Artificial Neural Network Application in Construction and the Built Environment: A Bibliometric Analysis
Abstract
:1. Introduction
- A quantitative analysis identifies the critical authors and journal articles from emerging regions that have had the most significant influence on ANNs in construction and the built environment over the past two decades.
- Identify the key growth areas in research on ANNs in the built environment.
- Recognize the structure of the knowledge base on ANNs in the built environment.
- Reveal the areas that need further investigation by identifying the gaps in knowledge.
- Identify the directions of future research.
2. Methodology: Multi-Stage Critical Literature Review
2.1. Phase 1: Data Collection
2.2. Phase 2: Quantitative Analysis Stage
2.3. Phase 3: Qualitative Analysis Stage
3. Quantitative Analysis and Scientometrics
3.1. Authorship Analysis
3.1.1. Active Countries
3.1.2. Co-Authors Analysis
3.2. Citation Analysis
3.2.1. Journals Citation Analysis
3.2.2. Articles Citation Analysis
3.3. Authors’ Keywords Analysis
3.3.1. Co-Occurrence Analysis
3.3.2. Thematic Clusters
- Cluster one—“Red”: the largest cluster has 42 keywords. It is more related to the basics of the artificial neural networks within the early years of ANN integration in the AEC section, as it refers to research themes between 2007 and 2016 [13,15]. It includes keywords related to themes such as the construction industry and numerical models for data analysis. The construction theme focuses on construction management, concrete strength analysis, demolition waste estimates, labor productivity, and personnel issues on site. The second theme includes studies that use ANN algorithms for the prediction analysis for cost, behavior, and risk assessment. It includes keywords for fuzzy logic/sets, regression analysis, decision support systems, ensemble algorithms, genetic algorithms, and the Adaptive Neuro-Fuzzy Inference System (ANFIS).
- Cluster two—“Green”: this cluster (34 keywords) has one major theme that includes the typical hierarchy of artificial intelligence, including machine learning, deep learning, and convolutional neural networks (CNNs). On the other hand, it includes a construction safety theme [55,56]. The CNN theme is connected to all computer vision, remote sensing, and semantic analysis [57,58,59]. CNNs are also for building/urban extraction studies, the newest study fields in urban and built environments [60,61]. The second theme is an extension of the construction industry that shifted more towards construction safety [62,63] and cost estimations [64,65] within different project phases, not only the initial or conceptual phases.
- Cluster three—“Blue”: the cluster has three main themes: energy consumption, thermal comfort, and occupant behavior [66,67]. They are interconnected with keywords such as BMS, IoT, smart/intelligent, and homes/grid [37,68,69,70]. The interrelation between the three themes aligns with the significance of a holistic approach to serve each other towards adequate, sustainable, and energy-considered “building design” [71,72,73,74].
- Cluster five—“Grey”: the highest co-occurrence and length strength in this cluster is “energy efficiency” and then “data-driven” models. It refers to the set of publications that were published between 2018 and 2020. It ensures the track of development within this area using ML and ANN through models such as multiple regression analysis, support vector regression, and transfer learning. Thus, it helped to improve the energy models’ simulation efficiency and prediction accuracy.
- Cluster six—“Cyan”: this seems to be the smallest cluster with only 18 keywords; however, it was considered the core cluster in this analysis. It includes the primary keyword ANN together with major concepts of deep neural networks, genetic algorithms, digital twins, life cycle assessment, and sustainability [80,81,82,83,84].
- (2007–2014); represents the start of the integration of the ANN models into the AEC sector depending on the numerical models for data management, analysis, and forecasting over very considered phases of the construction project feasibility study or energy consumption analysis.
- (2014–2018); the intermediate phase, which followed the evolution of the concept of the IoT and publications, appears to integrate ANN, ML, and deep learning with three main themes: demand response within smart buildings, energy models, and the integration with BMS as a complementary approach for the IoT.
- (2018–Present)In recent years, the rate of trial experiments on the various core themes mentioned has been increasing in alignment with digital transformation. There is a paradigm shift that makes use of the development of remote-sensing technologies, which impose CNN and its related applications, such as computer vision, image processing, and semantic analysis.
