AI-based prediction for the strength, cost, and sustainability of eggshell and date palm ash-blended concrete
Source abstract
Abstract
Abstract Eggshell powder (ESP) and date palm ash (DPA) are increasingly used as sustainable cement substitutes in cementitious composites. This study used multi-expression programming (MEP) to develop prediction models due to its advantage of yielding model equations. The attributes of ESP and DPA-modified concrete chosen for modeling include compressive strength (C-S), eco-strength (E-C-S), and cost-strength ratio (C-S-R). Hyperparameters in MEP were fine-tuned to get the maximum accuracy for predictions. The models were validated using R 2 and statistical checks and analyzing the variance among predictions and real values. The MEP models were noted to be exact in estimating C-S, C-S-R, and E-C-S with an R 2 of 95, 93, and 92%, respectively, indicating good agreement with actual data. Additionally, the ±20% index analysis indicated that all values fall within the acceptable range, validating the model’s reliability. The mathematical expression-based MEP prediction models developed in this study can be applied to future C-S, C-S-R, and E-C-S predictions in ESP–DPA-modified concrete. These models are designed to operate with a predetermined set of input parameters and are incompatible with a variable set of inputs. Additionally, it is imperative to maintain consistency in the units of inputs to obtain precise predictions from the constructed models.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Innovative concrete reinforcement materials · Natural Fiber Reinforced Composites · Innovations in Concrete and Construction Materials
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
Architectural Engineering College, Jinhua University of Vocational Technology , Jinhua , 321000 , China
Wenzhou Mass Transit Railway Investment Group Co., Ltd. , Wenzhou , 325000 , China
Department of Civil Engineering, College of Engineering, University of Hafr Al Batin , Hafr Al Batin , 39524 , Saudi Arabia
Department of Electrical Engineering, College of Engineering, University of Hafr Al Batin , Hafr Al Batin , 39524 , Saudi Arabia
College of Computer Science and Engineering, University of Hafr Al Batin , Hafr Al Batin , 39524 , Saudi Arabia
Department of Civil Engineering, Faculty of Engineering, Beni-Suef University , Beni-Suef , Egypt
References (88)
Show source-provided references
1. Nasser, R. A., M. Z. M. Salem, S. Hiziroglu, H. A. Al-Mefarrej, A. S. Mohareb, M. Alam, et al. Chemical analysis of different parts of date palm (Phoenix dactylifera L.) using ultimate, proximate and thermo-gravimetric techniques for energy production. Energies, Vol. 9, 2016, id. 374.10.3390/en9050374 ↗
2. Adamu, M., H. Alanazi, Y. E. Ibrahim, and M. Abdellatief. Mechanical, microstructural characteristics and sustainability analysis of concrete incorporating date palm ash and eggshell powder as ternary blends cementitious materials. Construction and Building Materials, Vol. 411, 2024, id. 134753.10.1016/j.conbuildmat.2023.134753 ↗
3. Faiad, A., M. Alsmari, M. M. Z. Ahmed, M. L. Bouazizi, B. Alzahrani, and H. Alrobei. Date palm tree waste recycling: treatment and processing for potential engineering applications. Sustainability, Vol. 14, 2022, id. 1134.10.3390/su14031134 ↗
4. Yahya, S. A., T. Iqbal, M. M. Omar, and M. Ahmad. Techno-economic analysis of fast pyrolysis of date palm waste for adoption in Saudi Arabia. Energies, Vol. 14, 2021, id. 6048.10.3390/en14196048 ↗
5. Al-Kutti, W., M. Nasir, M. A. Megat Johari, A. B. M. S. Islam, A. A. Manda, and N. I. Blaisi. An overview and experimental study on hybrid binders containing date palm ash, fly ash, OPC and activator composites. Construction and Building Materials, Vol. 159, 2018, pp. 567–577.10.1016/j.conbuildmat.2017.11.017 ↗
