Data-Driven Civil Engineering: Applications of Artificial Intelligence, Machine Learning, and Deep Learning

Abstract

Artificial Intelligence (AI), Machine Learning (ML), and Deep Learning (DL) are a great advantage that is coming to civil engineering in ways that detail accuracy can be enhanced, many tasks automated, and predictive modeling improved. Across some of the significant subdomains, these technologies allow for eminent progress in structural health monitoring, geotechnical engineering, hydraulic systems, construction management. Currently, AI-powered models such as Artificial Neural Networks (ANNs), fuzzy logic, and evolution-based algorithms allow engineers to predict failure, optimize design, and better resource management of infrastructures. Yet, despite the potential, the adoption of AI, ML, and DL into civil engineering faces a host of challenges including data availability, computational complexity, model interpretability, integration with traditional systems, etc. High-quality, real-time data collection remains expensive and the resource-intensive nature of DL models limits their application to a large scale. In addition, the "black-box" nature of these models raises ethical and regulatory issues especially in decisions related to safety. Against this backdrop, this paper reviews current and potential applications of AI, ML, and DL in civil engineering within the framework of benefits and limitations of AI, ML, and DL, focusing on comparisons. Besides that, the paper outlines future directions regarding cloud computing, explainable AI, and regulatory frameworks. With all these changes within the scope of the discipline, AI-driven technologies will be major in safe, efficient, and sustainable infrastructure systems, provided that success is specifically dependent on addressing these key challenges.

Keywords

• Artificial Intelligence
• Deep Learning
• Machine Learning
• Civil Engineering
• Structural Engineering
• Hydraulic Engineering
• Transportation Engineering

Project Number

NA

References

1. Manzoor, B., Othman, I., Durdyev, S., Ismail, S., & Wahab, M. (2021). Influence of Artificial Intelligence in Civil Engineering toward Sustainable Development—A Systematic Literature Review. Applied System Innovation, 4(3), 52. https://doi.org/10.3390/asi4030052
2. Hwang, D., Wu, C., Lin, T., & Lin, C. (2023). The future application of artificial intelligence and telemedicine in the retina: A perspective. Taiwan Journal of Ophthalmology, 13(2), 133. https://doi.org/10.4103/tjo.tjo-d-23-00028
3. Brownjohn, J. M. W., De Stefano, A., Xu, Y., Wenzel, H., & Aktan, A. E. (2011). Vibration-based monitoring of civil infrastructure: challenges and successes. Journal of Civil Structural Health Monitoring, 1(3–4), 79–95. https://doi.org/10.1007/s13349-011-0009-5 Kumar, A., Arora, H. C., Kapoor, N. R., Kumar, K., Hadzima-Nyarko, M., & Radu, D. (2023). Machine learning intelligence to assess the shear capacity of corroded reinforced concrete beams. Scientific Reports, 13(1). https://doi.org/10.1038/s41598-023-30037-9
4. Kandrashina, M., Arsentev, D., Vinokur, A., Kolodochkin, A., Arzamazov, I., & Kozhukhov, D. (2023). Model for predicting the occurrence of soil compaction. E3S Web of Conferences, 392, 02007. https://doi.org/10.1051/e3sconf/202339202007
5. Nearing, G., Cohen, D., Dube, V., Gauch, M., Gilon, O., Harrigan, S., Hassidim, A., Klotz, D., Kratzert, F., Metzger, A., Nevo, S., Pappenberger, F., Prudhomme, C., Shalev, G., Shenzis, S., Tekalign, T. Y., Weitzner, D., & Matias, Y. (2024). Global prediction of extreme floods in ungauged watersheds. Nature, 627(8004), 559–563. https://doi.org/10.1038/s41586-024-07145-1
6. Kuusi, O., & Heinonen, S. (2022). Scenarios From Artificial Narrow Intelligence to Artificial General Intelligence—Reviewing the Results of the International Work/Technology 2050 Study. World Futures Review, 14(1), 65–79. https://doi.org/10.1177/19467567221101637
