Abstract
Purpose
This study reviews the extent of application of artificial intelligence (AI) tools in the construction industry.
Design/methodology/approach
A thorough literature review (based on 165 articles) was conducted using Elsevier's Scopus due to its simplicity and as it encapsulates an extensive variety of databases to identify the literature related to the scope of the present study.
Findings
The following items were extracted: type of AI tools used, the major purpose of application, the geographical location where the study was conducted and the distribution of studies in terms of the journals they are published by. Based on the review results, the disciplines the AI tools have been used for were classified into eight major areas, such as geotechnical engineering, project management, energy, hydrology, environment and transportation, while construction materials and structural engineering. ANN has been a widely used tool, while the researchers have also used other AI tools, which shows efforts of exploring other tools for better modelling abilities. There is also clear evidence of that studies are now growing from applying a single AI tool to applying hybrid ones to create a comparison and showcase which tool provides a better result in an apple-to-apple scenario.
Practical implications
The findings can be used, not only by the researchers interested in the application of AI tools in construction, but also by the industry practitioners, who are keen to further understand and explore the applications of AI tools in the field.
Originality/value
There are no studies to date which serves as the center point to learn about the different AI tools available and their level of application in different fields of AEC. The study sheds light on various studies, which have used AI in hybrid/evolutionary systems to develop effective and accurate predictive models, to offer researchers and model developers more tools to choose from.
Keywords
Citation
Momade, M.H., Durdyev, S., Estrella, D. and Ismail, S. (2021), "Systematic review of application of artificial intelligence tools in architectural, engineering and construction", Frontiers in Engineering and Built Environment, Vol. 1 No. 2, pp. 203-216. https://doi.org/10.1108/FEBE-07-2021-0036
Publisher
:Emerald Publishing Limited
Copyright © 2021, Mohammed Hamza Momade, Serdar Durdyev, Dave Estrella and Syuhaida Ismail
License
Published in Frontiers in Engineering and Built Environment. Published by Emerald Publishing Limited. This article is published under the Creative Commons Attribution (CC BY 4.0) licence. Anyone may reproduce, distribute, translate and create derivative works of this article (for both commercial and non-commercial purposes), subject to full attribution to the original publication and authors. The full terms of this licence maybe seen at http://creativecommons.org/licences/by/4.0/legalcode.
1. Introduction
Artificial intelligence (AI), which combines algorithm, math and creativity and incorporates various techniques, has played a significant role in several application areas (Zadeh, 1994; Jalal et al., 2013). It aims to exploit the tolerance for uncertainty and imprecision in achieving robustness, traceability as well as low solution cost (Momeni et al., 2018). Its principal components are probabilistic reasoning, neuro computing and fuzzy logic, which have been used in the field of prediction and forecasting (Maier and Dandy, 2000).
Due to poor performance of the conventional models in complex non-linear systems, researchers and practitioners have been seeking to improve prediction tools (Chou and Pham, 2013). Secondly, due to a complex and uncertain process, soft computing methods are preferred over conventional models for prediction and forecasting (Garg and Tai, 2014). Machine learning (ML) has also been widely used in the last decade throughout computer science (CS) and beyond (Fateh and Hejazi, 2020), which is a clear evidence of the advantage of the learning from data over learning “manually”.
The new techniques that apply AI tools have been widely used in various engineering fields for forecasting and prediction (Veeramachaneni et al., 2003; Sadowski et al., 2018; Manzoor et al., 2021). For example, data mining is acquired through a combination of technological methods in many areas, such as expert systems, CS, ML, information retrieval and statistics (Liao et al., 2012). Furthermore, ML models, which have also been used in a wide range of engineering practices, offer predictive and descriptive capability (Jing and Hudson, 2002). Thus, these features are utilized to generate new knowledge and explore through learning (Aladag et al., 2013).
Reportedly, AI tools and predictive models also offer practicality, feasibility and quickness in solving engineering problems (Roosta et al., 2014; Avunduk and Copur, 2018; Zhang et al., 2019). Artificial Neural Network (ANN), Support Vector Machine (SVM) and Multivariate Adaptive Regression Spline (Mars) are examples of tools and models that have been used in various engineering disciplines, such as analyzing the effect of curing time, dosage of bentonite, addition of silty clay and amount of water on the outputs (Amlashi et al., 2019), prediction of the fracture energy of asphalt specimens (Majidifard et al., 2019) and a 28-day compressive strength of a high and normal strength self-compacting concrete (Prasad et al., 2009) among others.
AI tools, which can contribute in solving complex problems (Tinoco et al., 2018), have also emerged as a powerful device to be used in the construction (hereafter, AEC) practices (Lagaros and Papadrakakis, 2012; Jodaei et al., 2012; Momade et al., 2020). The ongoing developments in the CS areas of AI and CI have led the researchers to apply them successfully in various fields of AEC industry (Mohammadhassani et al., 2013; Hamza et al., 2019). By applying AI in design, modelling and prediction, it can be helpful to efficiently use main construction resources, which will ultimately result in a lower cost for the work done (Tayfur et al., 2013). A comprehensive review of the context is a clear indication of a global interest in using different AI tools in various fields of AEC. Taking this as a point of departure, this study aims at longitudinally reviewing the context to understand the trends and investigate the developments in the scoping area. By doing so, the study fills the gap (lack of a robust review that focuses on, particularly, the application of AI in AEC) by providing insight into the body of knowledge on the subject.