4. Results
4.1. Energy Management in Buildings
4.1.1. Predicting Energy Consumption
4.1.2. Improving Energy Performance
4.2. Occupant Comfort in Buildings
4.2.1. Thermal Comfort of Occupants
4.2.2. Modelling Occupant Behavior
4.3. Design & Construction Optimization
4.3.1. Building Design Optimization
4.3.2. Construction Material Optimization
4.4. Cost Prediction
4.5. Health and Safety in Construction
4.5.1. Safety of Workers
4.5.2. Safety of Structures
4.6. Soil Mechanics
5. Discussion
5.1. Broadening the Application Ranges of ANN into the Current Construction 4.0 Technologies
- Robotics: To date, limited research has addressed artificial intelligence applications in robotics and automated systems in construction. It is a fertile area for future study as ANNs exhibit capabilities such as adaptive learning, pattern recognition, and real-time operation [26]. These ANN capabilities can help to develop advanced robotic systems to perform several tasks in the construction industry. The findings derived from the scientometric analysis suggest a lack of studies addressing and assessing the risks faced by construction workers. Section 4.5 in the qualitative analysis shows how the safety of workers in construction sites is monitored using ANN applications, with a need for studies on using these applications in robotic systems that can replace workers in performing arduous and dangerous tasks.
- 3D Printing: Another possible area of future research would be to investigate applying ANN in 3D printing due to its capability to process large datasets and its powerful computational ability. 3D printing technology has more potential for application with BIM development [222]. Although this technology is still in its infancy in the construction industry, it would be interesting to investigate the association between ANNs and BIM technology to produce 3D-printed buildings from a BIM model in future studies.
- Digital Twins: Digital twins are identified as a “digital copy of a physical asset, collecting real-time data from the asset and deriving information not being measured directly in the hardware” [223]. This is a potentially abundant research area as the models of existing structures can be enriched dynamically by incorporating the capabilities of ANNs into training existing building data linked with real-time IoT data. Also, the current shift from building-centered to human-centered approaches adds more complexity to the application capabilities of this technology [224].
- VR Applications: Future studies can explore the combined advantages of machine learning and VR applications to develop an intelligent system that assists in various tasks in the construction industry to improve design and safety.
5.2. Constructing ANN Applications Research in Developing Countries
5.3. Potential Areas for Improvement in the Application of Neural Networks
- Prevention of overestimation in ANNs: a limitation of developing ANN models is that ANNs build the models automatically after being fed raw data. This leads to the potential risk of overestimation due to the pseudorandom nature of trained datasets [225]. Further research areas can delve into the avoidance of overestimation.
- Selection of datasets: an additional weakness is the selection of adequate training datasets. Future studies, including dataset inclusion criteria, would be worthwhile.
6. Future Perspectives
- Broadening the application ranges of ANN into the current Construction 4.0 technologies, such as robotics, 3D printing, digital twins, and VR applications.
- Constructing ANN applications research in developing countries.
- Potential for improving Neural Networks through the prevention of overestimation in ANNs. The selection criteria of datasets and further research into the “white box model”.
6.1. Theoretical Implications
6.2. Practical Implications
7. Conclusions
Author Contributions
Funding
Data Availability Statement
Conflicts of Interest
Appendix A
Energy Management in Buildings | |||
---|---|---|---|
No. | Title | Year | Subcategory |
1 | A Comparative Analysis of Data-Driven Based Optimization Models for Energy-Efficient Buildings | 2020 | Improving Energy Performance |
2 | A comprehensive method for optimizing the design of a regular architectural space to improve building performance | 2021 | |
3 | A decision tree method for building energy demand modelling | 2010 | |
4 | A study on energy performance of 30 commercial office buildings in Hong Kong | 2017 | |
5 | A zone-level, building energy optimization combining an artificial neural network, a genetic algorithm, and model predictive control | 2018 | |
6 | Accuracy of different machine learning algorithms and added-value of predicting aggregated-level energy performance of commercial buildings | 2020 | |
7 | An ANN-GA Semantic Rule-Based System to Reduce the Gap Between Predicted and Actual Energy Consumption in Buildings | 2017 | |
8 | An original tool for checking energy performance and certification of buildings by means of Artificial Neural Networks | 2014 | |