7. Radanliev, P. (2024). Artificial intelligence: reflecting on the past and looking towards the next paradigm shift. Journal of Experimental & Theoretical Artificial Intelligence, 1–18. https://doi.org/10.1080/0952813x.2024.2323042
8. Rajwar, K., Deep, K., & Das, S. (2023). An exhaustive review of the metaheuristic algorithms for search and optimization: taxonomy, applications, and open challenges. Artificial Intelligence Review, 56(11), 13187–13257. https://doi.org/10.1007/s10462-023-10470-y

9. Khurana, D., Koli, A., Khatter, K., & Singh, S. (2022). Natural language processing: state of the art, current trends and challenges. Multimedia Tools and Applications, 82(3), 3713–3744. https://doi.org/10.1007/s11042-022-13428-4
10. Dixit, R., Chinnam, R. B., & Singh, H. (2020). Artificial Intelligence and Machine Learning in Sparse/Inaccurate Data Situations. IEEE Aerospace Conference, 1–8. https://doi.org/10.1109/aero47225.2020.9172612
11. Pakzad, S. S., Roshan, N., & Ghalehnovi, M. (2023). Comparison of various machine learning algorithms used for compressive strength prediction of steel fiber-reinforced concrete. Scientific Reports, 13(1). https://doi.org/10.1038/s41598-023-30606-y
12. Wang, J., & Biljecki, F. (2022). Unsupervised machine learning in urban studies: A systematic review of applications. Cities, 129, 103925. https://doi.org/10.1016/j.cities.2022.103925
13. Liu, X., & Rastgoftar, H. (2022). Boundary Control of Traffic Congestion Modeled as a Non-stationary Stochastic Process. 2022 17th International Conference on Control, Automation, Robotics and Vision (ICARCV), 455–461. https://doi.org/10.1109/icarcv57592.2022.10004375
14. Alzubaidi, L., Zhang, J., Humaidi, A. J., Al-Dujaili, A., Duan, Y., Al-Shamma, O., Santamaría, J., Fadhel, M. A., Al-Amidie, M., & Farhan, L. (2021). Review of deep learning: concepts, CNN architectures, challenges, applications, future directions. Journal of Big Data, 8(1). https://doi.org/10.1186/s40537-021-00444-8
15. Ceylan, H., Bayrak, M. B., & Gopalakrishnan, K. (2014). Neural Networks Applications in Pavement Engineering: a recent survey. International Journal of Pavement Research and Technology, 7(6), 434–444. https://doi.org/10.6135/ijprt.org.tw/2014.7(6).434
16. Kudus, S. A., Bunnori, N. M., Basri, S. R., Shahiron, S., Jamil, M. N. M., & Noorsuhada, M. N. (2012). An Overview Current Application of Artificial Neural Network in Concrete. Advanced Materials Research, 626, 372–375. https://doi.org/10.4028/www.scientific.net/amr.626.372
17. Doroshenko, A. (2020). Applying Artificial Neural Networks In Construction. E3S Web of Conferences, 143, 01029. https://doi.org/10.1051/e3sconf/202014301029
18. [m] Bianchini, A., & Bandini, P. (2009). Prediction of Pavement Performance through Neuro-Fuzzy Reasoning. Computer-Aided Civil and Infrastructure Engineering, 25(1), 39–54. https://doi.org/10.1111/j.1467-8667.2009.00615.x
19. Alvanitopoulos, P., Andreadis, I., & Elenas, A. (2010). Neuro-fuzzy techniques for the classification of earthquake damages in buildings. Measurement, 43(6), 797–809. https://doi.org/10.1016/j.measurement.2010.02.011
20. M, I., Masi, S., Caniani, D., & S, D. (2012). Fuzzy Logic and Neuro-Fuzzy Networks for Environmental Hazard Assessment. In InTech eBooks. https://doi.org/10.5772/36141
21. Piryonesi, S. M., Nasseri, M., & Ramezani, A. (2018). Resource leveling in construction projects with activity splitting and resource constraints: a simulated annealing optimization. Canadian Journal of Civil Engineering, 46(2), 81–86. https://doi.org/10.1139/cjce-2017-0670
22. Asmone, A. S., & Chew, M. Y. L. (2018). Merging building maintainability and sustainability assessment: A multicriteria decision making approach. IOP Conference Series Earth and Environmental Science, 117, 012029. https://doi.org/10.1088/1755-1315/117/1/012029
23. Bhosale, V., Shastri, S. S., & Khandare, M. A. (2017). A Review of Genetic Algorithm used for optimizing scheduling of Resource Constraint construction projects. International Research Journal of Engineering and Technology, 4(5), 2869-2872.
