018) , 2019, Eng. Appl. Artif. Intell..

[65]  Robert L. Lytton,et al.  3D simulation of deflection basin of pavements under high-speed moving loads , 2019, Construction and Building Materials.

[66]  Ruoyu Jin,et al.  Scientometric Review of Articles Published in ASCE’s Journal of Construction Engineering and Management from 2000 to 2018 , 2019, Journal of Construction Engineering and Management.

[67]  Yousef Hosseinzadeh,et al.  Design optimization of truss structures with continuous and discrete variables by hybrid of biogeography‐based optimization and differential evolution methods , 2018 .

[68]  Pei-Chun Lee,et al.  Mapping knowledge structure by keyword co-occurrence: a first look at journal papers in Technology Foresight , 2010, Scientometrics.

[69]  Yvonne Rogers,et al.  Citation counting, citation ranking, and h-index of human-computer interaction researchers: A comparison of Scopus and Web of Science , 2008, J. Assoc. Inf. Sci. Technol..

[70]  Rishabh Shrivastava,et al.  Artificial Intelligence Research in India: A Scientometric Analysis , 2016 .

[71]  W. Glänzel,et al.  Analysing Scientific Networks Through Co-Authorship , 2004 .

[72]  Oral Büyüköztürk,et al.  Deep Learning‐Based Crack Damage Detection Using Convolutional Neural Networks , 2017, Comput. Aided Civ. Infrastructure Eng..

[73]  QadirJunaid,et al.  Big Data in the construction industry , 2016 .

[74]  Jon Kleinberg,et al.  Authoritative sources in a hyperlinked environment , 1999, SODA '98.

[75]  Hosang Hyun,et al.  Multiple Modular Building Construction Project Scheduling Using Genetic Algorithms , 2019, Journal of Construction Engineering and Management.

[76]  Donald Bloswick,et al.  Impact of the OSHA Trench and Excavation Standard on Fatal Injury in the Construction Industry , 2002, Journal of occupational and environmental medicine.

[77]  Cornelius T. Leondes,et al.  Expert systems : the technology of knowledge management and decision making for the 21st century , 2002 .

[78]  Anne Morris,et al.  Expert systems in the United Kingdom: an evaluation of development methodologies , 1989 .

[79]  Ludo Waltman,et al.  Visualizing Bibliometric Networks , 2014 .

[80]  Shane Legg,et al.  Human-level control through deep reinforcement learning , 2015, Nature.

[81]  Diana Hicks,et al.  The difficulty of achieving full coverage of international social science literature and the bibliometric consequences , 1999, Scientometrics.

[82]  Feng Li.  The digital transformation of business models in the creative industries: A holistic framework and emerging trends , 2018, Technovation.

[83]  Dongming Zhang,et al.  Deep learning based image recognition for crack and leakage defects of metro shield tunnel , 2018, Tunnelling and Underground Space Technology.

[84]  Jixiu Wu,et al.  Automatic detection of hardhats worn by construction personnel: A deep learning approach and benchmark dataset , 2019, Automation in Construction.

[85]  Lukumon O. Oyedele,et al.  Big Data in the construction industry: A review of present status, opportunities, and future trends , 2016, Adv. Eng. Informatics.

[86]  Yong K. Cho,et al.  Multiobjective Construction Schedule Optimization Using Modified Niched Pareto Genetic Algorithm , 2016 .