9 | ANN-GA smart appliance scheduling for optimized energy management in the domestic sector | 2016 | |
10 | Artificial neural network-based decision support system for development of an energy-efficient built environment | 2018 | |
11 | Artificial neural networks for energy analysis of office buildings with daylighting | 2010 | |
12 | Attention-based interpretable neural network for building cooling load prediction | 2021 | |
13 | Comparative study of a building energy performance software (KEP-IYTE-ESS) and ANN-based building heat load estimation | 2014 | |
14 | Data-Driven Building Energy Modelling—Generalization Potential of Energy Signatures Through Interpretable Machine Learning | 2022 | |
15 | Data-driven model predictive control using random forests for building energy optimization and climate control | 2018 | |
16 | Deep learning for estimating building energy consumption | 2016 | |
17 | Development of a ranking procedure for energy performance evaluation of buildings based on occupant behavior | 2019 | |
18 | Early Phases predicting cooling loads for energy-efficient design in office buildings by machine learning | 2019 | |
19 | Energy performance forecasting of residential buildings using fuzzy approaches | 2020 | |
20 | Fault detection analysis using data mining techniques for a cluster of smart office buildings | 2015 | |
21 | Machine learning modelling for predicting non-domestic buildings energy performance: A model to support deep energy retrofit decision-making | 2020 | |
22 | Modelling heating and cooling loads by artificial intelligence for energy-efficient building design | 2014 | |
23 | Modelling the energy performance of residential buildings using advanced computational frameworks based on RVM, GMDH, ANFIS-BBO and ANFIS-IPSO | 2021 | Improving Energy Performance |
24 | More Buildings Make More Generalizable Models—Benchmarking Prediction Methods on Open Electrical Meter Data | 2019 | |
25 | Multi-Objective Optimization for Energy Performance Improvement of Residential Buildings: A Comparative Study | 2017 | |
26 | Predicting heating demand and sizing a stratified thermal storage tank using deep learning algorithms | 2018 | |
27 | Predicting hourly cooling load in the building: A comparison of support vector machine and different artificial neural networks | 2009 | |
28 | Prediction of building’s temperature using neural networks models | 2006 | |
29 | Short-Term Forecasting of Heat Demand of Buildings for Efficient and Optimal Energy Management Based on Integrated Machine Learning Models | 2020 | |
30 | Systematic approach to provide building occupants with feedback to reduce energy consumption | 2020 | |
31 | The London Heat Island and building cooling design | 2007 | |
32 | Usability evaluation of a web-based tool for supporting holistic building energy management | 2017 | |
33 | A data-driven interval forecasting model for building energy prediction using attention-based LSTM and fuzzy information granulation | 2022 | Predicting Energy Consumption |
34 | A hybrid model for building energy consumption forecasting using long short-term memory networks | 2020 | |
35 | A long short-term memory artificial neural network to predict daily HVAC consumption in buildings | 2020 | |
36 | An artificial neural network (ANN) expert system enhanced with the electromagnetism-based firefly algorithm (EFA) for predicting the energy consumption in buildings | 2020 | |
37 | Artificial neural network for assessment of energy consumption and cost for cross laminated timber office building in severe cold regions | 2018 | |
38 | Artificial neural network model for forecasting sub-hourly electricity usage in commercial buildings | 2016 | |
39 | Artificial neural networks for the prediction of the energy consumption of a passive solar building | 2000 | |
40 | Determining key variables influencing energy consumption in office buildings through cluster analysis of pre- and post-retrofit building data | 2018 | |
41 | Energy consumption predicting model of VRV (Variable refrigerant volume) system in office buildings based on data mining | 2016 | |
42 | Estimating building energy consumption using extreme learning machine method | 2016 | |
43 | Forecast electricity demand in commercial building with machine learning models to enable demand response programs | 2022 | |
44 | Forecasting energy demand of PCM integrated residential buildings: A machine learning approach | 2023 | |
45 | Forecasting peak energy demand for smart buildings | 2021 | |
46 | Hourly energy consumption prediction of an office building based on ensemble learning and energy consumption pattern classification | 2021 | |
47 | Improving consumption estimation of electrical materials in residential building construction | 2016 | |
48 | Measuring energy consumption efficiency of the construction industry: The case of China | 2015 | |
49 | Modelling energy consumption in residential buildings: A bottom-up analysis based on occupant behavior pattern clustering and stochastic simulation | 2017 | Predicting Energy Consumption |