The findings can be used, not only by the researchers interested in the application of AI tools in construction, but also by the industry practitioners, who are keen to further understand and explore the applications of AI tools in the field. There has been a significant rise in application of AI tools in the AEC industry and there are no studies to date that serve as the center point to learn about the different AI tools available and their level of application in different fields of AEC. The study can help researchers and practitioners develop AI tools that can improve the performance and accuracy in prediction and modelling in different aspects. It will also shed light on various studies that have used AI in hybrid/evolutionary systems to develop effective and accurate predictive models, to offer researchers and model developers more tools to choose from.
The article is structured in the following manner. First, a timeline is provided on the AI tools currently in use in the AEC body of knowledge. After that, the methodological approach, which is adopted by this study, has been described. This is followed by the results of a robust review of the context on the subject as well as the discussion of the findings that were pulled out of the reviewed publications. Lastly, the study presents concluding remarks and recommendations for future research.
2. Timeline and application of AI tools
The authors have created a timeline, seen in Figure 1, with respect to the year where AI tools were developed. It can be noted that AI tools are not a recently created tool as assumed but rather have been utilized heavily with advancements in computer and technology in recent years. As illustrated by Supplementary Figure 1, one AI tool found in 1894 by Galton was called LR. Most of them were created from 1991 to 2010 whereby a total of 24 new tools were generated by respective researchers for purposes of forecasting and modelling. From 2011 to 2020, a total of eight new tools have also been developed so far which further solidifies the statement, that a lot more research is now dependent on the abilities of these AI tools. Also, the authors are of the opinion that the advancement in computers and technology has facilitated and provided the researchers with more advanced tools in modelling and forecasting abilities.
3. Research method
This study provides a review (as described by Durdyev and Hosseini, 2019) of the research conducted on the application of AI tools in various areas of the AEC industry. To this end, a thorough literature review was conducted using Elsevier's Scopus (due to its simplicity and as it encapsulates extensive variety of databases (Falagas et al., 2008; Durdyev, 2020)) to identify the studies related to the scope of the present study.
3.1 Sampling
A systematic search (including all the search categories) was performed (refer to Figure 1). A preliminary survey carried out using keywords, “Artificial Intelligence”, “modelling”, “modeling” AND “construction”, which retrieved 2,585 publications. Limiting those publications to the source title and only journal articles (regardless of their status or type) resulted in 818 publications. It is worth to mentioning that, due to a significant increase in the number of studies on the area during this period, the search was limited to the 2014–2020 period. Lastly, a quick review of the abstract was performed to eliminate the studies that are irrelevant to the aim of the present study, which resulted in 165 journal articles of interest published by the prominent outlets on the subject.
4. Results
A total of 165 articles were extracted from the scientific journals. As can be noted from Table 1, several journals have published studies that focused on the application of AI tools in AEC fields. It is worth to point out that the top three journals are focusing primarily on automation, modelling and use of computers in engineering, while Table 1 also provides evidence (journals' scopes) of how widely (wide range of topics) the AI tools have been used.
From the studies reported from different geographical regions/countries, application of AI tools is one of the important issues that need to be assessed and discussed. Thus, Table 2 presents the geographical distribution of the retrieved studies that are conducted in the field of AI tools in AEC. Most of the reviewed studies on the application of AI tools are reported from the following countries: China, Turkey, Iran and USA. The geographical distribution shows that most of the studies reported are from developing countries, which is clear evidence of an increasing R&D trend in the implementation of AI tools in the AEC field. It is also worth pointing out that very few studies have been conducted in Oceania, South America and Africa.
5. Discussion
5.1 Types of AI tools and disciplines where they were used
A detailed review of the retrieved articles was conducted to identify all AI tools that have been used in various disciplines of the AEC industry. Thus, Supplementary Figure 2 illustrates a comprehensive list of all AI tools and the disciplines they were applied for.
The five most popular AI tools used in the retrieved studies are ANN (91), SVM and ANFIS (twelve), MARS and RF (nine), SVM and Fuzzy Logic (seven) and LR and PSO (six), which were used in a variety of disciplines. It is worth to point out that there are three main reasons for ANN being a widely used tool, which are: (1) ANN as a parallel processor of problems (violation of the constraints by any of the neurons would not affect the output of the problem); (2) its ability to extrapolate from historic data to generate forecast and (3) it provides solution for nonlinear problem (Murat and Ceylan, 2006). Thus, eight disciplines have been established to group each study, where AI tool(s) were applied into a category. The eight categories are materials, geotechnical, hydrology, project management, transportation, energy, structural and environmental.
5.1.1 Construction and building materials
Notably, most of the studies, for which AI tools were used, have been carried out for materials related topics. ANN has been the most popular (thirty-five studies) in the construction/building materials field, while ANFIS and SVM were used by five, and FL and MGGP by four studies. In terms of the sub-disciplines where the AI tools were used, the list comprises a wide range of topics, such as predicting compressive strength of concrete (Pham et al., 2019; Dutta et al., 2018; Tanyildizi, 2018; Liu and Zheng, 2019; Ghanizadeh and Rahrovan, 2019; Al-Gburi et al., 2018; Sadowski et al., 2018; Gazder et al., 2017; Reddy, 2018; Behnood and Golafshani, 2018; Nguyen et al., 2019), measuring the adhesion/cohesion force between asphalt molecules at nanoscale level (Hassan et al., 2018), estimating the shear strength from the relative density, particle size, distribution (gradation), material hardness, gradation and fineness modulus (Yang et al., 2019; Li et al., 2019), confining (normal) stress, forecasting atmospheric corrosion of metallic materials (Zhu et al., 2019) and modelling the surface roughness in the turning of hardened AISI H11 steel (Saini et al., 2012).
