[1]. Almeida, J.A., Bandelt, M.J., (2024), Plastic hinge length in reinforced HPFRCC beams and columns, Engineering Structures, 315: 118345.
[2] Lu, T., (2025), Mechanical Analysis of HPFRCC Precast Composite Column, Materials, 18(7): 1567.
[3] Guo, P., (2025), Applications of machine learning methods for design and characterization of high-performance fiber-reinforced cementitious composite (HPFRCC): a review, Journal of Sustainable Cement-Based Materials : 1-24.
[4] Dai, L., (2022), Using machine learning algorithms to estimate the compressive property of high strength fiber reinforced concrete, Materials, 15(13): 4450.
[5] Hasanzadeh, A., (2022), Prediction of the mechanical properties of basalt fiber reinforced high-performance concrete using machine learning techniques, Materials, 15(20): 7165.
[6] Jiao, P., (2019), High-performance fiber reinforced concrete as a repairing material to normal concrete structures: Experiments, numerical simulations and a machine learning-based prediction model, Construction and Building Materials, 223: 1167-1181.
[7] Kontoni, D.-P.N., Ahmadi, M., (2024), Practical prediction of ultimate axial strain and peak axial stress of FRP-confined concrete using hybrid ANFIS-PSO models, in Artificial intelligence applications for sustainable construction, Elsevier, : 225-255.
[8] Shariati, M., (2022), A novel hybrid extreme learning machine–grey wolf optimizer (ELM-GWO) model to predict compressive strength of concrete with partial replacements for cement, Engineering with Computers, 38(1): 757-779.
[9] Ahmadi, M., (2024), Hybrid bio-inspired metaheuristic approach for design compressive strength of high-strength concrete-filled high-strength steel tube columns, Neural Computing and Applications, 36(14): 7953-7969.
[10] Shariati, M., (2020), Prediction of concrete strength in presence of furnace slag and fly ash using Hybrid ANN-GA (Artificial Neural Network-Genetic Algorithm), Smart Structures and Systems, 25(2): 183-195.
[11] Faramarzi, A., (2020), Equilibrium optimizer: A novel optimization algorithm, Knowledge-based systems, 191: 105190.
[12]Natalli, J.F., (2021), A review on the evolution of Portland cement and chemical admixtures in Brazil, Revista IBRACON de Estruturas e Materiais, 14: e14603.
[13] Feng, Z., (2022), Optimal design of a low-cost high-performance hybrid fiber engineered cementitious composites, Construction and Building Materials, 345: 128372.
[14] Naseri, M., (1402), Investigation of the effect of calcination temperature of clay on compressive strength and durability of LC3 concrete, Journal of Concrete Structures and Materials, 8(2): 95-111
[15] Abdolahzade, S., Nili, M., (2022), Evaluation of engineering properties of self-compacting geopolymer concrete using Taguchi method, Journal of Concrete Structures and Materials, 7(2): 213-228
[16] Rai, R., Dhal, K.G., (2023), Recent developments in equilibrium optimizer algorithm: its variants and applications, Archives of Computational Methods in Engineering, 30(6): 3791-3844.
[17] Emani, D.M., (1403), Optimization of mixture design of cementitious concrete containing various percentages of recycled and waste materials: Crumb rubber and reclaimed asphalt pavement (RAP), Journal of Transportation Infrastructure Engineering, 11(1): 11-20.
[18] Rahmani, K., Naserlazade, K., Rostami Golehdar, M., (1402), Experimental study on the effect of aggregate and fiber content on the mechanical properties of single-component slag-based alkali-activated concrete, Journal of Concrete Structures and Materials, 8(2): 146-162.
[19] Ling, J.H., Lim, Y.T., Euniza, J., (2023), Methods to determine ductility of structural members: a review, Journal of the Civil Engineering Forum.
[20] Temesi, O.K., (2024), Ductility Index for Refractory High Entropy Alloys, Crystals, 14(10): 838.
[21] Cong, S., Zhou, Y., (2023), A review of convolutional neural network architectures and their optimizations, Artificial Intelligence Review, 56(3): 1905-1969.
[22] Hossen, M.S., (2020), Data preprocess, Machine Learning and Big Data: Concepts, Algorithms, Tools and Applications : 71-103.
[23] Hematibahar, M., (2024), Analysis of models to predict mechanical properties of high-performance and ultra-high-performance concrete using machine learning, Journal of Composites Science, 8(8): 287.
[24] Dehghani, M., Trojovská, E., Trojovský, P., (2022), Driving training-based optimization: a new human-based metaheuristic algorithm for solving optimization problems.