24. Mohapatra, S., & Mohapatra, P. (2023). American zebra optimization algorithm for global optimization problems. Scientific Reports, 13(1). https://doi.org/10.1038/s41598-023-31876-2
25. Kentli, A. (2020). Topology Optimization Applications on Engineering Structures. In IntechOpen eBooks. https://doi.org/10.5772/intechopen.90474
26. Awe, O., Okolie, S., & Fayomi, O. (2019). Optimization of Water Distribution Systems: A Review. Journal of Physics Conference Series, 1378(2), 022068. https://doi.org/10.1088/1742-6596/1378/2/022068
27. Sitzenfrei, R., Oberascher, M., & Zischg, J. (2019). Identification of Network Patterns in Optimal Water Distribution Systems Based on Complex Network Analysis. World Environmental and Water Resources Congress 2011, 473–483. https://doi.org/10.1061/9780784482353.045
28. Baek, S., Oh, J., Woo, H., Kim, I., & Jang, S. (2023). Localization of Cracks in Concrete Structures Lacking Reference Objects and Feature Points Using an Unmanned Aerial Vehicle. Applied Sciences, 13(17), 9918. https://doi.org/10.3390/app13179918
29. Yuan, Y., Ge, Z., Su, X., Guo, X., Suo, T., Liu, Y., & Yu, Q. (2021). Crack Length Measurement Using Convolutional Neural Networks and Image Processing. Sensors, 21(17), 5894. https://doi.org/10.3390/s21175894
30. Le, T., Nguyen, V., & Le, M. V. (2021). Development of Deep Learning Model for the Recognition of Cracks on Concrete Surfaces. Applied Computational Intelligence and Soft Computing, 2021, 1–10. https://doi.org/10.1155/2021/8858545
31. Fang, W., Chen, Y., & Xue, Q. (2021). Survey on Research of RNN-Based Spatio-Temporal Sequence Prediction Algorithms. Journal on Big Data, 3(3), 97–110. https://doi.org/10.32604/jbd.2021.016993
32. Belhadi, A., Djenouri, Y., Djenouri, D., & Lin, J. C. (2020). A recurrent neural network for urban long-term traffic flow forecasting. Applied Intelligence, 50(10), 3252–3265. https://doi.org/10.1007/s10489-020-01716-1
33. Sherstinsky, A. (2020). Fundamentals of Recurrent Neural Network (RNN) and Long Short-Term Memory (LSTM) network. Physica D Nonlinear Phenomena, 404, 132306. https://doi.org/10.1016/j.physd.2019.132306
34. Luleci, F., Catbas, F. N., & Avci, O. (2022). Generative Adversarial Networks for Data Generation in Structural Health Monitoring. Frontiers in Built Environment, 8. https://doi.org/10.3389/fbuil.2022.816644
35. Zhang, C., Kim, J., Jeon, J., Xing, J., Ahn, C., Tang, P., & Cai, H. (2022). Toward Integrated Human-Machine Intelligence for Civil Engineering: An Interdisciplinary Perspective. Computing in Civil Engineering 2021. https://doi.org/10.1061/9780784483893.035
36. Zinno, R., Haghshenas, S. S., Guido, G., & VItale, A. (2022). Artificial Intelligence and Structural Health Monitoring of Bridges: A Review of the State-of-the-Art. IEEE Access, 10, 88058–88078. https://doi.org/10.1109/access.2022.3199443
37. Zhou, Q., Teng, S., Situ, Z., Liao, X., Feng, J., Chen, G., Zhang, J., & Lu, Z. (2023). A deep-learning-technique-based data-driven model for accurate and rapid flood predictions in temporal and spatial dimensions. Hydrology and Earth System Sciences, 27(9), 1791–1808. https://doi.org/10.5194/hess-27-1791-2023
38. Savino, P., & Tondolo, F. (2022). Civil infrastructure defect assessment using pixel-wise segmentation based on deep learning. Journal of Civil Structural Health Monitoring, 13(1), 35–48. https://doi.org/10.1007/s13349-022-00618-9
39. Datta, S. D., Islam, M., Sobuz, M. H. R., Ahmed, S., & Kar, M. (2024). Artificial intelligence and machine learning applications in the project lifecycle of the construction industry: A comprehensive review. Heliyon, e26888. https://doi.org/10.1016/j.heliyon.2024.e26888
