Kamchoom, Viroon

Preferred name

Kamchoom, Viroon

Alternative Name

Kamchoom, V.

Main Affiliation

School of Engineering

viroon.ka@kmitl.ac.th

Now showing 1 - 10 of 19

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Investigation of long-term performance monitoring of cementitious mixes modified with healing agents and polymeric additives of self-healing polymer modified mortar (SHPMM)
(2026-03-01)
Kanwal, Humaira
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Wang, Ziping
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Hao, Wenfeng
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Javed, Kamran
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Asim, Muhammad
Concrete and mortar exhibit durability limitations in aggressive environments due to cracking, high permeability, and construction defects. Polymer-modified and self-healing cementitious materials have emerged as sustainable solutions; however, the synergistic use of polymer modifiers with chemical–biological healing agents remains underexplored. This study investigates self-healing polymer-modified mortar (SHPMM) incorporating styrene butadiene rubber (SBR) and ethylene vinyl acetate (EVA) as partial cement replacements at 0%,4%,8%,12% & 16%. A healing system consisting of 5% calcium lactate, 5% sodium silicate, 1% sodium carbonate. Also 1% effective microorganisms was added to all mixes. Workability, mechanical performance, durability, and microstructural characteristics were evaluated through slump, ultrasonic pulse velocity, strength tests, rapid chloride permeability, SEM, and EDX analyses. The results indicate that polymer addition significantly improves workability, strength, and durability. Slump values increased steadily with increasing polymer content. Optimum performance was observed at 4% and 8% polymer replacement, where permeability was markedly reduced. Compared to the control mix, compressive strength increased by 7–11%, split tensile strength by 12–17%, and flexural strength by 31–33%. RCPT values decreased substantially, with reductions of 32% and 45% for 4% and 8% SBR, and 22% and 58% for 4% and 8% EVA, respectively. Microstructural analysis confirmed improved matrix densification and crack-healing efficiency. EVA demonstrated superior performance compared to SBR, attributed to its powdered form and enhanced bonding characteristics. Overall, the combined application of polymer modifiers and healing agents effectively improves the mechanical performance, durability, and self-healing efficiency of cementitious composites, offering a viable solution for sustainable infrastructure in aggressive environments.
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Evaluating the impact of waste marble on the compressive strength of traditional concrete using machine learning
(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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Zurita Polo, Susana Monserrat
Waste marble, an industrial byproduct generated from marble cutting and polishing processes, can be effectively utilized as a partial replacement in concrete mixtures. Incorporating waste marble in concrete not only addresses environmental concerns related to marble waste disposal but also contributes to the sustainability of construction materials. Using machine learning (ML) to predict the impact of waste marble on the compressive strength of traditional concrete offers several advantages over repeated laboratory experiments. ML offers a powerful alternative to costly and time-consuming laboratory experiments, enabling faster and more sustainable exploration of the potential of waste marble in improving concrete’s compressive strength. This research has focused on evaluating the impact of waste marble on the compressive strength of traditional concrete using machine learning (ML). Advanced ML techniques such as the Group Methods Data Handling Neural Network (GMDH-NN), Support Vector Regression (SVR), K-Nearest Neighbors (kNN) and Adaptive Boosting (AdaBoost) have been applied in this research work. The GMDH-NN model was created using GMDH Shell 3.0 software, while AdaBoost, SVR and kNN models were created using “Orange Data Mining” software version 3.36. Error indices such as the sum of squared error (SSE), mean absolute error (MAE), mean squared error (MSE), root mean squared error (RMSE), and Error (%), and performance metrics such as Accuracy % and the R2 between predicted and calculated compressive strength parameters were used to evaluate the overall behavior of the models. Finally, the Hoffman sensitivity analysis procedure was applied to determine the individual relative impact of the input variables on the output. At the end of the processes, a total of 1135 waste marble concrete entries were collected containing constituents such as the cement density (C), waste marble (WM), fine aggregate (FAg), coarse aggregate (CAg), water (W), superplasticizer (PL) and curing age (Age) used as input variables of the waste marble concrete model. The records were divided into training set (900 records = 80%) and validation set (235 records = 20%) following standard partitioning pattern reported in the literature. The kNN and AdaBoost, with SSE of 1408.5 MPa2 and 1397 MPa2 respectively and a tie Accuracy of 95.5% and R2 of 0.985 showed the best models suggesting excellent model performance while GMDH-NN showed the worst. Conversely, RF balances accuracy and model complexity, making it a practical alternative to kNN and AdaBoost. And lastly, Age, Coarse Aggregates, Water, and Plasticizer play the most significant roles in determining the compressive strength, while Cement, Waste Marble, and Fine Aggregates have comparatively smaller impacts. However, considering the standard proportion required for waste marble powder to replace cement, it showed a remarkable influence on the behavior of the concrete thus a recommended potential for its used as replacement for cement.
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Evaluating the slope behavior for geophysical flow prediction with advanced machine learning combinations
(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
Ensuring safety in geotechnical engineering has consistently posed challenges due to the inherent variability of soil. In the case of slope stability problems, performing on-site tests is both costly and time-intensive due to the need for sophisticated equipment (to acquire and move) and logistics. Consequently, the analysis of simulation models based on soft computing proves to be a practical and invaluable alternative. In this research work, learning abilities of the Class Noise Two (CN2), Stochastic Gradient Descent (SGD), Group Method of Data Handling (GMDH) and artificial neural network (ANN) have been investigated in the prediction of the factor of safety (FOS) of slopes. This has been successfully done through literature search, data curation and data sorting. A total of three hundred and forty-nine (349) data entries on the FOS of slopes were collected from literature and sorted to remove odd values and unlogic results, which had been used together in a previous research work. After the sorting process, the remainder of the realistic data entries was 296. The previous work which had included unrealistic data entries had unit weight, γ (kN/m3), cohesion, C (kPa),angle of internal friction (Φ°), slope angle (°), slope height H (m), and pore water pressure ratio, r<inf>u</inf> as the studied parameters, which formed the independent variables. After careful checks, the initial results showed poor correlation with the individual factors and the factors were collected into three non-dimensional parameters based on the understanding of the physics of flows, which are: C/γ.h-Cohesion/unit weight x slope height, tan(ϕ)/tan(β)-the tangent of internal friction angle/Tangent of slope angle, and ρ/γ.h-Water pressure/unit weight x slope height, which are deployed as inputs and FOS-the safety factor of the slope as the output. At the end of the exercise, the ANN outclassed the other techniques with SSE of 62%, MAE of 0.27, MSE of 0.21, RMSE of 0.46, average total error of 24%, and R2 of 0.946 thereby becoming the decisive intelligent model in this exercise. However, there is an advantage the deployment of GMDH, which comes second in order of superiority, has over the ANN. This is the development of a closed-form equation that allows its model to be applied manually in the design of slope stability problems. Overall, the present research models outperformed the eleven (11) models of the previous work due to sorting and elimination of unrealistic data entries deposited in the literature, the application of dimensionless combination of the studied slope stability parameters and the superiority of the selected machine learning techniques.
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Effects of milling followed by different gradation sizes of lawrencepur sand on the properties of cementitious mortar
(2025-12-01)
Aslam, Hafiz Muhammad Shahzad
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Aslam, Hafiz Muhammad Usman
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Onyelowe, Kennedy C.
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Noshin, Sadaf
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Yasin, Mazhar