6. Thomas, B. S., J. Yang, K. H. Mo, J. A. Abdalla, R. A. Hawileh, and E. Ariyachandra. Biomass ashes from agricultural wastes as supplementary cementitious materials or aggregate replacement in cement/geopolymer concrete: A comprehensive review. Journal of Building Engineering, Vol. 40, 2021, id. 102332.10.1016/j.jobe.2021.102332 ↗
7. Al-Kutti, W., A. B. M. Saiful Islam, and M. Nasir. Potential use of date palm ash in cement-based materials. Journal of King Saud University - Engineering Sciences, Vol. 31, 2019, pp. 26–31.10.1016/j.jksues.2017.01.004 ↗
8. Nasir, M. and W. Al-Kutti. Performance of date palm ash as a cementitious material by evaluating strength, durability, and characterization. Buildings, Vol. 9, 2018, id. 6.10.3390/buildings9010006 ↗
9. Blaisi, N. I. Environmental assessment of utilizing date palm ash as partial replacement of cement in mortar. Journal of Hazardous Materials, Vol. 357, 2018, pp. 175–179.10.1016/j.jhazmat.2018.06.013 ↗
10. Ashraf, N., M. Nasir, W. Al-Kutti, and F. A. Al-Maziad. Assessment of thermal and energy performance of masonry blocks prepared with date palm ash. Materials for Renewable and Sustainable Energy, Vol. 9, 2020, pp. 1–13.10.1007/s40243-020-00178-2 ↗
11. Teara, A. and D. Shu Ing. Mechanical properties of high strength concrete that replace cement partly by using fly ash and eggshell powder. Physics and Chemistry of the Earth, Parts A/B/C, Vol. 120, 2020, id. 102942.10.1016/j.pce.2020.102942 ↗
12. Zaid, O., S. R. Zamir Hashmi, M. H. El Ouni, R. Martínez-García, J. de Prado-Gil, and S. E. A. S. Yousef. Experimental and analytical study of ultra-high-performance fiber-reinforced concrete modified with egg shell powder and nano-silica. Journal of Materials Research and Technology, Vol. 24, 2023, pp. 7162–7188.10.1016/j.jmrt.2023.04.240 ↗
13. Adamu, M., Y. E. Ibrahim, and H. Alanazi. Optimization of sustainable concrete properties modified with blends of date palm ash and eggshell powder using response surface methodology. Developments in the Built Environment, Vol. 17, 2024, id. 100359.10.1016/j.dibe.2024.100359 ↗
14. Yang, D., J. Zhao, W. Ahmad, M. Nasir Amin, F. Aslam, K. Khan, et al. Potential use of waste eggshells in cement-based materials: A bibliographic analysis and review of the material properties. Construction and Building Materials, Vol. 344, 2022, id. 128143.10.1016/j.conbuildmat.2022.128143 ↗
15. Abdellatief, M., Y. M. Ahmed, M. Taman, E. Elfadaly, Y. Tang, and A. A. Abadel. Physico-mechanical, thermal insulation properties, and microstructure of geopolymer foam concrete containing sawdust ash and egg shell. Journal of Building Engineering, Vol. 90, 2024, id. 109374.10.1016/j.jobe.2024.109374 ↗
16. Khan, K., W. Ahmad, M. N. Amin, and A. F. Deifalla. Investigating the feasibility of using waste eggshells in cement-based materials for sustainable construction. Journal of Materials Research and Technology, Vol. 23, 2023, pp. 4059–4074.10.1016/j.jmrt.2023.02.057 ↗
17. Wang, N., Z. Xia, M. N. Amin, W. Ahmad, K. Khan, F. Althoey, et al. Sustainable strategy of eggshell waste usage in cementitious composites: An integral testing and computational study for compressive behavior in aggressive environment. Construction and Building Materials, Vol. 386, 2023, id. 131536.10.1016/j.conbuildmat.2023.131536 ↗
18. Nandhini, K. and J. Karthikeyan. Sustainable and greener concrete production by utilizing waste eggshell powder as cementitious material – A review. Construction and Building Materials, Vol. 335, 2022, id. 127482.10.1016/j.conbuildmat.2022.127482 ↗
19. Ofuyatan, O. M., A. G. Adeniyi, D. Ijie, J. O. Ighalo, and J. Oluwafemi. Development of high-performance self compacting concrete using eggshell powder and blast furnace slag as partial cement replacement. Construction and Building Materials, Vol. 256, 2020, id. 119403.10.1016/j.conbuildmat.2020.119403 ↗
20. Rasid, N. N., N. H. Khalid, A. Mohamed, A. R. Sam, Z. A. Majid, and G. F. Huseien. Ground palm oil fuel ash and calcined eggshell powder as SiO2–CaO based accelerator in green concrete. Journal of Building Engineering, Vol. 65, 2023, id. 105617.10.1016/j.jobe.2022.105617 ↗
