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Cost-effective FRP solutions for enhancing strength and strain of sustainable concrete made with waste tyre rubber
(2026-12-01)
Saingam, Panumas
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Chatveera, Burachat
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Hussain, Qudeer
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Sua-iam, Gritsada
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Chaimahawan, Preeda
This study investigates the compressive behavior and analytical modeling of natural and rubberized concretes confined with cost-effective glass fiber-reinforced polymer (GFRP) jackets. Forty-two cylindrical specimens were tested under axial compression, including natural aggregate concrete (NAC) and rubberized concretes (RuC) prepared with 20% fine aggregate replacement using coarse (2.0 mm) and fine (0.425 mm) waste tire rubber. Both full and strip GFRP wrapping configurations with two, four, and six layers were examined. The results showed that GFRP confinement substantially enhanced both strength and ductility, transforming brittle failure into a gradual, energy-absorbing response. Full wrapping produced up to 63% and 90% strength increases for NAC and rubberized concretes, respectively, with ultimate strain gains exceeding 1300% in the fine-rubber mix. Strip wrapping achieved moderate yet significant improvements while offering material savings. Analytical models were developed for both concrete types to predict confined stress–strain behavior, achieving strong correlations (R2 = 0.84–0.99) between predicted and experimental data. The derived regression-based formulations successfully captured the influence of confinement pressure, rubber content, and wrapping configuration. These findings demonstrate that GFRP provides an economical and sustainable confinement solution for enhancing the performance of rubberized concrete in structural and retrofitting applications.
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Experimental evaluation of sustainable jute–basalt hybrid FRP systems for flexural strengthening of RC beams with variable wrapping and light-weight aggregate replacement
(2026-12-01)
Saingam, Panumas
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Hanif, Muhammad Adnan
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Ahmed, Fahad
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Chatveera, Burachat
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Sua-iam, Gritsada
This research experimentally assesses the flexural strengthening of reinforced concrete (RC) beams through the use of sustainable jute–basalt (JB) hybrid fiber-reinforced polymer (FRP) systems with variable wrapping schemes and lightweight aggregate (LWA) replacement. A total of 18 beams with identical geometry and reinforcement were tested under four-point bending, including controls, basalt FRP (BFRP), jute FRP (JFRP), and hybrid JB FRP systems in bottom-only, U-wrap, and full-wrap configurations. The results indicate that FRP confinement significantly modified failure modes, transitioning from the brittle crushing in controls to rupture- or debonding-controlled mechanisms. BFRP significantly enhanced strength, achieving up to 36.8% greater capacity in full-wrap beams, while JFRP improved ductility but was more prone to premature debonding. Hybrid JB FRP systems demonstrated the most balanced performance, with U-wrap hybrids achieving 39.6% higher load capacity and maintaining significant deformation capacity even in LWA concrete. Load–strain responses confirmed yielding of steel reinforcement in all cases, though maximum strains were reduced after confinement due to premature fiber debonding or rupture at smaller deflections. The use of LWA reduced ductility of control beams, but hybrid U-wrap systems successfully compensated for this limitation, providing the highest load and deflection values among all specimens. These results emphasize the potential of hybrid natural–mineral FRP systems as sustainable alternatives to synthetic composites, providing competitive ductility and reinforcement for structural retrofitting applications in both conventional and lightweight concretes.
