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 87

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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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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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Influence of physical and biochemical composition of three cellulose fibers on cracking of soil
(2019-01-01)
Boddu, Rishita
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Hong, Min
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Yongkang, Deng
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Fengjiao, Chen
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Garg, Ankit
Different soil improvement techniques have been used to intensify the engineering properties of soil. Three different lignocellulose fiber-reinforced (jute, coir and water hyacinth (WH)) have been explored on the desiccation potential of compacted clayey silt coil. The experimental methodology involved the mixing of fibers with soil at requisite amount and subjecting them to natural environment with controlled irrigating. The controlled irrigation comprised of 15 wetting/drying cycles for 105 days. Parameters like matric suction and water content were focused upon and recorded along with the surface crack formation. The data obtained from the field experiments were analyzed using the Artificial Neural Network (ANN) approach, which is developed in house using C++ language. From the analysis, it can be comprehended that coir is more effective as a reinforcement due to its multifilament nature and higher lignin content which is suitable in resisting crack formation. Further, optimization analysis and sensitivity analysis suggested mechanism of cracking for each fiber.
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Performance of Coir Fiber Addition for Clay as a Sub-Grade for Pavement Design
(2020-01-03)
Munirwan, R. P.
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Munirwansyah
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Marwan
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Ramadhansyah, P. J.
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Kamchoom, V.
Clay soil behavior often becomes problematic with building construction, it is about the shrink-swell behavior in clay when influenced by water content. Coconut husk fiber (coir fiber), in general, is an industrial waste which is still infrequent to be re-used. This study aims are to determine the effect of coir fiber addition for mechanical stabilization of clay soil in terms of CBR (California Bearing Ratio) value. Soil samples used for laboratory tests were collected from Ulee Glee area of Pidie Jaya Regency. According to AASHTO classification, the soil category is A-7-5 (25) while for USCS classification, the soil is OH (Organic High). The percentage of added coir fiber was 0%, 0.2%, 0.4% and 0.6% of the dry weight of the soil with a coir fiber length of 2 and 3 cm. Two treatments of clay-coir fiber mixing method were applied namely direct mixing and mixing by layers. The results of natural soil compaction test obtained that OMC (Optimum Moisture Content) value and dry soil weight (γ<inf>d</inf>max) was 26.8% and 1.34 gr/cm3 respectively. The highest CBR results were obtained for clay soil with 0.4% coir fiber 3 cm (direct mix) with CBR value = 17.7%. Furthermore, the lowest CBR value is 10% for percentage of 0.2% with coir fiber length 2 cm (by layer). In general, high organic clay soil with coir fiber mixture addition is able to increase the CBR values if compared to CBR of natural soil which is 8.15%. Thus, the use of coir fiber in this study is able to improve soil bearing capacity which is useful for construction material in the site.
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Evaluation of various machine learning-based bias correction approaches for NASA POWER air temperatures: a case study of Nigeria
(2025-01-01)
Faloye, Oluwaseun Temitope
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Kamchoom, Viroon
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Ogunrinde, Akinwale T.
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Ajayi, Ayodele Ebenezer
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Oguntunde, Philip Gbenro
Remotely sensed air temperature data from NASA POWER are widely used in regions with scarce climatic observations, particularly for agricultural applications such as calculating crop water requirements. This study employed a suite of machine learning (ML) algorithms to correct biases in NASA POWER air temperature outputs, including multiple support vector regression (SVR) variants—Linear SVR, Quadratic SVR, Cubic SVR, Fine Gaussian SVR, Medium Gaussian SVR, Coarse Gaussian SVR—and ensemble decision tree models: bagged trees (BGT) and boosted trees (BT). The objective of this study was to assess the ability of different ML algorithms to reduce biases in NASA POWER air temperature data, with the broader goal of identifying the most suitable ML method for air temperature bias correction in Nigeria. For this analysis, we used daily air temperature records from seven meteorological stations across diverse regions of Nigeria. The performance of NASA POWER minimum and maximum air temperature datasets was evaluated using standard error metrics. Subsequent application of ML algorithms significantly improved data accuracy: the normalized root mean square error (NRMSE) of the corrected outputs was mostly below 10%, indicating excellent predictive performance when ML was integrated. Among the SVR variants tested, Fine Gaussian SVR consistently yielded the best prediction results. This finding suggests that Fine Gaussian SVR is a robust tool for enhancing the reliability of air temperature data—critical for improving the accuracy of crop water requirement calculations in regions where in-situ air temperature observations are limited.
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Effectiveness of elephant grass roots in improving soil shear strength for slope reinforcement