50 | Prediction of building energy consumption by using artificial neural networks | 2009 | |
51 | Prediction of hourly energy consumption in buildings based on a feedback artificial neural network | 2005 | |
52 | Prediction of occupancy level and energy consumption in office building using blind system identification and neural networks | 2019 | |
53 | Vector field-based support vector regression for building energy consumption prediction | 2019 | |
54 | Visualized strategy for predicting buildings energy consumption during Early Phases design stage using parametric analysis | 2017 | |
Occupant Comfort in buildings | |||
No. | Title | Year | Subcategory |
55 | A machine-learning-based approach to predict residential annual space heating and cooling loads considering occupant behavior | 2020 | Modelling Occupant Behavior |
56 | BIM-PoseNet: Indoor camera localization using a 3D indoor model and deep learning from synthetic images | 2019 | |
57 | Building occupancy modelling using generative adversarial network | 2018 | |
58 | Development and comparative analysis of the fuzzy inference system-based construction labor productivity models | 2023 | |
59 | Engineering Approach Using ANN to Improve and Predict Construction Labor Productivity under Different Influences | 2017 | |
60 | Machine learning for occupant-behavior-sensitive cooling energy consumption prediction in office buildings | 2021 | |
61 | Modelling occupant behavior in buildings | 2020 | |
62 | Occupant behavior modelling methods for resilient building design, operation, and policy at urban scale: A review | 2021 | |
63 | Opportunistic occupancy-count estimation using sensor fusion: A case study | 2019 | |
64 | Predicting the construction labor productivity using artificial neural network and grasshopper optimization algorithm | 2023 | |
65 | Reinforcement learning of occupant behavior model for cross-building transfer learning to various HVAC control systems | 2021 | |
66 | Simulating the impact of occupant behavior on energy use of HVAC systems by implementing a behavioral artificial neural network model | 2019 | |
67 | Applying Artificial Neural Networks for Measuring and Predicting Construction-Labor Productivity | 2015 | |
68 | A building information model (BIM) and artificial neural network (ANN) based system for personal thermal comfort evaluation and energy efficient design of interior space | 2019 | Thermal Comfort of Occupants |
69 | A novel method based on neural networks for designing internal coverings in buildings: Energy saving and thermal comfort | 2019 | |
70 | Adaptive behavior and different thermal experiences of real people: A Bayesian neural network approach to thermal preference prediction and classification | 2021 | |
71 | Application of multi-objective genetic algorithm to optimize energy efficiency and thermal comfort in building design | 2015 | |
72 | Artificial neural network analysis of teachers’ performance against thermal comfort | 2021 | |
73 | Artificial neural network for the thermal comfort index prediction: Development of a new simplified algorithm | 2020 | |
74 | Artificial neural network models using thermal sensations and occupants’ behavior for predicting thermal comfort | 2018 | |
75 | Automated classification of indoor environmental quality control using stacked ensembles based on electroencephalograms | 2020 | Thermal Comfort of Occupants |
76 | Comparative performance of machine learning algorithms in the prediction of indoor daylight illuminances | 2020 | |
77 | Data driven indoor air quality prediction in educational facilities based on IoT network | 2021 | |
78 | Development and application of linear ventilation and temperature models for indoor environmental prediction and HVAC systems control | 2019 | |
79 | Fast prediction for multi-parameters (concentration, temperature, and humidity) of indoor environment towards the online control of HVAC system | 2021 | |
80 | Indoor environmental quality evaluation of lecture classrooms in an institutional building in a cold climate | 2019 | |
81 | Infrared infused cicion-based | 2022 | |
82 | Model predictive control with adaptive machine-learning-based model for building energy efficiency and comfort optimization | 2020 | |
83 | Neural networks based predictive control for thermal comfort and energy savings in public buildings | 2012 | |
84 | Temperature-preference learning with neural networks for occupant-centric building indoor climate controls | 2019 | |
85 | Thermal comfort prediction in a building category: Artificial neural network generation from calibrated models for a social housing stock in southern Europe | 2019 | |
86 | Toward contactless human thermal monitoring: A framework for Machine Learning-based human thermo-physiology modelling augmented with computer vision | 2023 | |
Occupant Comfort in buildings | |||
No. | Title | Year | Subcategory |
87 | A neural network approach to predicting the net costs associated with BIM adoption | 2020 | BIM adoption |
88 | Construction Cost Prediction Based on Genetic Algorithm and BIM | 2020 | |