40. Santaniello, P., & Russo, P. (2023). Bridge Damage Identification Using Deep Neural Networks on Time–Frequency Signals Representation. Sensors, 23(13), 6152. https://doi.org/10.3390/s23136152
41. Abadi, H. H. N., & Modarres, M. (2023). Predicting System Degradation with a Guided Neural Network Approach. Sensors, 23(14), 6346. https://doi.org/10.3390/s23146346
42. Huynh, T. Q., Nguyen, T. T., & Nguyen, H. (2022). Base resistance of super-large and long piles in soft soil: performance of artificial neural network model and field implications. Acta Geotechnica, 18(5), 2755–2775. https://doi.org/10.1007/s11440-022-01736-w
43. Yang, C., & Jiang, Z. (2022). A Discrete-Time Model-Based Method for Predicting Settlement of Geotechnical Foundations in Buildings. Mobile Information Systems, 2022, 1–7. https://doi.org/10.1155/2022/5631634
44. Lee, S., & Le, T. H. M. (2023). Feasibility of Sustainable Asphalt Concrete Materials Utilizing Waste Plastic Aggregate, Epoxy Resin, and Magnesium-Based Additive. Polymers, 15(15), 3293. https://doi.org/10.3390/polym15153293
45. Sesugh, T., Onyia, M., & Fidelis, O. (2024). Predicting the compressive strength of self-compacting concrete using artificial intelligence techniques: A review. Turkish Journal of Engineering, 8(3), 537-550. https://doi.org/10.31127/tuje.1422225
46. Sun, G., Hasanipanah, M., Amnieh, H. B., & Foong, L. K. (2020). Feasibility of indirect measurement of bearing capacity of driven piles based on a computational intelligence technique. Measurement, 156, 107577. https://doi.org/10.1016/j.measurement.2020.107577
47. Ofrikhter, I. V., & Ponomarev, A. B. (2021). Estimation of load-set behavior of driven concrete piles using artificial neural network and cone penetration test. Journal of Physics Conference Series, 1928(1), 012055. https://doi.org/10.1088/1742-6596/1928/1/012055
48. Pham, T. A., Ly, H., Tran, V. Q., Van Giap, L., Vu, H. T., & Duong, H. T. (2020). Prediction of Pile Axial Bearing Capacity Using Artificial Neural Network and Random Forest. Applied Sciences, 10(5), 1871. https://doi.org/10.3390/app10051871
49. Abioye, E. A., Hensel, O., Esau, T. J., Elijah, O., Abidin, M. S. Z., Ayobami, A. S., Yerima, O., & Nasirahmadi, A. (2022). Precision Irrigation Management Using Machine Learning and Digital Farming Solutions. AgriEngineering, 4(1), 70–103. https://doi.org/10.3390/agriengineering4010006
50. Uzer, A. U. (2024). Efficient prediction of compressive strength in geotechnical engineering using artificial neural networks. Turkish Journal of Engineering, 8(3), 457-468. https://doi.org/10.31127/tuje.1415931
51. Othman, M. M. (2023). Modeling of daily groundwater level using deep learning neural networks. Turkish Journal of Engineering, 7(4), 331-337. https://doi.org/10.31127/tuje.1169908
52. Mogaraju, J. K. (2023). Application of machine learning algorithms in the investigation of groundwater quality parameters over YSR district, India. Turkish Journal of Engineering, 7(1), 64-72. https://doi.org/10.31127/tuje.1032314
53. Rajwar, K., Deep, K., & Das, S. (2023). An exhaustive review of the metaheuristic algorithms for search and optimization: taxonomy, applications, and open challenges. Artificial Intelligence Review, 56(11), 13187–13257. https://doi.org/10.1007/s10462-023-10470-y
54. Poudel, S., Arafat, M. Y., & Moh, S. (2023). Bio-Inspired Optimization-Based Path Planning Algorithms in Unmanned Aerial Vehicles: A Survey. Sensors, 23(6), 3051. https://doi.org/10.3390/s23063051 Alkhraisat, H., Dalbah, L. M., Al-Betar, M. A., Awadallah, M. A., Assaleh, K., & Deriche, M. (2023). Size Optimization of Truss Structures Using Improved Grey Wolf Optimizer. IEEE Access, 11, 13383–13397. https://doi.org/10.1109/access.2023.3243164
