AI-driven Modeling for the Optimization of Concrete Strength for Low-Cost Business Production in the USA Construction Industry

Authors

  • Md. Habibur Rahman Sobuz Department of Building Engineering and Construction Management, Khulna University of Engineering & Technology, Khulna – 9203, Bangladesh | Faculty of Engineering & Quantity Surveying, INTI International University (INTI-IU), Persiaran Perdana BBN, Putra Nilai, Nilai 71800, Negeri Sembilan, Malaysia
  • Mohammad Abu Saleh Department of Business Administration, International American University, 3440 Wilshire Blvd STE 1000, Los Angeles, CA 90010, USA
  • Md. Samiun Department of Business Administration, International American University, 3440 Wilshire Blvd STE 1000, Los Angeles, CA 90010, USA
  • Mohammad Hossain Department of Business Administration, International American University, 3440 Wilshire Blvd STE 1000, Los Angeles, CA 90010, USA
  • Anupom Debnath Department of Business Administration, International American University, 3440 Wilshire Blvd STE 1000, Los Angeles, CA 90010, USA
  • Mahafuj Hassan Department of Business Administration, International American University, 3440 Wilshire Blvd STE 1000, Los Angeles, CA 90010, USA
  • Sanchita Saha Department of Business Administration, Westcliff University, 17877 Von Karman Ave 4th Floor, Irvine, CA 92614, USA
  • Rakibul Hasan Department of Business Administration, Westcliff University, 17877 Von Karman Ave 4th Floor, Irvine, CA 92614, USA
  • Md. Kawsarul Islam Kabbo Department of Building Engineering and Construction Management, Khulna University of Engineering & Technology, Khulna – 9203, Bangladesh
  • Md. Munir Hayet Khan Faculty of Engineering & Quantity Surveying, INTI International University (INTI-IU), Persiaran Perdana BBN, Putra Nilai, Nilai 71800, Negeri Sembilan, Malaysia
Volume: 15 | Issue: 1 | Pages: 20529-20537 | February 2025 | https://doi.org/10.48084/etasr.9733

Abstract

The need to develop ecologically friendly sustainable building materials is made apparent by the worldwide construction industry's substantial contribution to global greenhouse gas emissions. The use of supplemental materials in concrete is one potential solution to lessen the environmental footprint. Thus, the purpose of this work is to use Machine Learning (ML) algorithms to forecast and create an empirical formula for the Compressive Strength (CS) of concrete with supplemental materials. Six distinct ML models—XGBoost, Linear Regression, Decision Tree, k-Nearest Neighbors, Bagging, and Adaptive Boosting—were trained and tested using a dataset that included 359 experimental data of varying mix proportions. The most significant factors used as input parameters are cement, aggregates, water, superplasticizer, silica fume, ambient curing, and supplemental material. Several statistical measures, such as Mean Absolute Error (MAE), coefficient of determination (R2), and Mean Square Error (MSE), were used to evaluate the models. XGBoost model outperformed the other models with R2 values of 0.99 at the training stage. To ascertain how the input parameters affected the outcome, feature importance analysis using Shapely Additive exPlanations (SHAP) was conducted. It was demonstrated that curing age and cement type significantly affected the strength of concrete with high SHAP values. By eliminating experimental procedures, reducing the demand for labor and resources, increasing time efficiency, and offering insightful information for enhancing sustainable manufacturing of concrete, this research advances the low-cost production of concrete in the USA construction industry.

Keywords:

AI, construction materials, ML, business production, strength prediction

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References

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How to Cite

[1]
Sobuz, M.H.R., Saleh, M.A., Samiun, M., Hossain, M., Debnath, A., Hassan, M., Saha, S., Hasan, R., Kabbo, M.K.I. and Khan, M.M.H. 2025. AI-driven Modeling for the Optimization of Concrete Strength for Low-Cost Business Production in the USA Construction Industry. Engineering, Technology & Applied Science Research. 15, 1 (Feb. 2025), 20529–20537. DOI:https://doi.org/10.48084/etasr.9733.

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