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 53

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Machine Learning-Based Prediction of Undrained Shear Strength in Marine Alluvial Clays: A Case Study of Bangkok
(2026-01-01)
Ramineni, Sai Krishna Akash
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Song, Zejun
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Garg, Ankit
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Kamchoom, Viroon
Accurate evaluation of undrained shear strength (Su) is crucial for the safe design of foundations and slopes in marine alluvial clays, including those commonly found in Bangkok. In this study, we assembled an automated machine learning (AutoML) workflow using open-source Python libraries to explore suitable predictive models for Su based on 152 undisturbed clay samples. The input variables considered include depth, moisture content, liquid limit, plastic limit, vane shear strength (PP), and total unit weight. Across the models evaluated, ridge regression offered a stable balance between accuracy and computational efficiency, with a mean absolute error of 0.550 t/m2, a root mean square error of 0.710 t/m2,, and an R² of 0.809, while requiring less than 0.05s of training time. The AutoML process facilitated a more transparent comparison of candidate algorithms, providing insight into variable relevance. Specifically, PP, depth, and unit weight emerged as the most influential predictors. Traditional index properties showed comparatively lower contributions. Five-fold cross-validation suggested that the selected model maintained consistent performance (mean R² = 0.810; standard deviation = 0.025). These results suggest that a streamlined AutoML workflow can aid in identifying reliable and easy-to-interpret models for Su estimation in Bangkok clays. Such an approach may complement laboratory testing and help reduce some of the uncertainty associated with empirical correlations, especially in preliminary design stages.
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Biochar Amendment as a Mitigation Against Freezing–Thawing Effects on Soil Hydraulic Properties
(2025-01-01)
Chen, Zhongkui
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Intraravimonmata, Chitipat
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Kamchoom, Viroon
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Chen, Rui
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Sinsamutpadung, Natdanai
Seasonal freeze–thaw cycles compromise soil structure, thereby increasing hydraulic conductivity but diminishing water retention capacity—both of which are essential for sustaining crop health and nutrient retention in agricultural soils. Prior research has suggested that biochar may alleviate these detrimental effects; however; further investigation into its influence on soil hydraulic properties through freeze–thaw cycles is essential. This study explores the impact of freeze–thaw cycles on the soil water retention and hydraulic conductivity and evaluates the potential of peanut shell biochar to mitigate these effects. Peanut shell biochar was used, and its effects on soil water retention and unsaturated hydraulic conductivity were evaluated through evaporation tests. The findings indicate that freeze–thaw cycles predominantly affect clay’s ability to retain water and control hydraulic conductivity by generating macropores and fissures; with a notable increase in conductivity at high matric potentials. The impact lessens as matric potential decreases below −30 kPa, resulting in smaller differences in conductivity. Introducing biochar helps mitigate these effects by converting large pores into smaller micro- or meso-pores, effectively increasing water retention, especially at higher content of biochar. While biochar’s impact is more pronounced at higher matric potentials, it also significantly reduces conductivity at lower potentials. The total porosity of the soil increased under low biochar application rates (0% and 1%) but declined at higher application rates (2% and 3%) as the number of freeze–thaw cycles increased. Furthermore, the characteristics of soil deformation during freeze–thaw cycles shifted from frost heaving to thaw settlement with increasing biochar application rates. Notably, an optimal biochar application rate was observed to mitigate soil deformation induced by freeze–thaw processes. These findings contribute to the scientific understanding necessary for the development and management of sustainable agricultural soil systems.
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Semi-analytical solutions for pore-water pressure distributions and slope stability in an infinite multi-layered vegetated slope considering highly-nonlinear hydraulic properties of soil
(2025-10-01)
Feng, Song
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Huang, Ruhong
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Li, Guangyao
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Zhan, Liangtong
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Kamchoom, Viroon