5.1.2 Structural engineering
In the structural engineering field, ANN, ANIFS and SVR have 17, six and three applications, respectively. Examples of subtopics where AI tools were used in the structural field are automatic identification of defect in the bearing (Sun et al., 2018), predicting the stable cross-sectional geometry of alluvial channel profiles (Armaghani et al., 2018; Jung et al., 2019; Gholami et al., 2019), developing new afflux methods for arched bridge constructions (Hajihassani et al., 2019), prediction of vertical settlement in the ERP system (Bui et al., 2019), detecting beam failure and joint failure (Yavuz, 2019; Yaseen et al., 2018; Alwanas et al., 2019; Nikoo et al., 2018), predicting settlement of shallow foundations and calculating the complexity of the grouting efficiency level (Zhu et al., 2019).
5.1.3 Energy
For energy-related studies, ANN and Mars have a total of 12 and three studies, respectively, while SVM and SVR have been used by two studies each. Some examples of the energy-related sub-disciplines are an accurate estimation of direct horizontal irradiance (Notton et al., 2019; Ozoegwu, 2019) and estimating the cooling load of a building (Deng et al., 2018; Do and Cetin, 2018; Goudarzi et al., 2019).
5.1.4 Environment
As can be noted from Supplementary Figure 2, very few studies (two), which focused on developing models for forecasting carbon emission intensity (Acheampong and Boateng, 2019; Pour et al., 2018), were reported on environmental related topics that were used in AI tools. Thus, future researchers can consider this to be an area to explore further.
5.1.5 Geotechnical engineering
For geotechnical-related topics, ANN and RF were used by eight and five studies, respectively, while two studies used RBF, LR, DT, SVM and BRT. The sub-disciplines focused on the literature are landslide susceptibility modelling (Chen et al., 2019b; Rahmati et al., 2019), determine movement in landslide (Zhu et al., 2019; Park and Kim, 2019) and prediction of the open stope hanging wall (HW) stability (He et al., 2019; Chen et al., 2018; Lian et al., 2018).
5.1.6 Hydrology
In the hydrology field, ANN has again been the widely (four studies) used tool, SVM by two studies and GP, FNN, ELM, LSM by one study each. The AI tools were primarily used to model groundwater quality parameters (Kisi et al., 2019), measure water level and support real-time environmental monitoring of water quality parameters (Azad et al., 2019; Ao et al., 2019; Fijani et al., 2019; Delafrouz et al., 2018).
5.1.7 Project management
In the project management related studies, ANN was used by nine, and FL and SVM by two studies each. The researchers used the AI tools in the following sub-disciplines: labour productivity (Golnaraghi et al., 2019; Zhang and Fang, 2019; Momade et al., 2020), construction cost prediction (Golnaraghi et al., 2019), quality control (Fijani et al., 2019), construction litigation (Arditi and Pulket, 2010), cost optimization and prediction of incidents at construction sites (Ayhan and Tokdemir, 2019; Sarkar et al., 2019).
5.1.8 Transportation
For transportation, ANN and GEP have four applications each, SA has two and Mars and GP have one each. Prediction of the fracture energy of asphalt mixture specimens (Majidifard et al., 2019) and the flow number of dense asphalt mixtures (Mirzahosseini et al., 2013) were the primary focus of the studies that used AI tools in transportation related studies.
5.2 Use of hybrid tools
Empirical methods along with modelling capabilities have become more powerful due to the challenges in the modelling of processes experienced in the nature (Yurdakul and Akdas, 2013; Momade and Hainin, 2019). Reportedly, use of AI tools are providing more accurate results in comparison to conventional regression tools used over the decades. There is no requirement for any predetermined mathematical models for their computations as they have an ability to tolerate experimental inaccuracies far better than other tools (Eldin and Senouci, 1994; Amlashi et al., 2019). On the other hand, the regression modelling is based on a predefined structure of the model with a limited number of linear/non-linear equations. Recent studies have shown that applications of many regression models have limitations in obtaining an accurate regression equation. Lack of consideration of several factors by the regression model is another factor that made the regression analysis unsuitable (Yeh and Lien, 2009). Thus, various soft computing techniques (SCT) have emerged aiming at coping with these limitations, while the most prominent feature of the SCT is the ability to learn from experience and extract the knowledge contained in the experimental data (Mohammadi Bayazidi et al., 2014).
To increase the accuracy of results and to use a more robust performance, many newer studies are combining two AI tools. For example: ANFIS that was first used by Takatgi and Sugeno (1985) is a fuzzy inference system implemented in the framework of adaptive networks, which has been used earlier in many studies prediction of compression coefficient of soil (Pham et al., 2019), measures the adhesion/cohesion force among asphalt molecules at nanoscale level (Hassan et al., 2018), prediction of vertical settlement in ERP system (Bui et al., 2019) and others. FNN, which utilizes fuzzy sets and constitutes a three-layered architecture, is another example of a hybrid technique introduced in the reviewed articles. The network does not learn from examples, but through the evaluation of a special fuzzy error measure (Nauck and Kruse, 1993). The extended version incorporates fuzzy-if-then rules through the reduction of the number of nodes in the network's hidden layer. FNN has been used for various research topics, such as contractor prequalification (Lam et al., 2001), to estimate contractors' mark-up (Liu and Ling, 2003), among others. Examples of other hybrid techniques that have been used in various disciplines/sub-disciplines are Support Vector Regressional – Particle Swarm Optimization (SVR – PSO) (Yaseen et al., 2018), SVR (Goudarzi et al., 2019), multi-gene genetic programming (MGGP) (Hodhod et al., 2018), Random Support Vector Regression (RSVR) (Zhang and Fang, 2019), ANN/simulated annealing (ANN/SA) (Majidifard et al., 2019), weights of evidence–multivariate logistic regression (WoE – RF) (Chen et al., 2019a) and phase space reconstruction – ANN (PSR–ANN) (Delafrouz et al., 2018).