21. Hamada, H., B. Tayeh, F. Yahaya, K. Muthusamy, and A. Al-Attar. Effects of nano-palm oil fuel ash and nano-eggshell powder on concrete. Construction and Building Materials, Vol. 261, 2020, id. 119790.10.1016/j.conbuildmat.2020.119790 ↗
22. Khan, K., W. Ahmad, M. N. Amin, and A. Ahmad. A systematic review of the research development on the application of machine learning for concrete. Materials, Vol. 15, 2022, id. 4512.10.3390/ma15134512 ↗
23. Li, T., J. Yang, P. Jiang, A. H. AlAteah, A. Alsubeai, A. M. Alfares, et al. Predicting high-strength concrete’s compressive strength: a comparative study of artificial neural networks, adaptive neuro-fuzzy inference system, and response surface methodology. Materials, Vol. 17, 2024, id. 4533.10.3390/ma17184533 ↗
24. Qian, Y., J. Yang, W. Yang, A. H. Alateah, A. Alsubeai, A. M. Alfares, et al. Prediction of ultra-high-performance concrete (UHPC) properties using gene expression programming (GEP). Buildings, Vol. 14, 2024, id. 2675.10.3390/buildings14092675 ↗
25. Luo, J., G. Wang, G. Li, and G. Pesce. Transport infrastructure connectivity and conflict resolution: a machine learning analysis. Neural Computing and Applications, Vol. 34, 2022, pp. 6585–6601.10.1007/s00521-021-06015-5 ↗
26. Khan, M., J. Lao, and J.-G. Dai. Comparative study of advanced computational techniques for estimating the compressive strength of UHPC. Journal of Asian Concrete Federation, Vol. 8, 2022, pp. 51–68.10.18702/acf.2022.6.8.1.51 ↗
27. Zhang, N., C. Yan, L. Li, and M. Khan. Assessment of fiber factor for the fracture toughness of polyethylene fiber reinforced geopolymer. Construction and Building Materials, Vol. 319, 2022, id. 126130.10.1016/j.conbuildmat.2021.126130 ↗
28. Essam, A., S. A. Mostafa, M. Khan, and A. M. Tahwia. Modified particle packing approach for optimizing waste marble powder as a cement substitute in high-performance concrete. Construction and Building Materials, Vol. 409, 2023, id. 133845.10.1016/j.conbuildmat.2023.133845 ↗
29. Alyami, M., K. Onyelowe, A. H. AlAteah, T. S. Alahmari, A. Alsubeai, I. Ullah, et al. Innovative hybrid machine learning models for estimating the compressive strength of copper mine tailings concrete. Case Studies in Construction Materials, Vol. 21, 2024, id. e03869.10.1016/j.cscm.2024.e03869 ↗
30. Tian, Q., Y. Lu, J. Zhou, S. Song, L. Yang, T. Cheng, et al. Compressive strength of waste-derived cementitious composites using machine learning. Reviews on Advanced Materials Science, Vol. 63, 2024, id. 20240008.10.1515/rams-2024-0008 ↗
31. Wang, K. L., J. Li, L. Li, E. Deng, Y. Zhang, and Z. Li. Machine learning to estimate the bond strength of the corroded steel bar-concrete. Structural Concrete, Vol. 25, 2024, pp. 696–715.10.1002/suco.202300401 ↗
32. Xia, X. Optimizing and hyper-tuning machine learning models for the water absorption of eggshell and glass-based cementitious composite. PloS One, Vol. 19, 2024, id. e0296494.10.1371/journal.pone.0296494 ↗
33. Ahmad, W., V. S. Veeraghantla, and A. Byrne. Advancing sustainable concrete using biochar: Experimental and modelling study for mechanical strength evaluation. Sustainability, Vol. 17, 2025, id. 2516.10.3390/su17062516 ↗
34. Bai, C., H. Nguyen, P. G. Asteris, T. Nguyen-Thoi, and J. Zhou. A refreshing view of soft computing models for predicting the deflection of reinforced concrete beams. Applied Soft Computing, Vol. 97, 2020, id. 106831.10.1016/j.asoc.2020.106831 ↗
35. Ke, B., M. Khandelwal, P. G. Asteris, A. D. Skentou, A. Mamou, and D. J. Armaghani. Rock-burst occurrence prediction based on optimized Naïve Bayes Models. IEEE Access, Vol. 9, 2021, pp. 91347–91360.10.1109/ACCESS.2021.3089205 ↗
36. Armaghani, D. J., E. Momeni, and P. G. Asteris. Application of group method of data handling technique in assessing deformation of rock mass. Metaheuristic Comput Appl, Vol. 1, No. 1, 2020, pp. 1–18.