89 | Developing an Integrative Data Intelligence Model for Construction Cost Estimation | 2022 | |
90 | Forecasting the net costs to organizations of building information modelling (BIM) implementation at different levels of development (LOD) | 2019 | |
91 | A CBR-based hybrid model for predicting a construction duration and cost based on project characteristics in multi-family housing projects | 2010 | Early Phases |
92 | A computer-based cost prediction model for institutional building projects in Nigeria an artificial neural network approach | 2014 | |
93 | A hybrid approach for a cost estimate of residential buildings in Egypt at the Early Phases stage | 2020 | |
94 | A model utilizing the artificial neural network in cost estimation of construction projects in Jordan | 2021 | |
95 | A neural network approach for Early Phases cost estimation of structural systems of buildings | 2004 | |
96 | Conceptual estimation of construction costs using the multistep ahead approach | 2016 | |
97 | Efficient estimation and optimization of building costs using machine learning | 2023 | |
98 | Improved similarity measure in case-based reasoning: a case study of construction cost estimation | 2020 | |
99 | Investment decision management: Prediction of the cost and period of commercial building construction using artificial neural network | 2011 | Early Phases |
100 | Neural Network-Based Model for Predicting Preliminary Construction Cost as Part of Cost Predicting System | 2020 | |
101 | Predicting construction cost and schedule success using artificial neural networks ensemble and support vector machines classification models | 2012 | |
102 | Web-based conceptual cost estimates for construction projects using Evolutionary Fuzzy Neural Inference Model | 2009 | |
103 | Cost premium prediction of certified green buildings: A neural network approach | 2011 | Environmental Impact Assessment |
104 | Environmental impacts cost assessment model of residential building using an artificial neural network | 2021 | |
105 | A framework of developing machine learning models for facility life-cycle cost analysis | 2020 | LCA |
106 | Extreme Gradient Boosting-Based Machine Learning Approach for Green Building Cost Prediction | 2022 | |
107 | Life cycle environmental and cost assessment of prefabricated components manufacture | 2023 | |
108 | Multi-objective optimization of building design for life cycle cost and CO2 emissions: A case study of a low-energy residential building in a severe cold climate | 2022 | |
109 | An artificial neural network approach to predicting most applicable post-contract cost controlling techniques in construction projects | 2020 | Post-contract |
110 | Application of artificial neural network methodology for predicting seismic retrofit construction costs | 2014 | Reconstruction |
111 | Approximately predicting the cost and duration of school reconstruction projects in Taiwan | 2006 | |
112 | Predicting cost deviation in reconstruction projects: Artificial neural networks versus regression | 2003 | |
113 | Construction cost prediction model for conventional and sustainable college buildings in North America | 2017 | |
114 | Novel Machine-Learning Model for Estimating Construction Costs Considering Economic Variables and Indexes | 2018 | |
Design & Construction Optimization | |||
No. | Title | Year | Subcategory |
118 | Modelling the confined compressive strength of hybrid circular concrete columns using neural networks | 2011 | Building Design Optimization |
119 | Multi-objective optimization for building retrofit: A model using genetic algorithm and artificial neural network and an application | 2014 | |
120 | Multi-objective optimization of building design using TRNSYS simulations, genetic algorithm, and Artificial Neural Network | 2010 | |
121 | On-demand monitoring of construction projects through a game-like hybrid application of BIM and machine learning | 2020 | |
122 | Predicting Crowd Egress and Environment Relationships to Support Building Design Optimization | 2020 | |
123 | Robust optimal design of zero/low energy buildings considering uncertainties and the impacts of objective functions | 2019 | |
124 | A neural network method for analyzing concrete durability | 2008 | Construction material optimization |
125 | An artificial neural network approach for prediction of long-term strength properties of steel fiber reinforced concrete containing fly ash | 2008 | |
126 | ANN-Python prediction model for the compressive strength of green concrete | 2023 | |
127 | Artificial neural networks in classification of steel grades based on non-destructive tests | 2020 | Construction material optimization |
128 | Compressive strength prediction of recycled concrete based on deep learning | 2018 | |
129 | Concrete compressive strength prediction using the imperialist competitive algorithm | 2018 | |
130 | Deep belief network-based 3D models classification in building information modelling | 2015 | |
131 | Deep neural network with high-order neuron for the prediction of foamed concrete strength | 2019 | |