55. Wang, C., Li, D., Kaewniam, P., Wang, J., & Hababi, T. A. (2023). An ED-PSO model updating algorithm for structure health monitoring of beam-like structures. Journal of Measurements in Engineering, 11(3), 358–372. https://doi.org/10.21595/jme.2023.23417
56. Ruff, L., Kauffmann, J. R., Vandermeulen, R. A., Montavon, G., Samek, W., Kloft, M., Dietterich, T. G., & Muller, K. (2021). A Unifying Review of Deep and Shallow Anomaly Detection. Proceedings of the IEEE, 109(5), 756–795. https://doi.org/10.1109/jproc.2021.3052449
57. Alzubaidi, L., Zhang, J., Humaidi, A. J., Al-Dujaili, A., Duan, Y., Al-Shamma, O., Santamaría, J., Fadhel, M. A., Al-Amidie, M., & Farhan, L. (2021). Review of deep learning: concepts, CNN architectures, challenges, applications, future directions. Journal of Big Data, 8(1). https://doi.org/10.1186/s40537-021-00444-8
58. Gwon, G., Lee, J. H., Kim, I., & Jung, H. (2023). CNN-Based Image Quality Classification Considering Quality Degradation in Bridge Inspection Using an Unmanned Aerial Vehicle. IEEE Access, 11, 22096–22113. https://doi.org/10.1109/access.2023.3238204
59. Kim, S., Jang, J., & Na, W. S. (2021). Automatic Creation of Heuristic-Based Truck Movement Paths for Construction Equipment Control. Applied Sciences, 11(13), 5837. https://doi.org/10.3390/app11135837
60. Martinez, P., Barkokebas, B., Hamzeh, F., Al-Hussein, M., & Ahmad, R. (2021). A vision-based approach for automatic progress tracking of floor paneling in offsite construction facilities. Automation in Construction, 125, 103620. https://doi.org/10.1016/j.autcon.2021.103620
61. Luo, X., Zhu, C., Zhang, D., & Li, Q. (2023). Dynamic Graph Convolutional Network with Attention Fusion for Traffic Flow Prediction. In Frontiers in artificial intelligence and applications. https://doi.org/10.3233/faia230446 Liu, Y., Lyu, C., Zhang, Y., Liu, Z., Yu, W., & Qu, X. (2021). DeepTSP: Deep traffic state prediction model based on large-scale empirical data. Communications in Transportation Research, 1, 100012. https://doi.org/10.1016/j.commtr.2021.100012
62. Chang, C. M., Salinas, G. T., Gamero, T. S., Schroeder, S., Canchanya, M. a. V., & Mahnaz, S. L. (2023). An Infrastructure Management Humanistic Approach for Smart Cities Development, Evolution, and Sustainability. Infrastructures, 8(9), 127. https://doi.org/10.3390/infrastructures8090127
63. Nübel, K., Bühler, M. M., & Jelinek, T. (2021). Federated Digital Platforms: Value Chain Integration for Sustainable Infrastructure Planning and Delivery. Sustainability, 13(16), 8996. https://doi.org/10.3390/su13168996
64. Kicinger, R., Arciszewski, T., & De Jong, K. (2005). Evolutionary computation and structural design: A survey of the state-of-the-art. Computers & Structures, 83(23–24), 1943–1978. https://doi.org/10.1016/j.compstruc.2005.03.002
65. Greiner, D., Periaux, J., Quagliarella, D., Magalhaes-Mendes, J., & Galván, B. (2018). Evolutionary Algorithms and Metaheuristics: Applications in Engineering Design and Optimization. Mathematical Problems in Engineering, 2018, 1–4. https://doi.org/10.1155/2018/2793762
66. Gomes, C., Parente, M., Azenha, M., & Lino, J. C. (2018). An integrated framework for multi-criteria optimization of thin concrete shells at early design stages. Advanced Engineering Informatics, 38, 330–342. https://doi.org/10.1016/j.aei.2018.08.003
