In contemporary construction practices, the precision in forecasting the efficacy of concrete infrastructures plays a pivotal role in ensuring both economic viability and structural robustness. Classical methodologies often falter in encapsulating the myriad variables that intricately govern concrete performance, underscoring the exigency for progressive predictive apparatuses. This manuscript endeavors to fill this lacuna by harnessing a diverse spectrum of computational algorithms, namely linear regression, Gated Recurrent Units (GRU), Long Short-Term Memory (LSTM), and WaveNet, and subjecting them to scrupulous evaluation across multifarious metrics to discern their inherent capabilities and constraints of paramount significance, WaveNet manifested commendable prowess, registering an R^2 coefficient of 0.884 and a Root Mean Square Error (RMSE) of 23.22. Complementing the technical assessment, this study infuses an economic perspective, elucidating a cogent cost-efficiency rationale advocating the ubiquitous integration of these avant-garde predictive modalities within the construction milieu. Our interdisciplinary stratagem forges a novel conduit for synergetic research, intertwining the realms of civil engineering, computational analytics, and fiscal studies. The empirical results accentuate that machine learning paradigms not only augment predictive precision but also bolster economic viability, heralding them as indispensable instruments in the avant- garde toolkit of construction administration.

1. Ahmad A., Ahmad W., Aslam F., and Joyklad P., “Compressive Strength Prediction of Fly Ash-Based Geopolymer Concrete Via Advanced Machine Learning Techniques,” Case Studies in Construction Materials, vol. 16, pp. 00840, 2022. https://doi.org/10.1016/j.cscm.2021.e00840
2. Ahmad A., Ahmad W., Chaiyasarn K., Ostrowski K.., and Aslam F., “Prediction of Geopolymer Concrete Compressive Strength Using Novel Machine Learning Algorithms,” Polymers, vol. 13, no. 19, pp. 3389, 2021. https://doi.org/10.3390/polym13193389
3. Ahmad A., Chaiyasarn K., Farooq F., Ahmad W., and Suparp S., “Compressive Strength Prediction Via Gene Expression Programming (GEP) and Artificial Neural Network (ANN) for Concrete Containing RCA,” Buildings, vol. 11, no. 8, pp. 324, 2021. https://doi.org/10.3390/buildings11080324
4. Ahmad M., Hu J., Ahmad F., Tang X., and Amjad M., “Supervised Learning Methods for Modeling Concrete Compressive Strength Prediction at High Temperature,” Materials, vol. 14, no. 8, pp. 1983, 2021. https://doi.org/10.3390/ma14081983
5. Aljadani E., Assiri F., and Alshutayri A., “Detecting Spam Reviews in Arabic by Deep Learning,” The International Arab Journal of Information Technology, vol. 21, no. 3, pp. 495-505, 2024. DOI:10.34028/iajit/21/3/12
6. Amin M., Iqbal M., Khan K., Qadir M., and Shalabi F., “Ensemble Tree-Based Approach Towards Flexural Strength Prediction of FRP Reinforced Concrete Beams,” Polymers, vol. 14, no. 7, pp. 1303, 2022. https://doi.org/10.3390/polym14071303
7. Chen S., Zhang S., Han W., and Wu G., “Ensemble Learning Based Approach for FRP-Concrete Bond Strength Prediction,” Construction and Building Materials, vol. 302, pp. 124230, 2021. https://doi.org/10.1016/j.conbuildmat.2021.124230
8. Concrete Compressive Strength (kaggle.com), https://www.kaggle.com/datasets/elikplim/concrete-compressive-strength-data-set, Last Visited, 2024.