The swift rise of urbanization and industrialization has severely depleted natural sand resources and escalated industrial waste accumulation. Environmental damage from over-dredging and limited disposal space has driven researchers to seek alternative solutions. In Lahore, Punjab, Pakistan, coarse pit sand is predominantly used, and overly fine sands from the Ravi and Chenab rivers, which do not meet ASTM grading standards. In this research, Lawrencepur Sand was milled, and its effects on the sand's physical properties and the mortar's mechanical performance were evaluated. For this, comprehensive experimental investigations were conducted, and the mechanical properties of the mortar were assessed at 3, 7, and 28 days. From the experiment, it is clear that milling refines the physical properties of sand by decreasing size, fineness modulus (FM), and absorption, while increasing density and specific gravity, which enhances mortar performance. Milled sand improved mortar density (3.1–11.5 %), compressive strength (10.6–71.4 %), and flexural strength (13.5–48 %), while reducing water absorption by 9–34 %. Excessive milling reduced strength and increased water absorption. SPSS analysis confirmed that milled sand significantly improved mortar performance, with strong statistical validation (p < 0.001). Scanning Electron Microscopy (SEM) and X-ray Diffraction (XRD) analysis also confirmed microstructural densification of mortar by milling, and over-milling led to a decline in performance due to poor packing.
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Developing advanced datadriven framework to predict the bearing capacity of piles on rock
(2025-12-01)
Onyelowe, Kennedy C.
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Hanandeh, Shadi
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Kamchoom, Viroon
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Ebid, Ahmed M.
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Reyes Silva, Fabián Danilo
Developing accurate predictive models for pile bearing capacity on rock is crucial for optimizing foundation design and ensuring structural stability. This research presents an advanced data-driven framework that integrates multiple machine learning algorithms to predict the bearing capacity of piles based on geotechnical and in-situ test parameters. A comprehensive dataset comprising key influencing factors such as pile dimensions, geological characteristics, and penetration resistance was utilized to train and validate various models, including Kstar, M5Rules, ElasticNet, XNV, and Decision Trees. The Taylor diagram and statistical evaluations demonstrated the superiority of the proposed models in capturing complex nonlinear relationships, with high correlation coefficients and low root mean square errors indicating robust predictive capabilities. Sensitivity analyses using Hoffman and Gardener’s approach and SHAP values identified the most influential parameters, revealing that penetration resistance, pile embedment depth, and geological conditions significantly impact pile capacity. The findings underscore the effectiveness of machine learning in geotechnical engineering applications, offering a reliable and efficient alternative to traditional empirical and analytical methods. The developed framework provides engineers and practitioners with a powerful tool for improving pile design accuracy, reducing uncertainties, and optimizing construction practices. Future research should focus on expanding the dataset with diverse geological conditions and exploring hybrid modeling techniques to enhance prediction accuracy further.
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Prediction and validation of mechanical properties of self-compacting geopolymer concrete using combined machine learning methods a comparative and suitability assessment of the best analysis
(2025-12-01)
Onyelowe, Kennedy C.
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Ebid, Ahmed M.
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Awoyera, Paul
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Kamchoom, Viroon
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Rosero, Evelin
Recent sustainable engineering trends show the re-use of wastes in the production of concrete materials. This was important in two ways. First, there is a great environmental necessity to eliminate these industrial wastes and their usage in a solid waste upcycling system to ensure structural sustainability creates an avenue for this process. Second, it has become important to reduce laboratory and equipment costs by establishing intelligent models through the application of these supplementary cements and optimized for optimal performance of concrete materials. For these reasons, the present research work has applied the intelligent learning abilities of eight (8) ensemble-based and one (1) symbolic regression machine learning methods to predict the strengths (compressive-Fc, flexural-Ff and splitting tensile-Ft) of SCGPC with the “Orange Data Mining” software version 3.36. In this research paper, the influence of the industrial wastes like ground granulated blast furnace slag (GGBS) and fly ash (FA) and alkali activators such as (NaOH and Na<inf>2</inf>SiO<inf>3</inf>) on the performance self-compacting geopolymer concrete (SCGPC) in terms of strength has been studied. This has been executed using 132 mix entries at different curing regimes