67. Khodabandehlou, H., Pekcan, G., & Fadali, M. S. (2018). Vibration‐based structural condition assessment using convolution neural networks. Structural Control and Health Monitoring, e2308. https://doi.org/10.1002/stc.2308
68. Avci, O., Abdeljaber, O., Kiranyaz, S., Hussein, M., Gabbouj, M., & Inman, D. J. (2020). A review of vibration-based damage detection in civil structures: From traditional methods to Machine Learning and Deep Learning applications. Mechanical Systems and Signal Processing, 147, 107077. https://doi.org/10.1016/j.ymssp.2020.107077 Neves, A. C., González, I., Leander, J., & Karoumi, R. (2017). Structural health monitoring of bridges: a model-free ANN-based approach to damage detection. Journal of Civil Structural Health Monitoring, 7(5), 689–702. https://doi.org/10.1007/s13349-017-0252-5
69. Leung, A. K., Liu, J., & Jiang, Z. (2023). When nature meets technology: AI-informed discovery of soil-water-root physical interaction. E3S Web of Conferences, 382, 21001. https://doi.org/10.1051/e3sconf/202338221001
70. Provenzano, P. (2003). A Fuzzy‐Neural Network Method for Modeling Uncertainties in Soil‐Structure Interaction Problems. Computer-Aided Civil and Infrastructure Engineering, 18(6), 391–411. https://doi.org/10.1111/1467-8667.00326
71. Deris, A. M., Solemon, B., & Omar, R. C. (2021). A comparative study of supervised machine learning approaches for slope failure production. E3S Web of Conferences, 325, 01001. https://doi.org/10.1051/e3sconf/202132501001
72. Adeyemo, J., & Stretch, D. (2017). Review of hybrid evolutionary algorithms for optimizing a reservoir. South African Journal of Chemical Engineering, 25, 22–31. https://doi.org/10.1016/j.sajce.2017.11.004
73. Lai, G., Chang, W., Yang, Y., & Liu, H. (2018). Modeling Long- and Short-Term Temporal Patterns with Deep Neural Networks. In International ACM SIGIR Conference on Research & Development in Information Retrieval (41st ed.). ACM. https://doi.org/10.1145/3209978.3210006 Wei, J., Luo, X., Huang, H., Liao, W., Lei, X., Zhao, J., & Wang, H. (2023). Enable high-resolution, real-time ensemble simulation and data assimilation of flood inundation using distributed GPU parallelization. Journal of Hydrology, 619, 129277. https://doi.org/10.1016/j.jhydrol.2023.129277
74. Olariu, S. (2021). Vehicular Crowdsourcing for Congestion Support in Smart Cities. Smart Cities, 4(2), 662–685. https://doi.org/10.3390/smartcities4020034
75. Wang, J., Jiang, S., Qiu, Y., Zhang, Y., Ying, J., & Du, Y. (2021). Traffic Signal Optimization under Connected-Vehicle Environment: An Overview. Journal of Advanced Transportation, 2021, 1–16. https://doi.org/10.1155/2021/3584569
76. Huang, Y., Jafari, M., & Jin, P. J. (2022). Driving Safety Prediction and Safe Route Mapping Using In-Vehicle and Roadside Data. SSRN Electronic Journal. https://doi.org/10.2139/ssrn.4135994 Ruther, R., Klos, A., Rosenbaum, M., & Schiffmann, W. (2021). Traffic Flow Forecast of Road Networks With Recurrent Neural Networks. In International Symposium on Computer Science and Intelligent Controls (ISCSIC) (Vol. 79, pp. 31–38). IEEE. https://doi.org/10.1109/iscsic54682.2021.00018
77. Belhadi, A., Djenouri, Y., Djenouri, D., & Lin, J. C. (2020). A recurrent neural network for urban long-term traffic flow forecasting. Applied Intelligence, 50(10), 3252–3265. https://doi.org/10.1007/s10489-020-01716-1
78. Liu, S., Li, Z., & Li, H. (2020). Research on short-term traffic flow prediction model based on RNN-LSTM. IOP Conference Series Materials Science and Engineering, 806(1), 012017. https://doi.org/10.1088/1757-899x/806/1/012017