6. Conclusion
Several studies have reported the practicality and feasibility of the AI and predictive models in solving engineering problems. A thorough literature review was conducted on the application of AI tools in the AEC industry. A total of 165 selected journal articles related to AI applications in the AEC industry from 2014 to 2020 were robustly reviewed. The following items were extracted from the review: type of AI tools used, the major purpose of application, the geographical location where the study was conducted and the distribution of studies in terms of the outlets they are published by.
Based on the review results, the disciplines the AI tools have been used for were classified into eight major areas such as geotechnical engineering, project management, energy, hydrology, environment and transportation, while construction materials and structural engineering are the top disciplines where the AI tools were used the most. In total, 47 AI tools were identified and have been used in various AEC disciplines. Although ANN has been widely used AI tool, the researchers have used other AI tools, which shows efforts of exploring other tools for better modelling abilities. There is also clear evidence of that studies are now growing from applying a single AI tool to applying hybrid ones to create a comparison and showcase which tool provides a better result in an apple-to-apple scenario.
The findings of the study highlighted on the new trends on AI applications by different researchers. It also established a focal point for all researchers who can use this study to understand to what length the studies have been conducted. This paper can serve as a primary source for all researchers to review upon and use as a basis to understand how to carry forward the studies in application of AI tools and AEC topics such as project management, transportation, material properties, etc.
Although the study provides a snapshot of the AI studies, due to several limitations, the researchers must be cautious while implementing the research outcomes. Firstly, this study reviews the studies that are indexed by Scopus starting from 2014 and until its commencement. Secondly, although the authors have put in the best of efforts to capture each and every research paper on application of AI tools in the AEC industry; however, there will be papers that were not captured due to the keywords used for the search of literature in the start of the research. Thirdly, a co-citation network could also be established, which provides “a forward-looking assessment on document similarity in contrast to bibliographic coupling”. This remains a limitation of the research which the authors believe future reviewers can address when preparing their articles on similar/related topics.
Although AI tools have been used in wide range of disciplines, there are also other areas which future research can focus on:
Geotechnical engineering: Earth retaining structures, earthquake engineering, ground motion hazard analysis, shallow and deep foundation modelling and laboratory and field testing of soil related properties.
Project management: schedule and scope management, as well as risk and safety-related studies.
Transportation: urban transportation planning, air pollution and health-related studies as well as transportation safety and design.
Environmental studies: water resources planning and management, as well as waste-water treatment.
Figures
Number of studies per journal published on the subject
| Journal | # of articles |
|---|---|
| “Automation in construction, Engineering with computers, Mathematical methods in the applied sciences” | 9 |
| “Neural computing and applications” | 8 |
| “Science of the total environment, Construction and building materials” | 7 |
| “Energy and buildings, Advances in engineering software” | 6 |
| “Energies, computers and concrete” | 5 |
| “Journal of computing in civil engineering, Journal of cleaner production, Journal of materials in civil engineering, Construction management and economics, Computers and structures” | 4 |
| “Engineering structures, building and environment, Computers and geotechnics, Radiation measurements” | 3 |
| “Canadian journal of civil engineering, Journal of civil engineering and management, Advances in structural engineering, International journal of civil engineering, Hydrological processes, Frontiers of structural and civil engineering, Archives of civil and mechanical engineering, Cement and concrete research, Canadian geotechnical journal, Engineering applications of artificial intelligence, Composites part B: Engineering, Journal of Zhejiang University: Science A, KSCE journal of civil engineering, Sustainability, Scientia iranica” | 2 |
| “Computers and operations research, Applied soft computing journal, Computer-aided civil and infrastructure engineering, Safety science, Neurocomputing, Advanced engineering informatics, Energy, Journal of construction engineering and management, Journal of water resources planning and management, Structure and infrastructure engineering, Hydrological sciences journal, Journal of hydrology, Environmental modelling and software, Road materials and pavement design, Computational materials science, Energy conversion and management, Accident analysis and prevention, Expert systems with applications, Tunnelling and underground space technology, Structural engineering and mechanics, Applied soft computing journal, Structural concrete, Soft computing, European journal of environmental and civil engineering, computational intelligence and neuroscience, Civil engineering and environmental systems, Ecological modelling, Mathematical problems in engineering” | 1 |
| Total | 165 |
Geographical distribution of the reviewed studies
| Country | # of studies | % of studies |
|---|---|---|
| USA | 15 | North America (14%) |
| Canada | 8 | |
| China | 23 | Asia (60%) |
| Turkey | 17 | |
| Iran | 15 | |
| India | 11 | |
| Taiwan | 7 | |
| Vietnam | 6 | |
| Korea | 5 | |
| Malaysia, Hong Kong | 4 | |
| Japan, Singapore, Indonesia | 2 | |
| Iraq, Syria, Nepal, South Korea, Abu Dhabi, Saudi Arabia, Kuwait | 1 | |
| Poland | 6 | Europe (20%) |
| Spain | 4 | |
| Portugal, France | 3 | |
| UK, Italy | 2 | |
| Greece, Germany, Netherlands, Czech Republic, Austria, Norway, Sweden, Cyprus | 1 | |
| Australia | 5 | Oceania (3%) |
| Brazil | 2 | South America (1%) |
| Nigeria, Egypt | 1 | Africa (2%) |
The supplementary file is available online for this article.