Accurately depicting the highly nonlinear hydraulic properties of soil is critical for predicting pore-water pressure distributions and evaluating the stability of vegetated slopes. Accordingly, semi-analytical solutions are proposed for calculating pore-water pressure distributions and slope stability in an infinite multi-layered slope considering both hydrological and mechanical effects of vegetation. The solutions have the advantage of depicting the highly nonlinear hydraulic properties of soil, both with and without roots, using a multi-exponential function. After verifying the solutions, parametric studies are conducted to investigate influential factors on pore-water pressure distributions, including root architecture, root volume ratio, root depth and the combination of different soil layers in landfill cover. It is found that compared to the multi-exponential function, the single-exponential function commonly used in published solutions significantly underestimates negative pore-water pressure induced by root water uptake by up to 65 kPa under drying conditions, because it fails to depict soil hydraulic properties accurately. When root reduces the hydraulic conductivity of unsaturated soil, larger negative pore-water pressure induced by root water uptake within root zone could be observed under drying conditions, while the trend reverses under wetting conditions. The effects of root architecture and root-induced changes in the hydraulic conductivity of unsaturated soil on pore-water pressure distributions become more significant as the root volume ratio increases. Under drying conditions, root water uptake induces the largest negative pore-water pressure near the ground surface in the three-layer landfill cover, compared with the cover with capillary barrier effects and single-layer cover. The derived solutions can be used to guide engineering practices of vegetated slope and landfill cover.
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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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Enhancing clay properties with eggshell powder: A sustainable alternative for soil stabilization
(2026-07-01)
Munirwan, Reza Pahlevi
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Taib, Aizat Mohd
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Jaya, Ramadhansyah Putra
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Yuliana, Yuliana
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Kamchoom, Viroon
Poultry waste is increasing rapidly in many countries as urbanization and industrialization rise and create environmental and economic issues. Eggshell waste deteriorates infrastructures but could effectively stabilize clay soil. This study investigates the performance of eggshell powder (ESP) as a stabilizing agent for clay soil, emphasizing its effects on the mechanical properties of clay and its suitability for construction. The methodology involved preparing soil and ESP samples, followed by standard Proctor compaction tests, direct shear tests, and microstructural analysis. Various percentages of eggshell powder (0%, 3%, 6%, and 9%) were added to the clay soil. The results revealed that the addition of ESP improved the plasticity, compaction behavior, and shear strength of soil. The results showed that the plasticity index decreased from 30.45% (untreated soil) to 21.78% at 6% ESP, and the liquid limit reduced from 65.28% to 57.80%, enhancing soil workability and reducing swelling. Additionally, soil cohesion increased substantially from 82.7 kN/m² (untreated soil) to 144.5 kN/m² at 9% ESP, while the internal friction angle improved from 18° to 25°, contributing to its overall strength and stability. The microstructural analysis confirmed these findings, showing a denser soil matrix and stronger inter-particle bonds. This study concludes that ESP is a promising alternative to traditional soil stabilizers, offering environmental benefits by utilizing waste material and reducing the need for cement and lime. The use of ESP in soil stabilization contributes to sustainable construction practices and presents a viable solution for improving the performance of clay soils in construction.
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Experimental and numerical modelling of time-dependent behaviour in deep cement mixing column improved montmorillonitic clay
(2025-07-15)
Choudhary, Sourabh
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Singh, Moirangthem Johnson
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Kamchoom, Viroon
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Choi, Clarence E.
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Borana, Lalit
The time-dependent behaviour of soft and clayey soils treated with Deep Cement Mixing (DCM) columns is important for analyzing the long-term performance of civil engineering infrastructures. Previous studies on DCM-installed composite soil (CS) have primarily focused on examining the soil strength and stiffness characteristics. The limited focus on the time-dependent settlement and stress-strain distribution of CS underscores the need for a more comprehensive understanding of this complex phenomenon. In this study, a lab-scale physical ground model is designed and developed to investigate the time-dependent settlement profile of the composite Montmorillonitic Clay soil (MMC). The settlement behaviour of the ground model is assessed using Creep Hypothesis B and the results are further validated with the Power Law Model. Additionally, a FEM-based numerical simulation is performed to examine the time-dependent settlement and the stress distribution between the column and surrounding clay soil at different depths. The results from the physical model test show that the time-dependent parameter of the ground model (i.e., DCM column installed in MMC) is proportionate to the loading rate until the failure of the DCM column is reached. However, the time-dependent parameter was found to be decreased by 59.04 % in the post-failure phase of the DCM column. This reduction indicates that the DCM column was the primary load-bearing component before its failure. The numerical study shows that the pore water pressure dissipation in the clay soil and DCM column interface was similar at various depths. The top and bottom sections of the DCM column possess higher stress levels, which demonstrates its susceptibility for failure in the DCM column.
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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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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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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.

Kamchoom, Viroon

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