Artificial Neural Network Application in Construction and the Built Environment: A Bibliometric Analysis

被引:1
作者
Kaushik, Amit Kant [1 ]
Islam, Rubina [2 ]
Elbahy, Salma [1 ]
Arif, Mohammed [3 ]
机构
[1] Northumbria Univ, Fac Engn & Environm, Dept Architecture & Built Environm, Newcastle Upon Tyne NE1 8ST, England
[2] Univ Coll Estate Management, Reading RG1 4BS, England
[3] Leeds Trinity Univ, Enterprise & External Engagement, Horsforth Leeds LS18 5HD, England
关键词
artificial neural network; built environment; bibliometric analysis; scientometric analysis; energy efficiency; BUILDING ENERGY-CONSUMPTION; COMPRESSIVE STRENGTH PREDICTION; REINFORCED-CONCRETE; OFFICE BUILDINGS; GENETIC ALGORITHM; OCCUPANT BEHAVIOR; THERMAL COMFORT; MULTIOBJECTIVE OPTIMIZATION; RECONSTRUCTION PROJECTS; RESIDENTIAL BUILDINGS;
D O I
10.3390/buildings14082423
中图分类号
TU [建筑科学];
学科分类号
0813 ;
摘要
Over the past decade, there has been a dramatic increase in the use of various technologies in the Architecture, Engineering, and Construction sector. Artificial intelligence has played a significant role throughout the different phases of the design and construction process. A growing body of literature recognizes the importance of artificial neural network applications in numerous areas of the construction industry and the built environment, presenting a need to explore the main research themes, attributes, benefits, and challenges. A three-step extensive research method was utilized by conducting a bibliometric search of English language articles and conducting quantitative and qualitative analyses. The bibliometric analysis aimed to identify the current research directions and gaps forming future research areas. The scientometric analysis of keywords revealed diverse areas within the construction industry linked to ANNs. The qualitative analysis of the selected literature revealed that energy management in buildings and construction cost predictions were the leading research topics in the study area. These findings recommend directions for further research in the field, for example, broadening the application ranges of ANNs in the current Construction 4.0 technologies, such as robotics, 3D printing, digital twins, and VR applications.
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页数:36
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