References
Acheampong, A.O. and Boateng, E.B. (2019), “Modelling carbon emission intensity: application of artificial neural network”, Journal of Cleaner Production, Vol. 225, pp. 833-856.
Al-Gburi, M., Jonasson, J.-E. and Nilsson, M. (2018), “Prediction of restraint in second cast sections of concrete culverts using artificial neural networks”, European Journal of Environmental and Civil Engineering, Vol. 22 No. 2, pp. 226-245.
Aladag, C., Kayabasi, A. and Gokceoglu, C. (2013), “Estimation of pressuremeter modulus and limit pressure of clayey soils by various artificial neural network models”, Neural Computing and Applications, Vol. 23 No. 2, pp. 333-339.
Alwanas, A.A.H., Al-Musawi, A.A., Salih, S.Q., Tao, H., Ali, M. and Yaseen, Z.M. (2019), “Load-carrying capacity and mode failure simulation of beam-column joint connection: application of self-tuning machine learning model”, Engineering Structures, Vol. 194, pp. 220-229.
Amlashi, A.T., Abdollahi, S.M., Goodarzi, S. and Ghanizadeh, A.R. (2019), “Soft computing based formulations for slump, compressive strength, and elastic modulus of bentonite plastic concrete”, Journal of Cleaner Production, Vol. 230, pp. 1197-1216.
Ao, Y., Li, H., Zhu, L., Ali, S. and Yang, Z. (2019), “The linear random forest algorithm and its advantages in machine learning assisted logging regression modeling”, Journal of Petroleum Science and Engineering, Vol. 174, pp. 776-789.
Arditi, D. and Pulket, T. (2010), “Predicting the outcome of construction litigation using an integrated artificial intelligence model”, Journal of Computing in Civil Engineering, Vol. 24 No. 1, pp. 73-80.
Armaghani, D.J., Faradonbeh, R.S., Momeni, E., Fahimifar, A. and Tahir, M. (2018), “Performance prediction of tunnel boring machine through developing a gene expression programming equation”, Engineering with Computers, Vol. 34 No. 1, pp. 129-141.
Avunduk, E. and Copur, H. (2018), “Empirical modeling for predicting excavation performance of EPB TBM based on soil properties”, Tunnelling and Underground Space Technology, Vol. 71, pp. 340-353.
Ayhan, B.U. and Tokdemir, O.B. (2019), “Predicting the outcome of construction incidents”, Safety Science, Vol. 113, pp. 91-104.
Azad, A., Karami, H., Farzin, S., Mousavi, S.-F. and Kisi, O. (2019), “Modeling river water quality parameters using modified adaptive neuro fuzzy inference system”, Water Science and Engineering, Vol. 12 No. 1, pp. 45-54.
Behnood, A. and Golafshani, E.M. (2018), “Predicting the compressive strength of silica fume concrete using hybrid artificial neural network with multi-objective grey wolves”, Journal of Cleaner Production, Vol. 202, pp. 54-64.
Bui, X.-N., Moayedi, H. and Rashid, A.S.A. (2019), “Developing a predictive method based on optimized M5Rules–GA predicting heating load of an energy-efficient building system”, Engineering with Computers, Vol. 36, pp. 931-940.
Chen, W., Peng, J., Hong, H., Shahabi, H., Pradhan, B., Liu, J., Zhu, A.-X., Pei, X. and Duan, Z. (2018), “Landslide susceptibility modelling using GIS-based machine learning techniques for Chongren County, Jiangxi Province, China”, Science of the Total Environment, Vol. 626, pp. 1121-1135.
Chen, F., Zhou, Z., Lin, A., Niu, J., Qin, W. and Yang, Z. (2019a), “Evaluation of direct horizontal irradiance in China using a physically-based model and machine learning methods”, Energies, Vol. 12 No. 1, p. 150.
Chen, W., Sun, Z. and Han, J. (2019b), “Landslide susceptibility modeling using integrated ensemble weights of evidence with logistic regression and random forest models”, Applied Sciences, Vol. 9 No. 1, p. 171.
Chou, J.-S. and Pham, A.-D. (2013), “Enhanced artificial intelligence for ensemble approach to predicting high performance concrete compressive strength”, Construction and Building Materials, Vol. 49, pp. 554-563.
Delafrouz, H., Ghaheri, A. and Ghorbani, M.A. (2018), “A novel hybrid neural network based on phase space reconstruction technique for daily river flow prediction”, Soft Computing, Vol. 22 No. 7, pp. 2205-2215.
Deng, H., Fannon, D. and Eckelman, M.J. (2018), “Predictive modeling for US commercial building energy use: a comparison of existing statistical and machine learning algorithms using CBECS microdata”, Energy and Buildings, Vol. 163, pp. 34-43.
Do, H. and Cetin, K.S. (2018), “Evaluation of the causes and impact of outliers on residential building energy use prediction using inverse modeling”, Building and Environment, Vol. 138, pp. 194-206.
Durdyev, S. (2020), “Review of construction journals on causes of project cost overruns”, Engineering, Construction and Architectural Management, Vol. 28 No. 4, pp. 1241-1260.
Durdyev, S. and Hosseini, M.R. (2019), “Causes of delays on construction projects: a comprehensive list”, International Journal of Managing Projects in Business, Vol. 13 No. 1, pp. 20-46.
Dutta, S., Samui, P. and Kim, D. (2018), “Comparison of machine learning techniques to predict compressive strength of concrete”, Computers and Concrete, Vol. 21 No. 4, pp. 463-470.
