The DOI matches its Crossref record; 55 of the 55 references it cites were found in a registry or an index.
Machine learning methods in civil engineering: a systematic review
Open accessAbstract
Machine learning has found applications across a range of commercial enterprises. One of the exciting industries impacted by AI has been civil engineering. The aim of this paper is to review recent developments in AI as they relate to civil engineering. We highlight potential applications as well as the risks.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
The source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
In the citing paper
“The”
From Artificial intelligence in civil engineering: emerging applications and opportunities (2025) · cites this paper as Kamolov et al., 2024
Experimental — accuracy not yet measured
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
New Era of Artificial Intelligence in Education: Towards a Sustainable Multifaceted Revolution
Kamalov, F., Santandreu Calonge, D., & Gurrib, I. (2023) · Sustainability · doi:10.3390/su151612451 · Cited by 1,089
Applications of artificial intelligence for disaster management
Sun, W., Bocchini, P., & Davison, B. D. (2020) · Natural Hazards · doi:10.1007/s11069-020-04124-3 · Cited by 491
Machine Learning in Disaster Management: Recent Developments in Methods and Applications
Linardos, V., Drakaki, M., Tzionas, P., & Karnavas, Y. (2022) · Machine Learning and Knowledge Extraction · doi:10.3390/make4020020 · Cited by 319
The role of artificial neural network and machine learning in utilizing spatial information
Goel, A., Kumar, A., & Kumar, A. (2022) · Spatial Information Research · doi:10.1007/s41324-022-00494-x · Cited by 304
Artificial Intelligence in Civil Engineering
Lu, P., Chen, S., & Zheng, Y. (2012) · Mathematical Problems in Engineering · doi:10.1155/2012/145974 · Cited by 265
A new computational intelligence approach to detect autistic features for autism screening
Thabtah, F., Kamalov, F., & Rajab, K. D. (2018) · International Journal of Medical Informatics · doi:10.1016/j.ijmedinf.2018.06.009 · Cited by 209
Kamalov, F., Pourghebleh, B., Gheisari, M., Liu, Y., & Moussa, S. (2023) · Sustainability · doi:10.3390/su15043317 · Cited by 163
Tan, L., Guo, J., Mohanarajah, S., & Zhou, K. (2020) · Natural Hazards · doi:10.1007/s11069-020-04429-3 · Cited by 130
Fairness and accountability of AI in disaster risk management: Opportunities and challenges
Gevaert, C. M., Carman, M., Rosman, B., Georgiadou, Y., & Soden, R. (2021) · Patterns · doi:10.1016/j.patter.2021.100363 · Cited by 127
Forecasting significant stock price changes using neural networks
Kamalov, F. (2020) · Neural Computing and Applications · doi:10.1007/s00521-020-04942-3 · Cited by 125
Machine Learning in Structural Design: An Opinionated Review
Málaga‐Chuquitaype, C. (2022) · Frontiers in Built Environment · doi:10.3389/fbuil.2022.815717 · Cited by 102
Least Loss: A simplified filter method for feature selection
Thabtah, F., Kamalov, F., Hammoud, S., et al. (2020) · Information Sciences · doi:10.1016/j.ins.2020.05.017 · Cited by 81
Artificial Intelligence for Natural Hazards Risk Analysis: Potential, Challenges, and Research Needs
Guikema, S. D. (2020) · Risk Analysis · doi:10.1111/risa.13476 · Cited by 79
State-of-the-art AI-based computational analysis in civil engineering
Wang, C., Song, L.-H., Yuan, Z., & Fan, J. (2023) · Journal of Industrial Information Integration · doi:10.1016/j.jii.2023.100470 · Cited by 69
Predicting bitcoin price movements using sentiment analysis: a machine learning approach
Gurrib, I., & Kamalov, F. (2021) · Studies in Economics and Finance · doi:10.1108/sef-07-2021-0293 · Cited by 68
Mondal, T. G., & Chen, G. (2022) · Frontiers in Built Environment · doi:10.3389/fbuil.2022.1007886 · Cited by 68
Harle, S. M. (2024) · Asian Journal of Civil Engineering · doi:10.1007/s42107-023-00760-9 · Cited by 65