37. Liao, J., P. G. Asteris, L. Cavaleri, A. S. Mohammed, M. E. Lemonis, M. Z. Tsoukalas, et al. Novel fuzzy-based optimization approaches for the prediction of ultimate axial load of circular concrete-filled steel tubes. Buildings, Vol. 11, 2021, id. 629.10.3390/buildings11120629 ↗
38. Cavaleri, L., G. E. Chatzarakis, F. Di Trapani, M. G. Douvika, K. Roinos, N. M. Vaxevanidis, et al. Modeling of surface roughness in electro-discharge machining using artificial neural networks. Advances in Materials Research, Vol. 6, 2017, id. 169.
39. Ashrafian, A., E. Panahi, S. Salehi, M. Karoglou, and P. G. Asteris. Mapping the strength of agro-ecological lightweight concrete containing oil palm by-product using artificial intelligence techniques. Structures, Vol. 48, 2023, pp. 1209–1229.10.1016/j.istruc.2022.12.108 ↗
40. Alkayem, N. F., L. Shen, A. Mayya, P. G. Asteris, R. Fu, G. Di Luzio, et al. Prediction of concrete and FRC properties at high temperature using machine and deep learning: A review of recent advances and future perspectives. Journal of Building Engineering, Vol. 83, 2024, id. 108369.10.1016/j.jobe.2023.108369 ↗
41. Skentou, A. D., A. Bardhan, A. Mamou, M. E. Lemonis, G. Kumar, P. Samui, et al. Closed-form equation for estimating unconfined compressive strength of granite from three non-destructive tests using soft computing models. Rock Mechanics and Rock Engineering, Vol. 56, 2023, pp. 487–514.10.1007/s00603-022-03046-9 ↗
42. Sarir, P., J. Chen, P. G. Asteris, D. J. Armaghani, and M. M. Tahir. Developing GEP tree-based, neuro-swarm, and whale optimization models for evaluation of bearing capacity of concrete-filled steel tube columns. Engineering with Computers, Vol. 37, 2021, pp. 1–19.10.1007/s00366-019-00808-y ↗
43. Zhou, J., P. G. Asteris, D. J. Armaghani, and B. T. Pham. Prediction of ground vibration induced by blasting operations through the use of the Bayesian Network and random forest models. Soil Dynamics and Earthquake Engineering, Vol. 139, 2020, id. 106390.10.1016/j.soildyn.2020.106390 ↗
44. Long, X., M.-H. Mao, T.-X. Su, Y.-T. Su, and M.-K. Tian. Machine learning method to predict dynamic compressive response of concrete-like material at high strain rates. Defence Technology, Vol. 23, 2023, pp. 100–111.10.1016/j.dt.2022.02.003 ↗
45. Niu, Y., W. Wang, Y. Su, F. Jia, and X. Long. Plastic damage prediction of concrete under compression based on deep learning. Acta Mechanica, Vol. 235, 2024, pp. 255–266.10.1007/s00707-023-03743-8 ↗
46. Huang, G., L. Zhang, S. Chu, Y. Xie, and Y. Chen. A highly ductile carbon material made of triangle rings: A study of machine learning. Applied Physics Letters, Vol. 124, 2024, id. 043103.10.1063/5.0189906 ↗
47. Zhao, H., Y. Wang, X. Liu, X. Wang, Z. Chen, Z. Lei, et al. Review on solid wastes incorporated cementitious material using 3D concrete printing technology. Case Studies in Construction Materials, Vol. 21, 2024, id. e03676.10.1016/j.cscm.2024.e03676 ↗