Eldin, N.N. and Senouci, A.B. (1994), “Measurement and prediction of the strength of rubberized concrete”, Cement and Concrete Composites, Vol. 16 No. 4, pp. 287-298.
Falagas, M.E., Pitsouni, E.I., Malietzis, G.A. and Pappas, G. (2008), “Comparison of PubMed, Scopus, web of science, and Google scholar: strengths and weaknesses”, The FASEB Journal, Vol. 22 No. 2, pp. 338-342.
Fateh, A. and Hejazi, F. (2020), “Experimental testing of variable stiffness bracing system for reinforced concrete structure under dynamic load”, Journal of Earthquake Engineering, pp. 1-22, in press.
Fijani, E., Barzegar, R., Deo, R., Tziritis, E. and Konstantinos, S. (2019), “Design and implementation of a hybrid model based on two-layer decomposition method coupled with extreme learning machines to support real-time environmental monitoring of water quality parameters”, Science of the Total Environment, Vol. 648, pp. 839-853.
Garg, A. and Tai, K. (2014), “Stepwise approach for the evolution of generalized genetic programming model in prediction of surface finish of the turning process”, Advances in Engineering Software, Vol. 78, pp. 16-27.
Gazder, U., Al-Amoudi, O.S.B., Khan, S.M.S. and Maslehuddin, M. (2017), “Predicting compressive strength of bended cement concrete with ANNs”, Computers and Concrete, Vol. 20 No. 6, pp. 627-634.
Ghanizadeh, A.R. and Rahrovan, M. (2019), “Modeling of unconfined compressive strength of soil-RAP blend stabilized with Portland cement using multivariate adaptive regression spline”, Frontiers of Structural and Civil Engineering, Vol. 13 No. 4, pp. 787-799.
Gholami, A., Bonakdari, H., Samui, P., Mohammadian, M. and Gharabaghi, B. (2019), “Predicting stable alluvial channel profiles using emotional artificial neural networks”, Applied Soft Computing, Vol. 78, pp. 420-437.
Golnaraghi, S., Zangenehmadar, Z., Moselhi, O. and Alkass, S. (2019), “Application of artificial neural network (s) in predicting formwork labour productivity”, Advances in Civil Engineering, Vol. 2019, 5972620.
Goudarzi, S., Anisi, M.H., Kama, N., Doctor, F., Soleymani, S.A. and Sangaiah, A.K. (2019), “Predictive modelling of building energy consumption based on a hybrid nature-inspired optimization algorithm”, Energy and Buildings, Vol. 196, pp. 83-93.
Hajihassani, M., Kalatehjari, R., Marto, A., Mohamad, H. and Khosrotash, M. (2019), “3D prediction of tunneling-induced ground movements based on a hybrid ANN and empirical methods”, Engineering with Computers, Vol. 36, pp. 251-269.
Hamza, M., Shahid, S., Bin Hainin, M.R. and Nashwan, M.S. (2019), “Construction labour productivity: review of factors identified”, International Journal of Construction Management, pp. 1-13, in press.
Hassan, M.R., Mamun, A.A., Hossain, M.I. and Arifuzzaman, M. (2018), “Moisture damage modeling in lime and chemically modified asphalt at nanolevel using ensemble computational intelligence”, Computational Intelligence and Neuroscience, Vol. 2018, 7525789.
He, Q., Shahabi, H., Shirzadi, A., Li, S., Chen, W., Wang, N., Chai, H., Bian, H., Ma, J. and Chen, Y. (2019), “Landslide spatial modelling using novel bivariate statistical based Naïve Bayes, RBF Classifier, and RBF Network machine learning algorithms”, Science of the Total Environment, Vol. 663, pp. 1-15.
Hodhod, O.A., Said, T.E. and Ataya, A.M. (2018), “Prediction of creep in concrete using genetic programming hybridized with ANN”, Computers and Concrete, Vol. 21 No. 5, pp. 513-523.
Jalal, M., Ramezanianpour, A.A., Pouladkhan, A.R. and Tedro, P. (2013), “Application of genetic programming (GP) and ANFIS for strength enhancement modeling of CFRP-retrofitted concrete cylinders”, Neural Computing and Applications, Vol. 23 No. 2, pp. 455-470.
Jing, L. and Hudson, J. (2002), “Numerical methods in rock mechanics”, International Journal of Rock Mechanics and Mining Sciences, Vol. 39 No. 4, pp. 409-427.
Jodaei, A., Jalal, M. and Yas, M. (2012), “Free vibration analysis of functionally graded annular plates by state-space based differential quadrature method and comparative modeling by ANN”, Composites Part B: Engineering, Vol. 43 No. 2, pp. 340-353.
Jung, J.-H., Chung, H., Kwon, Y.-S. and Lee, I.-M. (2019), “An ANN to predict ground condition ahead of tunnel face using TBM operational data”, KSCE Journal of Civil Engineering, Vol. 23, pp. 3200-3206.
Kisi, O., Azad, A., Kashi, H., Saeedian, A., Hashemi, S.A.A. and Ghorbani, S. (2019), “Modeling groundwater quality parameters using hybrid neuro-fuzzy methods”, Water Resources Management, Vol. 33 No. 2, pp. 847-861.
Lagaros, N.D. and Papadrakakis, M. (2012), “Neural network based prediction schemes of the non-linear seismic response of 3D buildings”, Advances in Engineering Software, Vol. 44 No. 1, pp. 92-115.
Lam, K., Hu, T., Thomas Ng, S., Skitmore, M. and Cheung, S.O. (2001), “A fuzzy neural network approach for contractor prequalification”, Construction Management and Economics, Vol. 19 No. 2, pp. 175-188.