CHALLENGES OF ARTIFICIAL INTELLIGENCE APPLICATION FOR DISASTER RISK MANAGEMENT
Velev, D. G., & Златева, П. (2023) · The international archives of the photogrammetry, remote sensing and spatial information sciences/International archives of the photogrammetry, remote sensing and spatial information sciences · doi:10.5194/isprs-archives-xlviii-m-1-2023-387-2023 · Cited by 63
Natural Disaster Application on Big Data and Machine Learning: A Review
Arinta, R. R., & Emanuel Andi, W. R. (2019) · International Conference on Information Technology, Information Systems and Electrical Engineering (ICITISEE) · doi:10.1109/icitisee48480.2019.9003984 · Cited by 62
Kraus, M. A., & Drass, M. (2020) · Glass Structures & Engineering · doi:10.1007/s40940-020-00132-8 · Cited by 61
Feature Selection in Imbalanced Data
Kamalov, F., Thabtah, F., & Leung, H. (2022) · Annals of Data Science · doi:10.1007/s40745-021-00366-5 · Cited by 61
Predictive maintenance applications for machine learning
Cline, B., Niculescu, R. S., Huffman, D. L., et al. (2017) · Proceedings, annual Reliability and Maintainability Symposium/Proceedings. Annual Reliability and Maintainability Symposium · doi:10.1109/ram.2017.7889679 · Cited by 59
Deep learning for Covid-19 forecasting: State-of-the-art review.
Kamalov, F., Rajab, K. D., Cherukuri, A. K., Elnagar, A., & Safaraliev, M. (2022) · Neurocomputing · doi:10.1016/j.neucom.2022.09.005 · Cited by 55
Financial Forecasting with Machine Learning: Price Vs Return
(2021) · doi:10.3844/jcssp.2021.251.264 · Cited by 53
Stock price forecast with deep learning
Kamalov, F., Smail, L., & Gurrib, I. (2020) · International Conference on Decision Aid Sciences and Applications (DASA) · doi:10.1109/dasa51403.2020.9317260 · Cited by 44
Feature selection for intrusion detection systems
Kamalov, F., Moussa, S., Zgheib, R., et al. (2020) · International Symposium on Computational Intelligence and Design · doi:10.1109/iscid51228.2020.00065 · Cited by 37
Forecasting COVID-19: Vector Autoregression-Based Model
Rajab, K. D., Kamalov, F., & Cherukuri, A. K. (2022) · Arabian Journal for Science and Engineering · doi:10.1007/s13369-021-06526-2 · Cited by 35
An Approach-Driven: Use of Artificial Intelligence and Its Applications in Civil Engineering
Kumar, A., & Mor, N. (2021) · Studies in big data · doi:10.1007/978-981-33-6400-4_10 · Cited by 34
Gurrib, I., & Kamalov, F. (2019) · Macroeconomics and Finance in Emerging Market Economies · doi:10.1080/17520843.2019.1574852 · Cited by 32
Artificial Intelligence for Natural Disaster Management
Pang, G. (2022) · IEEE Intelligent Systems · doi:10.1109/mis.2022.3220061 · Cited by 31
Powering Electricity Forecasting with Transfer Learning
Kamalov, F., Sulieman, H., Moussa, S., Reyes, J. A., & Safaraliev, M. (2024) · Energies · doi:10.3390/en17030626 · Cited by 30
Sensitivity Analysis for Feature Selection
Kamalov, F. (2018) · International Conference on Machine Learning and Applications · doi:10.1109/icmla.2018.00238 · Cited by 23
Forecasting with Deep Learning: S&P 500 index
Kamalov, F., Smail, L., & Gurrib, I. (2020) · International Symposium on Computational Intelligence and Design · doi:10.1109/iscid51228.2020.00102 · Cited by 23
Demertzis, K., Demertzis, S., & Iliadis, L. (2023) · Applied Sciences · doi:10.3390/app13063380 · Cited by 23
Orthogonal variance decomposition based feature selection
Kamalov, F. (2021) · Expert Systems with Applications · doi:10.1016/j.eswa.2021.115191 · Cited by 21
Zgheib, R., Chahbandarian, G., Kamalov, F., Messiry, H. E., & Al-Gindy, A. (2023) · Neurocomputing · doi:10.1016/j.neucom.2023.01.007 · Cited by 21
Leveraging Artificial Intelligence in Engineering, Management, and Safety of Infrastructure
Naser, M. Z. (2022) · CRC Press eBooks · doi:10.1201/9780367823467 · Cited by 20
Sustainability-driven structural design using artificial intelligence