and partitioned into 75% and 25% for training and validation, respectively. At the end of the process, performance indices were employed to test accuracy and comparatively the best. Also, the Taylor chart-based comparison of the performance of the ensemble-based machine learning models was conducted. The results show that for the compressive strength of the SCGPC (Fc) model, the K-NN outclassed all the ensemble techniques with an average R2 of 0.99, accuracy of 0.96, and an average error of 0.04%. This is followed in order of superiority by the SVM closing its model with an average R2 of 0.99, accuracy of 0.955 and average error of 0.045%. Both models ended with equal SSE, MAE, MSE, and RMSE. For the flexural strength of the SCGPC (Ff) model, the K-NN and the SVM performed equally in all the studied indices especially with average R2 of 0.99 and outclassed all the other ensemble techniques. Finally, for the splitting tensile strength (Ft) of the SCGPC, the K-NN and the SVM again performed equally with an average R2 of 0.985 and the other performance indices. The RSM showed a strong competition with R2 (standard = 1) of 0.987, 0.973, and 0.986 and adequate precision (standard = 7) of 129.7, 85.3, and 123.5, for the Fc, Ff, and Ft, respectively. In addition, the RSM as a symbolic regression learning system proposed closed-form equations with which the model can be applied manually to design the production of SCGPC performing at optimal strength with optimized application of the industrial waste materials especially the most influential components, which are the GGBS, FA and NaOH. The NB came least in performance. Overall, the studied ensemble-based ML techniques applied in this present research paper outperformed the techniques used in previous literatures, except the poorly performed NB.
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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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Data-driven framework for prediction of mechanical properties of waste glass aggregates concrete
(2025-12-01)
Onyelowe, Kennedy C.
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Hanandeh, Shadi
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Kamchoom, Viroon
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Ebid, Ahmed M.
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Imran, Hamza
This research presents a novel data-driven framework for predicting the mechanical properties of waste glass aggregate concrete using six advanced metaheuristic optimization algorithms: Bat Algorithm (Bat), Cuckoo Search Algorithm (Cuckoo), Elephant Herding Optimization (Elephant), Firefly Algorithm (Firefly), Rhinoceros Optimization Algorithm (Rhino), and Gray Wolf Optimizer (Wolf). The study evaluates these models based on their ability to predict compressive strength (Fc), tensile strength (Ft), density, and slump using key statistical performance indicators such as SSE, MAE, MSE, RMSE, accuracy, R2, and KGE. Sensitivity analysis was conducted using Hoffman and Gardener’s method as well as the SHAP technique to determine the most influential parameter in the prediction process. Results indicate that the Firefly and Wolf algorithms exhibited the highest prediction accuracy across all four properties, with Wolf emerging as the overall best-performing model due to its superior generalization ability, lower error rates, and high correlation with experimental results. Among the input parameters, the water-to-binder ratio was identified as the most influential factor affecting the mechanical properties of waste glass aggregate concrete, as demonstrated by both sensitivity analysis methods. This highlights the critical role of optimal water content in achieving desirable strength and workability in sustainable concrete mixtures. The study’s novelty lies in the comparative assessment of multiple optimization algorithms applied to waste-based concrete, an approach that has not been extensively explored in previous research. Additionally, the integration of SHAP analysis for feature importance ranking provides an interpretable machine learning approach to concrete mix design, which enhances decision-making for engineers and researchers. The practical implications of this research extend to sustainable machine learning-based concrete design, where AI-driven optimization can help reduce the reliance on conventional trial-and-error methods. By utilizing waste glass aggregates, the study supports circular economy initiatives in construction, reducing environmental impact while maintaining structural performance. The proposed models can be implemented in real-world scenarios to optimize mix designs for large-scale applications, leading to cost-effective and eco-friendly construction materials. This research advances the field of smart construction by demonstrating the effectiveness of machine learning in sustainable material engineering, paving the way for future AI-assisted innovations in the industry.
