Roller compacted concrete (RCC) has gained prominence in the construction industry due to its durability, cost-effectiveness, and environmental benefits, particularly with the incorporation of high-volume fly ash (HVFA). However, traditional experimental approaches to evaluating RCC's mechanical properties, such as compressive strength (CS) and splitting tensile strength (STS), are resource-intensive and time-consuming. To address these challenges, this study explores the application of artificial intelligence (AI), specifically artificial neural networks (ANN) and a hybrid ANN-Biogeography-Based Optimization (ANN-BBO) model, to predict the CS and STS of RCC. A dataset comprising 168 RCC mixtures, incorporating various material and process parameters, was analyzed. The ANN-BBO model demonstrated superior predictive accuracy compared to a standalone ANN, with R2 values exceeding 0.98 for both CS and STS, significantly reducing error margins. The findings highlight the effectiveness of AI-driven modeling in optimizing RCC mix designs, minimizing experimental costs, and enhancing the sustainability of concrete production. This research underscores the potential of integrating AI with optimization techniques to refine RCC performance assessment, which enables and facilitates more efficient and sustainable infrastructure development.

Centre for Artificial Intelligence Research and Optimisation Torrens University Australia Brisbane Australia

Civil Engineering Department Bursa Uludag University Bursa Turkey

Faculty of Electrical Engineering and Computer Science VSB Technical University of Ostrava Ostrava Czech Republic

Independent Researcher Sabzevar Iran

Mathematics Education Department Bursa Uludag University Bursa Turkey

University Research and Innovation Center Obuda University Budapest Hungary

Zobrazit více v PubMed

Ludwig, D., Nanni, A. & Shoenberger, J. E.

Liu, H. W., Tian, B., Hou, R. G. & Li, S. Research on roller compacted concrete graduation with vibration liquefaction.

Shamsaei, M., Aghayan, I. & Kazemi, K. A. Experimental investigation of using cross-linked polyethylene waste as aggregate in roller compacted concrete pavement.

Bayqra, S. H., Mardani-Aghabaglou, A. & Ramyar, K. Physical and mechanical properties of high volume fly ash roller compacted concrete pavement (A laboratory and case study).

Zdiri, M., Ben Ouezdou, M. & Neji, J. Theoretical and experimental study of roller-compacted concrete strength.

Nath, P., Sarker, P. K. & Biswas, W. K. Effect of fly ash on the service life, carbon footprint and embodied energy of high strength concrete in the marine environment.

Fernando, A., Siriwardana, C., Gunasekara, C., Shahzad, W., Sethunge, S., Zhang, K. & Rajapakse, D. Performance-based concrete for carbon footprint reduction in the construction industry: A comprehensive systematic review of current progress and future prospects. In

Lehne, J. & Preston, F. Making concrete change: Innovation in low-carbon cement and concrete. Chatham House Report. 13 (2018).

Mardani-Aghabaglou, A., Özen, S. & Altun, M. G. Durability performance and dimensional stability of polypropylene fiber reinforced concrete.

Kilic, I. & Gok, S. Strength and durability of roller compacted concrete with different types and addition rates of polypropylene fibers.

Shankar, S. S., Natarajan, M. & Arasu, A. Exploring the strength and durability characteristics of high-performance fibre reinforced concrete containing nanosilica.

Anbarasu, N. A., Sivakumar, V., Yuvaraj, S., Veeramani, V. & Velusamy, S. Pioneering the next frontier in construction with high-strength concrete infused by nano materials.

Kolase, P. K. & Desai, A. K. Experimental study on monotonic and fatigue behaviour of polypropylene fibre-reinforced roller-compacted concrete with fly ash.

Adamu, M., Mohammed, B. S. & Liew, M. S. Mechanical properties and performance of high volume fly ash roller compacted concrete containing crumb rubber and nano silica.

Cao, C., Sun, W. & Qin, H. The analysis on strength and fly ash effect of roller-compacted concrete with high volume fly ash.

Yerramala, A. & Babu, K. G. Transport properties of high volume fly ash roller compacted concrete.

Lam, N.-T.-M., Nguyen, D.-L. & Le, D.-H. Predicting compressive strength of roller-compacted concrete pavement containing steel slag aggregate and fly ash.

Kazemi, R. & Mirjalili, S. An AI-driven approach for modeling the compressive strength of sustainable concrete incorporating waste marble as an industrial by-product. PubMed PMC

Tipu, R. K., Panchal, V. & Pandya, K. Multi-objective optimized high-strength concrete mix design using a hybrid machine learning and metaheuristic algorithm.

Aggarwal, S., Singh, R., Rathore, A., Kapoor, K. & Patel, M. A novel data-driven machine learning techniques to predict compressive strength of fly ash and recycled coarse aggregates based self-compacting concrete.

Ashrafian, A. et al. Classification-based regression models for prediction of the mechanical properties of roller-compacted concrete pavement.

Fakhri, M., Amoosoltani, E., Farhani, M. & Ahmadi, A. Determining optimal combination of RCCP mixture containing RAP and crumb rubber using hybrid ANN-GA method considering energy absorbency approach.