(2025-09-01)
Munirwan, Reza Pahlevi
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Milasafarah, Syarifah
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Sungkar, Munira
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Gunawan, Hendra
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Jaya, Ramadhansyah Putra
Slope instability and landslides remain major geotechnical concerns in tropical regions, where intense rainfall, high humidity, and extensive human activities such as deforestation and poor land management accelerate erosion and ground failure. As a sustainable alternative to conventional engineering solutions, vegetation-based methods have gained increasing attention, particularly for their ability to reinforce soil through root systems. This study investigates the potential of elephant grass (Pennisetum purpureum) roots to enhance the shear strength of tropical soils for slope stabilization. Soil samples categorized as silty sand, clayey sand, and clay were collected and subjected to laboratory testing. Roots were added at varying proportions (0.1 %, 0.3 %, and 0.5 %) and lengths (2 cm and 3 cm), and samples were tested using direct shear tests to determine their mechanical behavior. Root tensile strength was also evaluated to assess its contribution to soil reinforcement. Additionally, scanning electron microscopy (SEM) was used to examine the microstructural interactions between roots and soil particles. The results revealed significant improvements in cohesion and internal friction angles, with optimal reinforcement observed at 0.3 % root content and 3 cm root length, especially in clay (cohesion = 0.61 kg/cm2, friction angle = 12.56°) and clayey sand (cohesion = 0.76 kg/cm2, friction angle = 13.29°). SEM analysis confirmed effective physical bonding and interlocking between roots and soil matrices. These findings show that elephant grass roots are an effective, eco-friendly solution for stabilizing landslide-prone soils, supporting bioengineering research and guiding future field validation and long-term performance studies.
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Permeability and setting time of bio-mediated soil under various medium concentrations
(2021-04-01)
Treebupachatsakul, Treesukon
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Kamchoom, Viroon
The bio-clogging using bacteria can be an eco-friendly and sustainable alternative to conventional grouting methods for seepage control. However, it remains unclear to date how the dilute concentration of bacterium and medium during field installation can affect the setting time of bacterium and its correlation with permeability reduction. In this study, the setting time of bacterium and its effectiveness in permeability reduction were addressed through experimental and theoretical investigations. A series of sand column was cultivated using different concentrations of Leuconostoc mesenteroides and culture medium. The distribution and composition of the bacterial product (i.e. dextran) were observed by refractometer, scanning electron microscope (SEM), and energy dispersive X-ray spectroscopy (EDS). Soil permeability was recorded using a constant head test. The results revealed that bacterium was effective to produce dextran at the setting time of about 5 d after installation. This dextran can reduce the permeability of bio-mediated soil by two orders of magnitude, even without culture medium supply. In general, the dextran production decreased proportionally with increase of bacterium and medium concentration. However, at 50% bacterium and medium concentration by weight, it still has a significant influence on permeability reduction with similar setting time, compared to 100% concentration.
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Combining compost and biochar facilitates moisture improvements and plant growth under field conditions
(2026-03-01)
Kamchoom, Viroon
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Chaisarn, Sumetha
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Ni, Junjun
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Chen, Zhongkui
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Sinsamutpadung, Natdanai
Aims: Using vegetation on degraded soils often faces difficulties due to poor nutrients and limited water retention. Soil amendments such as compost and biochar are therefore required. However, their combined effects on soil and plant development are complex, as intense rainfall and high temperatures in tropical conditions may accelerate amendment degradation and alter soil properties over time. Methods: A two-year field experiment was conducted to monitor soil moisture dynamics, microstructure, vetiver grass (Chrysopogon zizanioides) growth, including shoot height, root depth, and root biomass. Four treatments were compared: unamended soil (S), soil + compost (C), soil + compost + raw biochar (C + RB), and soil + compost + pre-treated biochar (C + PB). Results: S and C treatments exhibited relatively low water retention, associated with smaller proportions of sub-micropores and super-nanopores. C contained more large pores, promoting drainage but causing greater moisture fluctuations. In contrast, C + RB enhanced water retention by increasing fine-pore volumes, while C + PB achieved the highest and most stable soil water content. The improved moisture conditions in C + PB corresponded with deeper rooting and greater root biomass compared with other treatments. Conclusion: Pure compost promoted early plant growth but was less effective over multiple wet–dry seasons, as its organic matter decomposed rapidly under tropical conditions. Combining compost and biochar, particularly pre-treated biochar, offers a practical, field-ready approach to improve soil structure and moisture retention, thereby supporting sustained plant growth in degraded soils.
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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.

Kamchoom, Viroon

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