9. Dao D., Adeli H., Ly H., Le L., Le V., Le T., and Pham B., “A Sensitivity and Robustness Analysis of GPR and ANN for High-Performance Concrete Compressive Strength Prediction Using a Monte Carlo Simulation,” Sustainability, vol. 12, no. 3, pp. 830, 2020. https://doi.org/10.3390/su12030830
10. Dargan S., Kumar M., Ayyagari M., and Kumar G., “A Survey of Deep Learning and its Applications: A New Paradigm to Machine Learning,” Archives of Computational Methods in Engineering, vol. 27, pp. 1071-1092, 2020. https://doi.org/10.1007/s11831-019-09344-w
11. DeRousseau M., Laftchiev E., Kasprzyk J., Rajagopalan B., and Srubar W., “A Comparison of Machine Learning Methods for Predicting the Compressive Strength of Field- Placed Concrete,” Construction and Building Materials, vol. 228, pp. 116661, 2019. https://doi.org/10.1016/j.conbuildmat.2019.08.042
12. Feng D., Liu Z., Wang X., Chen Y., and Chang J., “Machine Learning-Based Compressive Strength Prediction for Concrete: An Adaptive Boosting Approach,” Construction and Building Materials, vol. 230, pp. 117000, 2020. https://doi.org/10.1016/j.conbuildmat.2019.117000
13. Gupta S. and Sharma N., “Machine Learning Driven Threat Identification to Enhance FANET Security Using Genetic Algorithm,” The International Arab Journal of Information Technology, vol. 21, no. 4, pp. 711-722, 2024. DOI:10.34028/iajit/21/4/12
14. Haque M., Chen B., Kashem A., Qureshi T., and Ahmed A., “Hybrid Intelligence Models for Compressive Strength Prediction of MPC Composites and Parametric Analysis with SHAP Algorithm,” Materials Today Communications, vol. 35, pp. 105547, 2023. https://doi.org/10.1016/j.mtcomm.2023.105547
15. Kamath M., Kumar S., and Tantri A., “Machine-Learning-Algorithm to Predict the High-Performance Concrete Compressive Strength Using Multiple Data,” Journal of Engineering, Design and Technology, vol. 22, no. 2, pp. 532-560, 2022. DOI:10.1108/JEDT-11-2021-0637
16. Latif S., “Concrete Compressive Strength Prediction Modeling Utilizing Deep Learning Long Short-Term Memory Algorithm for a Sustainable Environment,” Environmental Science and Pollution Research, vol. 28, no. 23, pp. 30294-30302, 2021. https://doi.org/10.1007/s11356-021-12877-y
17. Mechelli A. and Vieira S., Machine Learning Methods and Applications to Brain Disorder, Elsevier, 2020. https://doi.org/10.1016/B978-0-12-815739-8.00004-3
18. Oey T., Jones S., Bullard J., and Sant G., “Machine Learning Can Predict Setting Behavior and Strength Evolution of Hydrating Cement Systems,” Journal of the American Ceramic Society, vol. 103, no. 1, pp. 480-490, 2020. https://doi.org/10.1111/jace.16706
19. Qiu C., Gong S., and Gao W., “Three Artificial Intelligence-Based Solutions Predicting Concrete Slump,” UPB Scientific Bulletin, vol. 81, no. 4, pp. 3-14, 2019. https://www.scientificbulletin.upb.ro/rev_docs_arhiva/full0fc_432763.pdf
20. Rahman J., Ahmed K., Khan N., Islam K., and Mangalathu S., “Data-Driven Shear Strength Prediction of Steel Fiber Reinforced Concrete Beams Using Machine Learning Approach,” Engineering Structures, vol. 233, pp. 111743, 2021. https://doi.org/10.1016/j.engstruct.2020.111743
21. Sarker I., “Deep Learning: A Comprehensive Overview on Techniques, Taxonomy, Applications and Research Directions,” SN Computer Science, vol. 2, no. 420, pp. 1-20, 2021. https://doi.org/10.1007/s42979-021-00815-1
22. Sarla P., Priya S., Ravindran G., Shyamsunder M., Sirikonda S., Sultana F., and Reddy C., “Forecasting the Analysis of Concrete Compressive Strength, Mass and NDT Results Using ARIMA Model,” in Proceedings of the International Conference on Research in Sciences, Engineering and Technology, Warangal, 2022. https://doi.org/10.1063/5.0081945
23. Song H., Ahmad A., Farooq F., Ostrowski K., and Maślak M., “Predicting the Compressive Strength of Concrete with Fly Ash Admixture Using Machine Learning Algorithms,” Construction and Building Materials, vol. 308, pp. 125021, 2021. https://doi.org/10.1016/j.conbuildmat.2021.125021
24. Thuillard M., Advances in Computational Intelligence and Learning: Methods and Applications, Springer, 2002. https://doi.org/10.1007/978-94-010-0324-7_3
25. Tyagi A. and Abraham A., Recurrent Neural Networks: Concepts and Applications, CRC Press Tylor Francis Group, 2022. https://doi.org/10.1201/9781003307822
26. Wan Z., Xu Y., and Šavija B., “On the Use of Machine Learning Models for Prediction of Compressive Strength of Concrete: Influence of Dimensionality Reduction on the Model Performance,” Materials, vol. 14, no. 4, pp. 713, 2021. https://doi.org/10.3390/ma14040713
27. Wang X., Liu Y., and Xin H., “Bond Strength Prediction of Concrete-Encased Steel Structures Using Hybrid Machine Learning Method,” Structures, vol. 32, pp. 2279-2292, 2021. https://doi.org/10.1016/j.istruc.2021.04.018
28. Zeng Z., Zhu Z., Yao W., Wang Z., and Wang C., “Accurate Prediction of Concrete Compressive Strength Based on Explainable Features Using Deep Learning,” Construction and Building Materials, vol. 329, pp. 127082, 2022. https://doi.org/10.1016/j.conbuildmat.2022.127082