79. Xin-Chun, C., Peng, D., Nai-Qing, Y., & Meng-Xue, B. (2021). Study on Discrete Manufacturing Quality Control Technology Based on Big Data and Pattern Recognition. Mathematical Problems in Engineering, 2021, 1–10. https://doi.org/10.1155/2021/8847094
80. Slaton, T., Hernandez, C., & Akhavian, R. (2020). Construction activity recognition with convolutional recurrent networks. Automation in Construction, 113, 103138. https://doi.org/10.1016/j.autcon.2020.103138
81. Bouabdallaoui, Y., Lafhaj, Z., Yim, P., Ducoulombier, L., & Bennadji, B. (2021). Predictive Maintenance in Building Facilities: A Machine Learning-Based Approach. Sensors, 21(4), 1044. https://doi.org/10.3390/s21041044
82. Wang, Y., Ning, X., Zhen, D., Yong, W., & Zhang, H. (2023). Research on Construction Project Cost Prediction Model Based on Recurrent Neural Network. SHS Web of Conferences, 170, 02009. https://doi.org/10.1051/shsconf/202317002009
83. Saikai, Y., Peake, A., & Chenu, K. (2023). Deep reinforcement learning for irrigation scheduling using high-dimensional sensor feedback. PLOS Water, 2(9), e0000169. https://doi.org/10.1371/journal.pwat.0000169
84. Du, W., & Ding, X. (2024). Optimizing Irrigation Efficiency using Deep Reinforcement Learning in the Field. ACM Transactions on Sensor Networks. https://doi.org/10.1145/3662182 Kühnert, C., Gonuguntla, N. M., Krieg, H., Nowak, D., & Thomas, J. A. (2021). Application of LSTM Networks for Water Demand Prediction in Optimal Pump Control. Water, 13(5), 644. https://doi.org/10.3390/w13050644
85. Kaya, Y., Şenol, H. İ., Yiğit, A. Y., & Yakar, M. (2023). Car detection from very high-resolution UAV images using deep learning algorithms. Photogrammetric Engineering & Remote Sensing, 89(2), 117-123.
86. Uray, E. (2022). Gabion structures and retaining walls design criteria. Advanced Engineering Science, 2, 127–134. Retrieved from https://publish.mersin.edu.tr/index.php/ades/article/view/514
87. Karasu , V. ., Yalçın, C. . ., & Uras , Y. . (2023). Geology, geochemistry and isotope compositions of carbonate-hosted barite deposit in Koçaşlı (Gülnar-Mersin, Türkiye). Engineering Applications, 2(1), 75–83. Retrieved from https://publish.mersin.edu.tr/index.php/enap/article/view/852
88. Zela, K., & Saliaj, L. (2023). Forecasting through neural networks: Bitcoin price prediction. Engineering Applications, 2(3), 218–224. Retrieved from https://publish.mersin.edu.tr/index.php/enap/article/view/874
89. Hajderi , A. ., Bozo , L. ., & Basholli , F. . (2024). The impact of alternative fuel on diesel in reducing of pollution from vehicles. Advanced Engineering Science, 4, 15–24. Retrieved from https://publish.mersin.edu.tr/index.php/ades/article/view/1497
90. Hajderi , A. ., & Bozo, L., (2024). Health risks from air pollution from vehicles in the city of Tirana. Engineering Applications, 3(1), 68–77. Retrieved from https://publish.mersin.edu.tr/index.php/enap/article/view/1182
91. Bozkurt, Özlem. (2023). Formation of entrance doors in traditional wooden buildings in the city center of Tekirdağ. Advanced Engineering Science, 3, 103–111. Retrieved from https://publish.mersin.edu.tr/index.php/ades/article/view/961
92. Çimen, Ömür ., & Keskin, S. N. (2024). Investigation of the effect of Isparta pumice on the unconfined compressive strength and swelling pressure of clay. Advanced Engineering Science, 4, 113–119. Retrieved from https://publish.mersin.edu.tr/index.php/ades/article/view/1568
93. Karakurt , A. B. ., & Ertuğrul , Özgür L. . (2023). Effect of rice husk ash addition on the consolidation characteristics of cohesive soils. Engineering Applications, 2(1), 7–15. Retrieved from https://publish.mersin.edu.tr/index.php/enap/article/view/848