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Real-time interpretable and cluster-stratified lightGBM framework for high-precision concrete strength prediction and instantaneous mixture optimization
(2026-08-29)
Elsheikh, Ahmed
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Hematibahar, Mohammad
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Jueyendah, Sebghatullah
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Aljarah, Abdelmalek H.
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Martins, Carlos Humberto
This study presents a real-time, interpretable framework based on the light gradient boosting machine (LightGBM) algorithm for the accurate prediction and optimization of 28-day concrete compressive strength (Fc), validated using a dataset of 500 concrete mixtures. The proposed model was benchmarked against seven widely used regression algorithms, including linear regression (LR), ridge regression (RR), random forest (RF), K-nearest neighbors (KNN), support vector regression (SVR), decision tree (DT), and multivariate adaptive regression splines (MARS), to ensure a comprehensive comparative evaluation. The LightGBM model demonstrated superior predictive performance relative to the benchmark models, achieving an RMSE of 6.11 MPa and an R² of 0.951 during the initial evaluation. Model robustness and generalization capability were further verified using a 10 × 10 repeated k-fold cross-validation procedure, yielding stable results (R² = 0.940 ± 0.017; RMSE = 6.37 ± 0.49 MPa). To capture heterogeneity in mixture compositions, K-means clustering was applied to partition the dataset into four distinct mixture regimes, within which stratified LightGBM models further improved predictive accuracy, reducing RMSE to 3.7–5.1 MPa and achieving R² values exceeding 0.97. Model interpretability was enhanced through global and regime-specific SHAP (Shapley Additive Explanations) analyses, which provided transparent and physically consistent insights into feature contributions, consistently identifying cement as the dominant positive factor and water as the primary negative driver of CS. Furthermore, an interactive web-based prediction engine was developed to enable instantaneous strength prediction, real-time sensitivity analysis, 95% prediction interval estimation, and specification-driven mixture optimization with millisecond-level computational efficiency. Comprehensive diagnostic evaluations, including Taylor diagrams, residual control charts, calibration plots, and prediction-interval validation, confirmed the statistical reliability and practical applicability of the proposed framework. Overall, the developed LightGBM-based system provides an accurate, interpretable, and scalable decision-support tool for data-driven concrete mix design and performance optimization.
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Efficient machine learning for strength prediction of ready-mix concrete production (prolonged mixing)
(2026-01-19)
Tuvayanond, Wiput
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Kamchoom, Viroon
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Prasittisopin, Lapyote
Purpose – This paper aims to clarify the efficient process of the machine learning algorithms implemented in the ready-mix concrete (RMC) onsite. It proposes innovative machine learning algorithms in terms of preciseness and computation time for the RMC strength prediction. Design/methodology/approach – This paper presents an investigation of five different machine learning algorithms, namely, multilinear regression, support vector regression, k-nearest neighbors, extreme gradient boosting (XGBOOST) and deep neural network (DNN), that can be used to predict the 28- and 56-day compressive strengths of nine mix designs and four mixing conditions. Two algorithms were designated for fitting the actual and predicted 28- and 56-day compressive strength data. Moreover, the 28-day compressive strength data were implemented to predict 56-day compressive strength. Findings – The efficacy of the compressive strength data was predicted by DNN and XGBOOST algorithms. The computation time of the XGBOOST algorithm was apparently faster than the DNN, offering it to be the most suitable strength prediction tool for RMC. Research limitations/implications – Since none has been practically adopted the machine learning for strength prediction for RMC, the scope of this work focuses on the commercially available algorithms. The adoption of the modified methods to fit with the RMC data should be determined thereafter. Practical implications – The selected algorithms offer efficient prediction for promoting sustainability to the RMC industries. The standard adopting such algorithms can be established, excluding the traditional labor testing. The manufacturers can implement research to introduce machine learning in the quality controcl process of their plants. Originality/value – Regarding literature review, machine learning has been assessed regarding the laboratory concrete mix design and concrete performance. A study conducted based on the on-site production and prolonged mixing parameters is lacking.