48. Chen, H., B. Huang, H. Zhang, K. Xue, M. Sun, and Z. Wu. An efficient Bayesian method with intrusive homotopy surrogate model for stochastic model updating. Computer–Aided Civil and Infrastructure Engineering, Vol. 39, 2024, pp. 2500–2516.10.1111/mice.13206 ↗
49. Zhou, L., Q. Sun, S. Ding, S. Han, and A. Wang. A machine-learning-based method for ship propulsion power prediction in ice. Journal of Marine Science and Engineering, Vol. 11, 2023, id. 1381.10.3390/jmse11071381 ↗
50. Zhang, W., X. Yang, J. Lin, B. Lin, and Y. Huang. Experimental and numerical study on the torsional behavior of rectangular hollow reinforced concrete columns strengthened By CFRP. Structures, Vol. 70, 2024, id. 107690.10.1016/j.istruc.2024.107690 ↗
51. Guo, M., H. Huang, W. Zhang, C. Xue, and M. Huang. Assessment of RC frame capacity subjected to a loss of corner column. Journal of Structural Engineering, Vol. 148, 2022, id. 04022122.10.1061/(ASCE)ST.1943-541X.0003423 ↗
52. Huang, H., M. Li, Y. Yuan, and H. Bai. Experimental research on the seismic performance of precast concrete frame with replaceable artificial controllable plastic hinges. Journal of Structural Engineering, Vol. 149, 2023, id. 04022222.10.1061/JSENDH.STENG-11648 ↗
53. Yang, D., P. Xu, A. Zaman, T. Alomayri, M. Houda, A. Alaskar, et al. Compressive strength prediction of concrete blended with carbon nanotubes using gene expression programming and random forest: hyper-tuning and optimization. Journal of Materials Research and Technology, Vol. 24, 2023, pp. 7198–7218.10.1016/j.jmrt.2023.04.250 ↗
54. Aslam, F. and M. Z. Shahab. Supplementary cementitious materials in blended cement concrete: Advancements in predicting compressive strength through machine learning. Materials Today Communications, Vol. 38, 2024, id. 107725.10.1016/j.mtcomm.2023.107725 ↗
55. Chen, Z. Application of machine learning boosting and bagging methods to predict compressive and flexural strength of marble cement mortar. Materials Today Communications, Vol. 39, 2024, id. 108600.10.1016/j.mtcomm.2024.108600 ↗
56. Alyami, M., I. Ullah, A. H. AlAteah, A. Alsubeai, T. S. Alahmari, F. Farooq, et al. Machine learning models for predicting the compressive strength of cement-based mortar materials: Hyper tuning and optimization. Structures, Vol. 71, 2025, id. 107931.10.1016/j.istruc.2024.107931 ↗
57. Chu, H.-H., M. A. Khan, M. Javed, A. Zafar, M. Ijaz Khan, H. Alabduljabbar, et al. Sustainable use of fly-ash: Use of gene-expression programming (GEP) and multi-expression programming (MEP) for forecasting the compressive strength geopolymer concrete. Ain Shams Engineering Journal, Vol. 12, 2021, pp. 3603–3617.10.1016/j.asej.2021.03.018 ↗
58. Oltean, M. and D. Dumitrescu. Multi expression programming. Journal of Genetic Programming and Evolvable Machines, 2002.