Mathern, A., Ek, K. H., & Rempling, R. (2019) · Report · doi:10.2749/newyork.2019.1057 · Cited by 18
Nested ensemble selection: An effective hybrid feature selection method
Kamalov, F., Sulieman, H., Moussa, S., et al. (2023) · Heliyon · doi:10.1016/j.heliyon.2023.e19686 · Cited by 18
Present and Future of Artificial Intelligence in Disaster Management
Abid, S. K., Chan, S. W., Sulaiman, N., et al. (2023) · International Conference on Engineering Management of Communication and Technology (EMCTECH) · doi:10.1109/emctech58502.2023.10296991 · Cited by 18
Toward Integrated Human-Machine Intelligence for Civil Engineering: An Interdisciplinary Perspective
Zhang, C., Kim, J., Jeon, J., Xing, J., Ahn, C. R., Tang, P., & Cai, H. (2022) · ASCE International Conference on Computing in Civil Engineering (i3CE) · doi:10.1061/9780784483893.035 · Cited by 17
Performance of Equity Investments in Sustainable Environmental Markets
Gurrib, I., Kamalov, F., Starkova, O., Makki, A., Mirchandani, A., & Gupta, N. (2023) · Sustainability · doi:10.3390/su15097453 · Cited by 12
Artificial Intelligence and Machine Learning Techniques for Civil Engineering
Plevris, V., Ahmad, A., & Lagaros, N. D. (2023) · Advances in civil and industrial engineering book series · doi:10.4018/978-1-6684-5643-9 · Cited by 12
Machine learning-based forecasting of significant daily returns in foreign exchange markets
Kamalov, F., & Gurrib, I. (2022) · International Journal of Business Intelligence and Data Mining · doi:10.1504/ijbidm.2022.126505 · Cited by 11
Extraction of SIFT Features for Identifying Disaster Hit areas using Machine Learning Techniques
Kumar, V., Al‐Emran, A., Gupta, S. K., et al. (2022) · International Conference on Knowledge Engineering and Communication Systems (ICKECS) · doi:10.1109/ickecs56523.2022.10060037 · Cited by 11
XyGen: Synthetic data generator for feature selection
Kamalov, F., Elnaffar, S., Sulieman, H., et al. (2023) · Software Impacts · doi:10.1016/j.simpa.2023.100485 · Cited by 9
Forecasting Covid-19: SARMA-ARCH approach
Kamalov, F., & Thabtah, F. (2021) · Health and Technology · doi:10.1007/s12553-021-00587-x · Cited by 8
Naser, M. Z., & Ross, B. E. (2022) · Civil Engineering and Environmental Systems · doi:10.1080/10286608.2022.2049257 · Cited by 8
Drivers of the next-minute Bitcoin price using sparse regressions
Gurrib, I., Kamalov, F., Starkova, O., et al. (2023) · Studies in Economics and Finance · doi:10.1108/sef-04-2023-0182 · Cited by 7
Bitcoin Price Forecasting: Linear Discriminant Analysis with Sentiment Evaluation
Gurrib, I., Kamalov, F., & Smail, L. (2021) · doi:10.1145/3485557.3485561 · Cited by 6
AI (artificial intelligence)-assisted planning within emergency management operations
James Adams, B., Hasan, M., & Jacob Thorp, B. (2022) · Journal of Emergency Management · doi:10.5055/jem.0622 · Cited by 6
Zaman, N. T., Apostolou, E., Li, Y., et al. (2022) · Proceedings, annual Reliability and Maintainability Symposium/Proceedings. Annual Reliability and Maintainability Symposium · doi:10.1109/rams51457.2022.9893970 · Cited by 2
Opportunities in Civil Projects with Artificial Intelligence
Kjellgren, A., Kettil, P., Karlsson, M., & Rempling, R. (2023) · Report · doi:10.2749/istanbul.2023.0022 · Cited by 2
Opportunities in Civil Projects with Artificial Intelligence
Kjellgren, A., Kettil, P., Rempling, R., & Karlsson, M. (2023) · Report · doi:10.2749/newdelhi.2023.1207 · Cited by 2
Role of Artificial Intelligence in improving structural performance: A Review
Shandilya, T., Agrawal, V., & Gupta, R. (2022) · IOP Conference Series Earth and Environmental Science · doi:10.1088/1755-1315/1086/1/012002 · Cited by 1
Flagged first, then by citations
Research works printed but not linked: 8
- Yang, X., Xiaowei, J., & Hui, L. (2022). State-of-the-art and prospect of intelligent science and technology in civil engineering. Journal of Building Structures, 43(9), 23.