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Development of data-driven framework for the geotechnical behavior of xanthan gum-treated clay reinforced with polypropylene fibers
(2026-01-01)
Onyelowe, Kennedy C.
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Kamchoom, Viroon
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Baldovino, Jair De Jesús Arrieta
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Kumar, S. Anandha
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Ebid, Ahmed M.
This study develops a robust data-driven modeling framework to predict the unconfined compressive strength (UCS) and stiffness (Go) of low-plasticity clay soils stabilized with Xanthan Gum (XG) and Polypropylene Fiber (PPF), aiming to advance sustainable geotechnical design. A total of 108 soil specimens were prepared with varying XG dosages and cured over different periods, and predictive models were constructed using a Decision Table algorithm optimized with six bio-inspired optimization techniques. Among these, the Firefly-optimized model consistently provided the highest accuracy, demonstrating reliable agreement between predicted and measured values. Sensitivity analysis identified XG dosage, curing time, and dry density as the most influential factors governing UCS and Go. These findings highlight the strong potential of the proposed machine learning framework to guide field engineers in optimizing mix design parameters for improved mechanical behavior of bio-treated soils, reducing reliance on time-consuming and costly laboratory tests while promoting environmentally sustainable foundation practices. The need for this study arises from the growing demand for green soil stabilization techniques that minimize the use of cement and lime while still ensuring reliable performance in construction. Its applicability extends to real-world geotechnical projects such as embankments, road subgrades, and shallow foundations, where predictive modeling can significantly streamline design decisions and improve long-term sustainability.
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Mechanical properties of self compacting concrete reinforced with hybrid fibers and industrial wastes under elevated heat treatment
(2025-12-01)
Onyelowe, Kennedy C.
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Hanandeh, Shadi
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Kamchoom, Viroon
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Ebid, Ahmed M.
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Zurita Polo, Susana Monserrat
Machine learning prediction of the mechanical properties of self-compacting concrete (SCC) reinforced with hybrid fibers, incorporating industrial wastes like fly ash and blast furnace slag, and cured under elevated heat provides a reliable and efficient alternative to traditional laboratory experiments. In this work, extensive literature review leading to the collection, sorting and curation of a global database representative of the mechanical properties of self-compacting concrete reinforced with hybrid fiber mixed with industrial wastes for sustainable construction was conducted. The collected database constituted traditional concrete components and admixtures such as Cement (C), Fly ash (FA), Slag (BFS), Fine Aggregate (FAg), Coarse Aggregate (CAg), Water (W), Superplasticizer (PL), Fiber (Fi), and Temperature (Temp.) studied under the mechanical properties such as the Compressive Strength (Fc), Tensile Strength (Fsp), and Flexural Strength (Ff). The collected 114 records were divided into training set (90 records = 80%) and validation set (24 records = 20%) following the guidelines for data partitioning for optimal performance in machine learning predictions. Different advanced machine learning methods created using “Weka Data Mining” software version 3.8.6 were applied such as “Semi-supervised classifier (Kstar)”, “M5 classifier (M5Rules), “Elastic net classifier (ElasticNet), “Correlated Nystrom Views (XNV)”, and “Decision Table (DT)” to predict the output. The Hoffman/Gardener and SHAP techniques are used to estimate the sensitivity of the input parameter on the output. Finally, various performance metrics are used to evaluate the reliability of the models. The results show that the machine learning models show varying degrees of predictive accuracy, with the Kstar and XNV models consistently outperforming others across all mechanical properties. However, Kstar with accuracies of 96.5%, 96.0%, and 97.0% for Fc, Fsp, and Ff predictions, respectively proposed the most decisive model. Also, the Hoffman and Gardener method highlights the role of the binders, chemical additives, and curing, whereas SHAP attributes greater importance to aggregates and binder interactions.

Kamchoom, Viroon

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