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Bayesian-informed DNN and ensemble learning for predicting soil water characteristic curves from easily measurable parameters
(2026-01-01)
Liu, Yifei
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Ni, Junjun
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Zhang, Fei
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Kravchenko, Ekaterina
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Kamchoom, Viroon
Soil-water characteristic curve (SWCC) represents one of the important properties describing the hydraulic characteristics of unsaturated soil, with extensive application value in geotechnical engineering, but the experimental process for obtaining SWCC is complex and time-consuming. This research proposes a prediction framework based on Bayesian-informed machine learning for SWCC applicable to different soil types, using easily measurable soil parameters. This model uses quantified particle size distribution, bulk density, and saturated water content as input features, and employs Bayesian-Markov Chain Monte Carlo methods to inversely derive Fredlund-Xing (FX) model parameters as output features. Deep Neural Network (DNN) and stacking models based on ensemble learning of five regressors were constructed to establish the prediction framework. Results show that both DNN and stacking models effectively capture complex nonlinear relationships between the three easily measured soil parameters and FX parameters, demonstrating good prediction accuracy. The stacked model shows better prediction results, with R2 exceeding 0.94 for all three parameters, outperforming the DNN model (R2 = 0.93). Through feature engineering and SHAP (Shapley Additive Explanations)-based feature sensitivity analysis, the relationships between input features and the three FX model parameters were physically interpreted, providing physical interpretability for the machine learning models. The prediction method offers a new approach for fast and accurate acquisition of SWCC, expanding the application of machine learning methods in the field of unsaturated soils.
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Modeling the compressive strength behavior of concrete reinforced with basalt fiber
(2025-12-01)
Onyelowe, Kennedy C.
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Ebid, Ahmed M.
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Hanandeh, Shadi
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Kamchoom, Viroon
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Awoyera, Paul
This research investigates the compressive strength behavior of basalt fiber-reinforced concrete (BFRC) using machine learning models to optimize predictions and enhance its practical applications. The study incorporates various modeling techniques, including Artificial Neural Networks (ANN), k-Nearest Neighbors (KNN), Support Vector Machines (SVM), Decision Trees, and Random Forest (RF), to evaluate their predictive capabilities. Basalt Fiber Reinforced Concrete (BFRC) is a composite material that incorporates basalt fibers into traditional concrete to enhance its mechanical and durability properties. The use of basalt fibers, derived from natural volcanic rocks, aligns with sustainability goals due to their eco-friendliness, cost-effectiveness, and high performance. BFRC combines structural excellence with sustainability, making it an ideal material for modern construction practices. Its ability to enhance performance, reduce environmental impact, and ensure long-term durability positions it as a pivotal solution for sustainable infrastructure development. The developed models were used to predict compressive strength of basalt fiber concrete (Cs_bf) using the concrete mixture contents, age, and fiber dimensions. All the developed models were created using “Orange Data Mining” software version 3.36. A total of three hundred and nine (309) records were collected from literature for compressive strength for different mixing ratios of basalt fiber concrete with concrete at different ages. Each record contains the following data: C-Cement content (Kg/m3), FA-Fly ash content (Kg/m3), W-Water content (Kg/m3), SP-Super-plasticizer content (Kg/m3), CAg-Coarse aggregates content (Kg/m3), FAg-Fine aggregates content (Kg/m3), Age-The concrete age at testing (days), L_b-length of basalt fibers (mm), d_bf-Diameter of basalt fibers (µm), V_bf-Volume content of basalt fibers (%) and Cs_bf-Compressive strength of basalt fibre concrete (MPa). The collected records were divided into training set (249 records≈80%) and validation set (60 records≈ 20%). At the end of the process, it can be shown that the present research work outclassed other ML techniques applied in the previous research paper, which reported the utilization of the same size of data entries and basalt reinforced concrete constituents. Taylor chart for measured compressive strength of basalt fiber reinforced concrete predicted with ANN, KNN, SVM, Tree and RF is presented for comparing the performance of predictive models by illustrating three key statistical measures simultaneously: the correlation coefficient (R), the normalized standard deviation (σ), and the root-mean-square error (RMSE). Finally, it can be deduced that after considering the performance indices of the selected ensemble and classification models utilized in this present research paper, all the developed modes have almost the same excellent level of accuracy 95%, but ANN, KNN, and SVR produced R2 of 0.98 each with KNN producing MAE of 1.4 MPa, and MSE of 2.5 MPa to outperform ANN and SVR which produced MAE of 1.55 MPa/MSE of 4.1 MPa and MAE of 1.6 MPa/MSE of 3.85 MPa, respectively. Three techniques were used to estimate the impact of each input on the compressive strength, namely correlation matrix, sensitivity analysis and relative importance chart.