- Bao, Y., & Li, H. (2019). Artificial intelligence for civil engineering. Chin Civil Eng J, 52(5), 1-11.
- Singh, D. P., Srivastava, D., & Tiwari, A. K. (2023). A Critical Review on Sustainable Structural Optimization using Computational Approach.
- Zhang, C., Kim, J., Jeon, J., Xing, J., Ahn, C., Tang, P., & Cai, H. (2021). To- ward integrated human-machine intelligence for civil engineering: An interdisciplinary perspective. Computing in Civil Engineering 2021, 279-286. https://doi.org/10.1061/ 9780784483893.035 · DOI
- Liu, H., Zhang, F., Chen, Z., & Wang. (2024). Applied research status and prospects of artificial intelligence in civil engineering field. Journal of Civil and Environmental Engineer- ing.
- Singh, D. P., Srivastava, D., & Tiwari, A. K. (2023). A Critical Review on Sustainable Structural Optimization using Computational Approach.
- Shandilya, T., Agrawal, V., & Gupta, R. (2022, September). Role of Artificial Intelligence in improving structural performance: A Review. In IOP Conference Series: Earth and Environmental Science (Vol. 1086, No. 1, p. 012002). IOP Publishing. https://doi.org/ 10.1088/1755-1315/1086/1/012002 · DOI
- Linardos, V., Drakaki, M., Tzionas, P., & Karnavas, Y. L. (2022). Machine learning in disaster management: recent developments in methods and applications. Machine Learning and Knowledge Extraction, 4(2). https://doi.org/10.3390/ · DOI · Paper page
Cited by
Sorted by citations
Artificial intelligence in civil engineering: emerging applications and opportunities
Nyokum, T., & Tamut, Y. (2025) · Frontiers in Built Environment · doi:10.3389/fbuil.2025.1622873 · Cited by 33
Metadata
- Authors
- Kamolov, S.
- Affiliations
- Technological University of Tajikistan (TJ)
- Corresponding author
- Kamolov, Saidjon
- Venue
- Annals of Mathematics and Computer Science
- Publisher
- Vertex Academic Press
- Published
- 2024-02-17
- Volume
- 21
- Pages
- 181-191
- ISSN
- 2789-7206 (ISSN-L)
- OpenAlex
- W4391918024
- Cited by
- 12
- References
- 55
- Citations by year
- 2026: 6 · 2025: 6
- FWCI
- 0.41
- Type
- journal article
- Language
- en
- Open access
- diamond
- doi.org
- Licence
- Locations
- Annals of Mathematics and Computer Science (publishedVersion, cc-by-nc-nd)
- Topics
- Physical Sciences › Engineering › Civil and Structural Engineering › Infrastructure Maintenance and Monitoring
- Physical Sciences › Engineering › Civil and Structural Engineering › Civil and Structural Engineering Research
- Physical Sciences › Computer Science › Artificial Intelligence › Artificial Intelligence Applications
- Keywords
- machine learning, artificial intelligence, civil engineering applications, AI risks, civil engineering industry
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