132 | Designing the composition of cement stabilized rammed earth using artificial neural networks | 2019 | |
133 | Feature importance of stabilized rammed earth components affecting the compressive strength calculated with explainable artificial intelligence tools | 2020 | |
134 | Metamodel-based design optimization of structural one-way slabs based on deep learning neural networks to reduce environmental impact | 2018 | |
135 | Predicting concrete compressive strength using hybrid ensembling of surrogate machine learning models | 2021 | |
136 | Predicting the compressive strength of normal and High-Performance Concretes using ANN and ANFIS hybridized with Grey Wolf Optimizer | 2020 | |
137 | Predicting the core compressive strength of self-compacting concrete (SCC) mixtures with mineral additives using artificial neural network | 2011 | |
138 | Prediction of concrete compressive strength: Research on hybrid models genetic based algorithms and ANFIS | 2014 | |
139 | Properties and material models for common construction materials at elevated temperatures | 2019 | |
Health & Safety in Construction | |||
No. | Title | Year | Subcategory |
140 | A machine learning-based prediction and analysis of flood affected households: A case study of floods in Bangladesh | 2019 | Safety of structures |
141 | Application of the Artificial Neural Network for Predicting Mainshock-Aftershock Sequences in Seismic Assessment of Reinforced Concrete Structures | 2021 | |
142 | Defect detection in reinforced concrete using random neural architectures | 2014 | |
143 | Evolutionary learning based sustainable strain sensing model for structural health monitoring of high-rise buildings | 2017 | |
144 | Fusing damage-sensitive features and domain adaptation towards robust damage classification in real buildings | 2023 | |
145 | Image-driven structural steel damage condition assessment method using deep learning algorithm | 2019 | |
146 | Integration of super-pixel segmentation and deep-learning methods for evaluating earthquake-damaged buildings using single-phase remote sensing imagery | 2020 | |
147 | Investigation of the effects of corrosion on bond strength of steel in concrete using neural network | 2021 | |
148 | Neuro-fuzzy techniques for the classification of earthquake damages in buildings | 2010 | |
149 | Object-Based Convolutional Neural Network for High-Resolution Imagery Classification | 2017 | |
150 | Operational earthquake-induced building damage assessment using CNN-based direct remote sensing change detection on superpixel level | 2022 | |
151 | Smart performance-based design for building fire safety: Prediction of smoke motion via AI | 2021 | |
152 | Structural failure classification for reinforced concrete buildings using trained neural network based multi-objective genetic algorithm | 2017 | Safety of structures |
153 | Accident Analysis for Construction Safety Using Latent Class Clustering and Artificial Neural Networks | 2020 | Safety of workers |
154 | Construction Safety Risk Model with Construction Accident Network: A Graph Convolutional Network Approach | 2022 | |
155 | Convolutional neural networks: Computer vision-based workforce activity assessment in construction | 2018 | |
156 | Deep learning-based classification of work-related physical load levels in construction | 2020 | |
157 | Detecting safety helmet wearing on construction sites with bounding-box regression and deep transfer learning | 2021 | |
158 | Enhancing construction safety: Machine learning-based classification of injury types | 2023 | |
159 | Ergonomic posture recognition using 3D view-invariant features from single ordinary camera | 2018 | |
160 | Prediction of engineering performance: A neuro-fuzzy approach | 2005 | |
161 | Research on Safety Helmet Detection Algorithm Based on Improved YOLOv5s | 2023 | |
Soil Mechanics | |||
No. | Title | Year | |
162 | A fuzzy-neural network method for modelling uncertainties in soil-structure interaction problems | 2003 | |
163 | A new approach of hybrid bee colony optimized neural computing to estimate the soil compression coefficient for a housing construction project | 2019 | |
164 | Assessment of optimum settlement of structure adjacent urban tunnel by using neural network methods | 2013 | |
165 | Prediction of Soil Deformation in Tunnelling Using Artificial Neural Networks | 2016 |
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Kaushik, A.K.; Islam, R.; Elbahy, S.; Arif, M. Artificial Neural Network Application in Construction and the Built Environment: A Bibliometric Analysis. Buildings 2024, 14, 2423. https://doi.org/10.3390/buildings14082423
Kaushik AK, Islam R, Elbahy S, Arif M. Artificial Neural Network Application in Construction and the Built Environment: A Bibliometric Analysis. Buildings. 2024; 14(8):2423. https://doi.org/10.3390/buildings14082423
Chicago/Turabian StyleKaushik, Amit Kant, Rubina Islam, Salma Elbahy, and Mohammed Arif. 2024. "Artificial Neural Network Application in Construction and the Built Environment: A Bibliometric Analysis" Buildings 14, no. 8: 2423. https://doi.org/10.3390/buildings14082423