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Developing data driven framework to model earthquake induced liquefaction potential of granular terrain by machine learning classification models
(2025-12-01)
Onyelowe, Kennedy C.
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Kamchoom, Viroon
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Gnananandarao, Tammineni
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Arunachalam, Krishna P.
Earthquake-inducedliquefaction of soils poses a serious georisk in geotechnical designs, construction and the application of geotechnical structures around the world. In this study, the applicability of three soft computing models for liquefaction classification, a topic of significant importance within the fields of geotechnical and earthquake engineering has been evaluated. Twelve input parameters are used to classify the liquefaction potential for 234 data sets collected from an earthquake-induced liquefaction prone granular material environment. For developing the SVM_Poly, SVM_RBK models, an extensive number of trials were conducted using various combinations of C and d for polynomial kernels and C and ∂ for radial basis function kernel-based support vector machines (SVMs) utilizing user-defined parameters. In the same way, several experiments were conducted with a fixed value of C and ∂ kernel specific parameters in order to determine an appropriate value of error-insensitive zone (∋).Similarly, for the random forest classifier (RFC) model, the number of variables used (m) and the number of trees to be grown (k) are two user-defined parameters. These optimum values of m and k parameters are fixed using trial and error process and the same fixed values. The best model was developed as evidence from the confusion matrixes and statistical indicators. The calculated values of confusion matrixes and statistical indicators for training and testing shows that an accuracy of 0.89 indicates the model is correct in its predictions 89% of the time. A sensitivity of 0.85 signifies the model correctly identifies 85% of actual positive instances, while a specificity of 0.94 implies correct identification of 94% of actual negative instances. A precision of 0.94 suggests that when the model predicts a positive instance, it is correct 94% of the time. The Phi Correlation Coefficient, with a value of 0.82, indicates a strong positive correlation between predicted and actual values.Furthermore, the model exhibits a Mean Absolute Error (MAE) of 0.2351, reflecting a relatively low average error in predictions. The Root Mean Squared Error (RMSE) value of 0.3115 indicates better accuracy in predicting the target variable.Finally, all the developed models exhibit promising performance across various evaluation metrics, with low error measures (MAE and RMSE), high accuracy, and strong performance in correctly identifying both positive and negative instances, as evidenced by sensitivity and specificity. The high precision and Phi Correlation Coefficient further affirm the reliability and accuracy of the model’s predictions. However, among the three models FRC model is the best for classifying the liquefaction. The novelty of this research lies in its comparative evaluation and optimization of SVM_Poly, SVM_RBK, and RFC models using a comprehensive set of seismic and soil parameters to accurately classify earthquake-induced liquefaction potential, with the RFC model demonstrating superior predictive performance.
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Self compacting concrete with recycled aggregate compressive strength prediction based on gradient boosting regression tree with Bayesian optimization hybrid model
(2025-12-01)
Abood, Emad A.
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Thoeny, Zainab Abdulrdha
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Azize, Noralhuda M.