59. Arabshahi, A., N. Gharaei-Moghaddam, and M. Tavakkolizadeh. Development of applicable design models for concrete columns confined with aramid fiber reinforced polymer using multi-expression programming. Structures, Vol. 23, 2020, pp. 225–244.10.1016/j.istruc.2019.09.019 ↗
60. Abu Yaman, M., M. Abd Elaty, and M. Taman. Predicting the ingredients of self compacting concrete using artificial neural network. Alexandria Engineering Journal, Vol. 56, 2017, pp. 523–532.10.1016/j.aej.2017.04.007 ↗
61. Belalia Douma, O., B. Boukhatem, M. Ghrici, and A. Tagnit-Hamou. Prediction of properties of self-compacting concrete containing fly ash using artificial neural network. Neural Computing and Applications, Vol. 28, 2017, pp. 707–718.10.1007/s00521-016-2368-7 ↗
62. Zhang, J., G. Ma, Y. Huang, J. sun, F. Aslani, and B. Nener. Modelling uniaxial compressive strength of lightweight self-compacting concrete using random forest regression. Construction and Building Materials, Vol. 210, 2019, pp. 713–719.10.1016/j.conbuildmat.2019.03.189 ↗
63. Aslam, F., F. Farooq, M. N. Amin, K. Khan, A. Waheed, A. Akbar, et al. Applications of gene expression programming for estimating compressive strength of high-strength concrete. Advances in Civil Engineering, Vol. 2020, 2020, id. 8850535.10.1155/2020/8850535 ↗
64. Alsharari, F., K. Khan, M. N. Amin, W. Ahmad, U. Khan, M. Mutnbak, et al. Sustainable use of waste eggshells in cementitious materials: An experimental and modeling-based study. Case Studies in Construction Materials, Vol. 17, 2022, id. e01620.10.1016/j.cscm.2022.e01620 ↗
65. Amin, M. N., W. Ahmad, K. Khan, S. Nazar, A. M. A. Arab, and A. F. Deifalla. Evaluating the relevance of eggshell and glass powder for cement-based materials using machine learning and SHapley Additive exPlanations (SHAP) analysis. Case Studies in Construction Materials, Vol. 19, 2023, id. e02278.10.1016/j.cscm.2023.e02278 ↗
66. Chen, Z., B. Iftikhar, A. Ahmad, Y. Dodo, M. A. Abuhussain, F. Althoey, et al. Strength evaluation of eco-friendly waste-derived self-compacting concrete via interpretable genetic-based machine learning models. Materials Today Communications, Vol. 37, 2023, id. 107356.10.1016/j.mtcomm.2023.107356 ↗
67. Shah, S., M. Houda, S. Khan, F. Althoey, M. Abuhussain, M. A. Abuhussain, et al. Mechanical behaviour of E-waste aggregate concrete using a novel machine learning algorithm: Multi expression programming (MEP). Journal of Materials Research and Technology, Vol. 25, 2023, pp. 5720–5740.10.1016/j.jmrt.2023.07.041 ↗
68. Iftikhar, B., S. C. Alih, M. Vafaei, M. F. Javed, M. F. Rehman, S. S. Abdullaev, et al. Predicting compressive strength of eco-friendly plastic sand paver blocks using gene expression and artificial intelligence programming. Scientific Reports, Vol. 13, 2023, id. 12149.10.1038/s41598-023-39349-2 ↗
69. Liu, X., A. H. AlAteah, A. Alsubeai, T. S. Alahmari, and S. A. Mostafa. Prediction of flexural strength of concrete with eggshell and glass powders: Advanced cutting-edge approach for sustainable materials. Reviews on Advanced Materials Science, Vol. 63, 2024, id. 20240055.10.1515/rams-2024-0055 ↗
70. Zhang, Y., Q. Zhang, A. H. AlAteah, A. Essam, and S. A. Mostafa. Predictive modeling for mechanical characteristics of ultra high-performance concrete blended with eggshell powder and nano silica utilizing traditional technique and machine learning algorithm. Case Studies in Construction Materials, Vol. 21, 2024, id. e04025.10.1016/j.cscm.2024.e04025 ↗
71. Adamu, M. and Y. E. Ibrahim. Environmental sustainability and cost-benefit analysis of concrete containing date palm ash and eggshell powder: A response surface methodology approach. Case Studies in Chemical and Environmental Engineering, Vol. 9, 2024, id. 100636.10.1016/j.cscee.2024.100636 ↗
72. Chang, Q., C. Zhao, A. H. AlAteah, S. Alinsaif, M. Sufian, and A. Ahmad. AI-powered optimization of engineered cementitious composites properties and CO₂ emissions for sustainable construction. Case Studies in Construction Materials, Vol. 22, 2025, id. e04405.10.1016/j.cscm.2025.e04405 ↗
73. Oltean, M. and C. Grosan. A comparison of several linear genetic programming techniques. Complex Systems, Vol. 14, 2003, pp. 285–314.
74. Oltean, M. Improving the search by encoding multiple solutions in a chromosome. arXiv preprint arXiv:211011239, 2021, 81–107.