Vahidi, E. K., Malekabadi, M. M., Rezaei, A., Roshani, M. M. & Roshani, G. H. Modeling of mechanical properties of roller compacted concrete containing RHA using ANFIS.

Rooholamini, H., Hassani, A. & Aliha, M. Evaluating the effect of macro-synthetic fibre on the mechanical properties of roller-compacted concrete pavement using response surface methodology.

Ashrafian, A., Gandomi, A. H., Rezaie-Balf, M. & Emadi, M. An evolutionary approach to formulate the compressive strength of roller compacted concrete pavement.

Chakali, Y., Sadok, A. H., Tahlaiti, M. & Nacer, T. A PSO-ANN intelligent hybrid model to predict the compressive strength of limestone fillers roller compacted concrete (RCC) to build dams.

Adamu, M., Haruna, S., Ibrahim, Y. E. & Alanazi, H. Investigating the properties of roller-compacted rubberized concrete modified with nanosilica using response surface methodology.

Zhang, G., Hamzehkolaei, N. S., Rashnoozadeh, H., Band, S. S. & Mosavi, A. Reliability assessment of compressive and splitting tensile strength prediction of roller compacted concrete pavement: Introducing MARS-GOA-MCS.

Debbarma, S. G. & Ransinchung, R. N. Using artificial neural networks to predict the 28-day compressive strength of roller-compacted concrete pavements containing RAP aggregates.

Hoang, N.-D. Estimating the compressive strength of roller compacted concrete using a novel swarm-optimised light gradient boosting machine.

Calis, G., Yildizel, S. A. & Keskin, U. S. Predicting compressive strength of color pigment incorporated roller compacted concrete via machine learning algorithms: A comparative study.

Chaabene, W. B., Flah, M. & Nehdi, M. L. Machine learning prediction of mechanical properties of concrete: Critical review.

Waris, M. I., Plevris, V., Mir, J., Chairman, N. & Ahmad, A. An alternative approach for measuring the mechanical properties of hybrid concrete through image processing and machine learning.

Biswas, R. et al. A novel integrated approach of RUNge Kutta optimizer and ANN for estimating compressive strength of self-compacting concrete.

Kazemi, R. & Gholampour, A. Evaluating the rapid chloride permeability of self-compacting concrete containing fly ash and silica fume exposed to different temperatures: An artificial intelligence framework.

Yang, S., Sun, J. & Zhifeng, X. Prediction on compressive strength of recycled aggregate self-compacting concrete by machine learning method.

Tipu, R. K., Panchal, V. & Pandya, K. Enhancing chloride concentration prediction in marine concrete using conjugate gradient-optimized backpropagation neural network.

Wu, Y., Liu, F., Zheng, L., Wu, X. & Lai, C. CSR-SVM: Compositional semantic representation for intelligent identification of engineering change documents based on SVM.

Parekh, R. & Mitchell, O. Progress and obstacles in the use of artificial intelligence in civil engineering: An in-depth review.

Mirindi, D., Khang, A. & Mirindi, F. Artificial Intelligence (AI) and automation for driving green transportation systems: A comprehensive review. In

Boussetta, I., El Euch Khay, S. & Neji, J. Comprehensive study on mechanical and transport properties of roller-compacted concrete incorporating reclaimed asphalt pavement.

Korouzhdeh, T., Eskandari-Naddaf, H. & Kazemi, R. Hybrid artificial neural network with biogeography-based optimization to assess the role of cement fineness on ecological footprint and mechanical properties of cement mortar expose to freezing/thawing.

Kazemi, R., Eskandari-Naddaf, H. & Korouzhdeh, T. New insight into the prediction of strength properties of cementitious mortar containing nano-and micro-silica based on porosity using hybrid artificial intelligence techniques.

Bayqra, S. H. Assessment of cold joint formation between layers in high fly ash roller compacted concrete. PhD Thesis. Accessed from thesis center of Turkey (2023) 797198.

Yegnanarayana, B.

Panchal, G., Ganatra, A., Kosta, Y. & Panchal, D. Behaviour analysis of multilayer perceptrons with multiple hidden neurons and hidden layers.

Uzair, M. and N. Jamil. Effects of hidden layers on the efficiency of neural networks. In

Adeli, H. Neural networks in civil engineering: 1989–2000.

Simon, D. Biogeography-based optimization.

Yang, X.-S.

Hagan, M. T. & Menhaj, M. B. Training feedforward networks with the Marquardt algorithm. PubMed

El-Bakry, M. Feed forward neural networks modeling for K-P interactions.

Haykin, S.

Mehlig, B.

Haykin, S.

Rahmani, E., Sharbatdar, M. K. & Beygi, M. A comprehensive investigation into the effect of water to cement ratios and cement contents on the physical and mechanical properties of Roller Compacted Concrete Pavement (RCCP).

Lundberg, S. M. & Lee, S.-I. A unified approach to interpreting model predictions. Adv. Neural Inform. Process. Syst.