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Imran, Hamza
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Kamchoom, Viroon
Self-compacting concrete (SCC) is a special type of concrete that is used in applications requiring high workability, such as in densely reinforced or complex formwork situations. The estimation of 28-day compressive strength for this type is usually made by costly and time-consuming laboratory tests. The problem becomes even more complex when recycled aggregates are added to the mixture to promote eco-friendly and sustainable construction practices. In our research we presented a new hybrid model, GBRT, that was integrated with Bayesian Optimization. This model is able to accurately and efficiently estimate the compressive strength of SCC containing recycled aggregates. We evaluated the model using well-known performance metrics such as RMSE, MAE, and. The performance of the model gave us, on average, an RMSE of 6.000, MAE of 3.968, and of 0.806 in five-fold cross-validation, which emphasized its strong predictive capability and potential as a cost-effective alternative to conventional laboratory testing. The model was also compared with single learner models such as SVR and KNN in order to demonstrate the superiority of the hybrid approach in terms of prediction accuracy and robustness. Our hybrid model surpassed the two previously mentioned models when testing their performance on the test data. Since our model works as a black-box model, a novel explaining machine learning technique named SHAP (Shapley Additive Explanations) was employed to determine which predictors have the most importance and how they trend. The developed model is an accurate, fast, and economical substitute for predicting 28-day compressive strength of self-compacting concrete with recycled aggregates. Finally, the model is converted into an easy-to-use graphical interface that provides civil engineers and practitioners with a useful decision-support tool for mix design optimization and quality control in real-life construction projects.
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Optimizing the utilization of Metakaolin in pre-cured geopolymer concrete using ensemble and symbolic regressions
(2025-12-01)
Onyelowe, Kennedy C.
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Kamchoom, Viroon
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Ebid, Ahmed M.
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Hanandeh, Shadi
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Llamuca Llamuca, José Luis
The optimization of metakaolin (MK) in pre-cured geopolymer concrete involves developing predictive models to capture the interplay of various influencing factors and guide mix design for improved compressive strength and sustainability. Ensemble methods and symbolic regression are promising approaches for this task due to their complementary strengths and solving challenges associated with repeated experiments in the laboratory. Choosing machine learning predictions over repeated, expensive, and time-consuming experiments in research projects, such as optimizing the utilization of metakaolin in pre-cured geopolymer concrete, presents a paradigm shift in how data-driven insights can revolutionize material development. The integration of ensemble and symbolic regression models enables researchers to derive valuable predictions and optimize critical performance parameters efficiently. In this research work, 235 records were collected from extensive literature search for compressive strength for different mixing ratios of pre-cured metakaolin-based geopolymer concrete with concrete at different ages. Each record contains MK: The content of metakaolin (kg/m3), SHS: Sodium hydroxide solution content (kg/m3), SHSM: Sodium hydroxide solution molarity (Mole), SSS: Sodium silicate solution content (kg/m3), W: Extra water content (not including the water in alkaline solutions) (kg/m3), W/S: Water to Solid ratio (Total water content / Solid part of activator solutions + MK), Na<inf>2</inf>O/Al<inf>2</inf>O<inf>3</inf>: Sodium oxide to aluminium oxide ratio, SiO<inf>2</inf>/Al<inf>2</inf>O<inf>3</inf>: Silicon oxide to aluminium oxide ratio, H<inf>2</inf>O/Na<inf>2</inf>O: Water to Sodium oxide ratio, CA/FA: Coarse to Fine aggregate ratio, CAg: The content of coarse aggregates (kg/m3), SP: The content of super-plasticizer (kg/m3), PCC: 0 for no pre-curing, 1 for pre-curing at 60 °C, and 2 for pre-curing at 80 °C, CT: Curing temperature (°C), Age: The concrete age at testing (days) and CS: Compressive strength (MPa). The collected records were portioned into training set (180 records≈75%) and validation set (55 records≈ 25%) and modeled with ensemble and symbolic regression methods. At the end of the model work, performance metrics were used to evaluate the models’ ability and Hoffman and Gardener’s sensitivity analysis was used to evaluate the impact of the variables on the compressive strength of the pre-cured geopolymer concrete mixed with metakaolin. GB and KNN models became the decisive models with excellent performance which outclassed others and the sensitivity analysis indicated that SHSM, SSS, W/S, and Na<inf>2</inf>O/Al<inf>2</inf>O<inf>3</inf> are the most influential to the predicted compressive strength.

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