Li, L., Zheng, W. and Wang, Y. (2019), “Prediction of moment redistribution in statically indeterminate reinforced concrete structures using artificial neural network and support vector regression”, Applied Sciences, Vol. 9 No. 1, p. 28.
Lian, C., Zhu, L., Zeng, Z., Su, Y., Yao, W. and Tang, H. (2018), “Constructing prediction intervals for landslide displacement using bootstrapping random vector functional link networks selective ensemble with neural networks switched”, Neurocomputing, Vol. 291, pp. 1-10.
Liao, S.-H., Chu, P.-H. and Hsiao, P.-Y. (2012), “Data mining techniques and applications–A decade review from 2000 to 2011”, Expert Systems with Applications, Vol. 39 No. 12, pp. 11303-11311.
Liu, M. and Ling, Y.Y. (2003), “Using fuzzy neural network approach to estimate contractors' markup”, Building and Environment, Vol. 38 No. 11, pp. 1303-1308.
Liu, G. and Zheng, J. (2019), “Prediction model of compressive strength development in concrete containing four kinds of gelled materials with the artificial intelligence method”, Applied Sciences, Vol. 9 No. 6, p. 1039.
Maier, H.R. and Dandy, G.C. (2000), “Neural networks for the prediction and forecasting of water resources variables: a review of modelling issues and applications”, Environmental Modelling and Software, Vol. 15 No. 1, pp. 101-124.
Majidifard, H., Jahangiri, B., Buttlar, W.G. and Alavi, A.H. (2019), “New machine learning-based prediction models for fracture energy of asphalt mixtures”, Measurement, Vol. 135, pp. 438-451.
Manzoor, B., Othman, I., Durdyev, S., Ismail, S. and Wahab, M.H. (2021), “Influence of artificial intelligence in Civil engineering toward sustainable development—a systematic literature review”, Applied System Innovation, Vol. 4 No. 3, p. 52.
Mirzahosseini, M., Najjar, Y., Alavi, A.H. and Gandomi, A.H. (2013), “ANN-Based prediction model for rutting propensity of asphalt mixtures”, 92nd Annual Meeting of Transportation Research Board, Washington DC, USA.
Mohammadhassani, M., Nezamabadi-Pour, H., Jumaat, M., Jameel, M., Hakim, S. and Zargar, M. (2013), “Application of the ANFIS model in deflection prediction of concrete deep beam”, Structural Engineering and Mechanics, Vol. 45 No. 3, pp. 319-332.
Mohammadi Bayazidi, A., Wang, G.-G., Bolandi, H., Alavi, A.H. and Gandomi, A.H. (2014), “Multigene genetic programming for estimation of elastic modulus of concrete”, Mathematical Problems in Engineering, Vol. 2014, 474289.
Momade, M.H. and Hainin, M.R. (2019), “Identifying motivational and demotivational productivity factors in Qatar construction projects”, Engineering, Technology and Applied Science Research, Vol. 9 No. 2, pp. 3945-3948.
Momade, M.H., Shahid, S., Hainin, M.R.b., Nashwan, M.S. and Tahir Umar, A. (2020), “Modelling labour productivity using SVM and RF: a comparative study on classifiers performance”, International Journal of Construction Management, pp. 1-11, in press.
Momeni, E., Armaghani, D.J., Fatemi, S.A. and Nazir, R. (2018), “Prediction of bearing capacity of thin-walled foundation: a simulation approach”, Engineering with Computers, Vol. 34 No. 2, pp. 319-327.
Murat, Y.S. and Ceylan, H. (2006), “Use of artificial neural networks for transport energy demand modeling”, Energy Policy, Vol. 34 No. 17, pp. 3165-3172.
Nauck, D. and Kruse, R. (1993), “A fuzzy neural network learning fuzzy control rules and membership functions by fuzzy error backpropagation”, IEEE international conference on neural networks, pp. 1022-1027.
Nguyen, T., Kashani, A., Ngo, T. and Bordas, S. (2019), “Deep neural network with high‐order neuron for the prediction of foamed concrete strength”, Computer‐Aided Civil and Infrastructure Engineering, Vol. 34 No. 4, pp. 316-332.
Nikoo, M., Hadzima-Nyarko, M., Karlo Nyarko, E. and Nikoo, M. (2018), “Determining the natural frequency of cantilever beams using ANN and heuristic search”, Applied Artificial Intelligence, Vol. 32 No. 3, pp. 309-334.
Notton, G., Voyant, C., Fouilloy, A., Duchaud, J.L. and Nivet, M.L. (2019), “Some applications of ANN to solar radiation estimation and forecasting for energy applications”, Applied Sciences, Vol. 9 No. 1, p. 209.
Ozoegwu, C.G. (2019), “Artificial neural network forecast of monthly mean daily global solar radiation of selected locations based on time series and month number”, Journal of Cleaner Production, Vol. 216, pp. 1-13.
Park, S. and Kim, J. (2019), “Landslide susceptibility mapping based on random Forest and boosted regression tree models, and a comparison of their performance”, Applied Sciences, Vol. 9 No. 5, p. 942.
Pham, B.T., Nguyen, M.D., Van Dao, D., Prakash, I., Ly, H.-B., Le, T.-T., Ho, L.S., Nguyen, K.T., Ngo, T.Q. and Hoang, V. (2019), “Development of artificial intelligence models for the prediction of Compression Coefficient of soil: an application of Monte Carlo sensitivity analysis”, Science of The Total Environment, Vol. 679, pp. 172-184.