75. Oltean, M. and C. Grosan. Evolving digital circuits using multi expression programming. In Proceedings. 2004 NASA/DoD Conference on Evolvable Hardware, 2004, 2024, pp. 87–94.10.1109/EH.2004.1310814 ↗
76. Alavi, A. H., A. H. Gandomi, M. G. Sahab, and M. Gandomi. Multi expression programming: a new approach to formulation of soil classification. Engineering with Computers, Vol. 26, 2010, pp. 111–118.10.1007/s00366-009-0140-7 ↗
77. DeJong, K. A. Analysis of the behavior of a class of genetic adaptive systems. Dept. Computer and Communication Sciences, University of Michigan, Ann Arbor, United States, 1975.
78. Farooq, F., W. Ahmed, A. Akbar, F. Aslam, and R. Alyousef. Predictive modeling for sustainable high-performance concrete from industrial wastes: A comparison and optimization of models using ensemble learners. Journal of Cleaner Production, Vol. 292, 2021, id. 126032.10.1016/j.jclepro.2021.126032 ↗
79. Alade, I. O., M. A. Abd Rahman, and T. A. Saleh. Predicting the specific heat capacity of alumina/ethylene glycol nanofluids using support vector regression model optimized with Bayesian algorithm. Solar Energy, Vol. 183, 2019, pp. 74–82.10.1016/j.solener.2019.02.060 ↗
80. Grznár, P., M. Gregor, Š. Mozol, L. Mozolová, H. Krump, M. Mizerák, et al. A comprehensive analysis of sensitivity in simulation models for enhanced system understanding and optimisation. Processes, Vol. 12, 2024, id. 716.10.3390/pr12040716 ↗
81. Wei, W., P. Larrey-Lassalle, T. Faure, N. Dumoulin, P. Roux, and J.-D. Mathias. How to conduct a proper sensitivity analysis in life cycle assessment: taking into account correlations within LCI data and interactions within the LCA calculation model. Environmental Science & Technology, Vol. 49, 2015, pp. 377–385.10.1021/es502128k ↗
82. Iftikhar, B., S. C. Alih, M. Vafaei, M. A. Elkotb, M. Shutaywi, M. F. Javed, et al. Predictive modeling of compressive strength of sustainable rice husk ash concrete: Ensemble learner optimization and comparison. Journal of Cleaner Production, Vol. 348, 2022, id. 131285.10.1016/j.jclepro.2022.131285 ↗
83. Iftikhar Faraz, M., S. Ul Arifeen, M. Nasir Amin, A. Nafees, F. Althoey, and A. Niaz. A comprehensive GEP and MEP analysis of a cement-based concrete containing metakaolin. Structures, Vol. 53, 2023, pp. 937–948.10.1016/j.istruc.2023.04.050 ↗
84. Zou, B., Y. Wang, M. Nasir Amin, B. Iftikhar, K. Khan, M. Ali, et al. Artificial intelligence-based optimized models for predicting the slump and compressive strength of sustainable alkali-derived concrete. Construction and Building Materials, Vol. 409, 2023, id. 134092.10.1016/j.conbuildmat.2023.134092 ↗
85. Amin, M. N., W. Ahmad, K. Khan, and A. F. Deifalla. Optimizing compressive strength prediction models for rice husk ash concrete with evolutionary machine intelligence techniques. Case Studies in Construction Materials, Vol. 18, 2023, id. e02102.10.1016/j.cscm.2023.e02102 ↗
86. Baykasoğlu, A., H. Güllü, H. Çanakçı, and L. Özbakır. Prediction of compressive and tensile strength of limestone via genetic programming. Expert Systems with Applications, Vol. 35, 2008, pp. 111–123.10.1016/j.eswa.2007.06.006 ↗
87. Liu, H., S. A. Khan, M. N. Amin, F. Althoey, and M. T. Qadir. Evaluating the strength loss and the effectiveness of glass and eggshell powder for cement mortar under acidic conditions. Reviews on Advanced Materials Science, Vol. 63, 2024, id. 20240042.10.1515/rams-2024-0042 ↗
88. Abdalla, A. and A. Salih Mohammed. Surrogate models to predict the long-term compressive strength of cement-based mortar modified with fly ash. Archives of Computational Methods in Engineering, Vol. 29, 2022, pp. 4187–4212.10.1007/s11831-022-09734-7 ↗