Pour, S.H., Shahid, S., Chung, E.-S. and Wang, X.-J. (2018), “Model output statistics downscaling using support vector machine for the projection of spatial and temporal changes in rainfall of Bangladesh”, Atmospheric Research, Vol. 213, pp. 149-162.
Prasad, B.R., Eskandari, H. and Reddy, B.V. (2009), “Prediction of compressive strength of SCC and HPC with high volume fly ash using ANN”, Construction and Building Materials, Vol. 23 No. 1, pp. 117-128.
Rahmati, O., Falah, F., Naghibi, S.A., Biggs, T., Soltani, M., Deo, R.C., Cerdà, A., Mohammadi, F. and Bui, D.T. (2019), “Land subsidence modelling using tree-based machine learning algorithms”, Science of The Total Environment, Vol. 672, pp. 239-252.
Reddy, T.C.S. (2018), “Predicting the strength properties of slurry infiltrated fibrous concrete using artificial neural network”, Frontiers of Structural and Civil Engineering, Vol. 12 No. 4, pp. 490-503.
Roosta, M., Ghaedi, M., Daneshfar, A., Sahraei, R. and Asghari, A. (2014), “Optimization of the ultrasonic assisted removal of methylene blue by gold nanoparticles loaded on activated carbon using experimental design methodology”, Ultrasonics Sonochemistry, Vol. 21 No. 1, pp. 242-252.
Sadowski, L., Nikoo, M. and Nikoo, M. (2018), “Concrete compressive strength prediction using the imperialist competitive algorithm”, Computers and Concrete, Vol. 22 No. 4, pp. 355-363.
Saini, S., Ahuja, I.S. and Sharma, V.S. (2012), “Influence of cutting parameters on tool wear and surface roughness in hard turning of AISI H11 tool steel using ceramic tools”, International Journal of Precision Engineering and Manufacturing, Vol. 13 No. 8, pp. 1295-1302.
Sarkar, S., Vinay, S., Raj, R., Maiti, J. and Mitra, P. (2019), “Application of optimized machine learning techniques for prediction of occupational accidents”, Computers and Operations Research, Vol. 106, pp. 210-224.
Sun, W., Shi, M., Zhang, C., Zhao, J. and Song, X. (2018), “Dynamic load prediction of tunnel boring machine (TBM) based on heterogeneous in-situ data”, Automation in Construction, Vol. 92, pp. 23-34.
Takatgi, T. and Sugeno, M. (1985), “Fuzzy identification of systems and its application to model and control”, IEEE Transaction on Systems, Man, Cybernetics, Vol. 15 No. 1, pp. 116-132.
Tanyildizi, H. (2018), “Prediction of the strength properties of carbon fiber-reinforced lightweight concrete exposed to the high temperature using artificial neural network and support vector machine”, Advances in Civil Engineering, Vol. 2018, 5140610.
Tayfur, G., Erdem, T.K. and Kırca, Ö. (2013), “Strength prediction of high-strength concrete by fuzzy logic and artificial neural networks”, Journal of Materials in Civil Engineering, Vol. 26 No. 11, p. 04014079.
Tinoco, J., Gomes Correia, A., Cortez, P. and Toll, D.G. (2018), “Data-driven model for stability condition prediction of soil embankments based on visual data features”, Journal of Computing in Civil Engineering, Vol. 32 No. 4, p. 04018027.
Veeramachaneni, K., Peram, T., Mohan, C. and Osadciw, L.A. (2003), “Optimization using particle swarms with near neighbor interactions”, Genetic and Evolutionary Computation Conference, pp. 110-121.
Yang, L., Qi, C., Lin, X., Li, J. and Dong, X. (2019), “Prediction of dynamic increase factor for steel fibre reinforced concrete using a hybrid artificial intelligence model”, Engineering Structures, Vol. 189, pp. 309-318.
Yaseen, Z.M., Tran, M.T., Kim, S., Bakhshpoori, T. and Deo, R.C. (2018), “Shear strength prediction of steel fiber reinforced concrete beam using hybrid intelligence models: a new approach”, Engineering Structures, Vol. 177, pp. 244-255.
Yavuz, G. (2019), “Determining the shear strength of FRP-RC beams using soft computing and code methods”, Computers and Concrete, Vol. 23 No. 1, pp. 49-60.
Yeh, I.-C. and Lien, L.-C. (2009), “Knowledge discovery of concrete material using genetic operation trees”, Expert Systems with Applications, Vol. 36 No. 3, pp. 5807-5812.
Yurdakul, M. and Akdas, H. (2013), “Modeling uniaxial compressive strength of building stones using non-destructive test results as neural networks input parameters”, Construction and Building Materials, Vol. 47, pp. 1010-1019.
Zadeh, L. (1994), “Soft computing and fuzzy logic”, IEEE Software, Vol. 11 No. 6, pp. 48-56.
Zhang, Y. and Fang, S. (2019), “RSVRs based on feature extraction: a novel method for prediction of construction projects' costs”, KSCE Journal of Civil Engineering, Vol. 23 No. 4, pp. 1436-1441.
Zhang, P., Chen, R.-P. and Wu, H.-N. (2019), “Real-time analysis and regulation of EPB shield steering using Random Forest”, Automation in Construction, Vol. 106, p. 102860.
Zhu, Y., Wang, X., Deng, S., Zhao, M. and Ao, X. (2019), “Evaluation of curtain grouting efficiency by cloud model–based fuzzy comprehensive evaluation method”, KSCE Journal of Civil Engineering, Vol. 23, pp. 2852-2866.