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Evaluating the shear capacity of reinforced concrete beams retrofitted with hybrid FRP composite techniques: experimental and analytical study
(2026-07-01)
Joyklad, Panuwat
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Chatveera, Burachat
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Sua-Iam, Gritsada
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Chaimahawan, Preeda
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Suthumma, Chisanuphong
This study investigates the shear performance of reinforced concrete (RC) beams strengthened with externally bonded hybrid fiber-reinforced polymer systems. The strengthening materials include mild steel strips, glass chopped strand mat (GCSM) fiber-reinforced polymer (FRP) composites, natural basalt fiber-reinforced polymer (BFRP) composites, and hybrid combinations of GCSM and BFRP. The hybrid systems pair low-strain GCSM composites with higher-strain natural BFRP to improve both strength and ductility. Eleven RC beams were tested and compared to a control specimen that lacked shear reinforcement over half the span. All strengthened beams showed substantial improvements over the unstrengthened control (35.49 kN). Peak loads ranged from 59.94 kN to 144.86 kN, corresponding to increases of approximately 69–308% relative to the control. In term of deflections again peak load, strengthening increased deflections by roughly 4.4–10.5 times versus the control beam. Strip orientation and layering strongly influenced crack redistribution and energy dissipation. The hybrid GCSM–BFRP systems provided a balanced improvement in strength and post-peak behavior.These results indicate that steel-strip and composite-based strengthening approaches offer affordable and effective solutions for retrofitting shear-deficient RC elements, particularly in post-disaster or resource-limited settings.
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Balancing rheology, strength, and thermal performance in sustainable self-compacting mortars incorporating recycled concrete block fines and fine wood dust
(2026-06-15)
Chatveera, Burachat
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Chintanapakdee, Chatpan
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Saingam, Panumas
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Pongsopha, Phattharachai
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Hussain, Qudeer
The use of waste-derived materials in cementitious systems has been widely explored; however, limited studies have addressed the combined influence of recycled fine aggregates and bio-based additives under a rheology-controlled design framework. This study investigates the rheology-controlled performance of sustainable self-compacting mortars (SCM) incorporating recycled concrete block fines (RCBF) and fine wood dust (FWD) with calcium carbonate as an inert filler. RCBF replaced natural sand at 5–20% by volume, while FWD was added at 1–5% of binder volume to evaluate their combined influence on fresh behavior, mechanical performance, durability, and thermal conductivity. The results show that increasing RCBF and FWD contents increased viscosity and superplasticizer demand, while compressive strength decreased from 49.66 MPa (control) to 28.1 MPa at the highest replacement level. The mix with 5% RCBF and 1% FWD exhibited the best overall balance, maintaining a compressive strength of 45.1 MPa with moderate water absorption (6.27%) and limited sulfate-induced strength loss (∼4.0%). Across all mixtures, however, strength loss under sulfate exposure ranged from 1.9% to 13.1%. With increasing replacement levels, water absorption increased to 8.39% and ultrasonic pulse velocity decreased, indicating a progressive increase in porosity and internal discontinuity. In contrast, thermal conductivity significantly decreased from 2.133 to 1.311 W/m·K, indicating enhanced insulation performance. These results demonstrate that the combined use of RCBF and FWD enables a controlled trade-off between mechanical and durability properties and improved thermal performance, supporting the development of sustainable SCM systems governed by rheological design rather than strength maximization.
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Development of self-compacting mortar incorporating calcium carbonate and waste garnet: Workability, strength, and fire durability assessment
(2026-06-01)
Chatveera, Burachat
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Ejaz, Ali
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Chintanapakdee, Chatpan
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Saingam, Panumas
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Hussain, Qudeer
The excessive consumption of natural sand and cement in mortar production raises environmental concerns, underscoring the need for sustainable alternatives. While the separate use of cementitious and fine aggregate substitutes has been studied, their combined effects in self-compacting mortar (SCM) remain underexplored. This study addresses this gap by investigating SCM mixes incorporating calcium carbonate (CaCO₃) as a partial cement substitute (0–20%) and waste garnet (0–100%) as a fine aggregate replacement. A total of 18 mixes were evaluated for their fresh properties, mechanical performance, durability, and residual properties after elevated-temperature exposure. The results showed that the mix with 10% CaCO₃ and 60% waste garnet exhibited the best overall performance, achieving approximately 66 MPa compressive strength and 8.1 MPa flexural strength at 90 days, representing up to a 15% improvement over the control. Water absorption was reduced to 2.42% at 90 days, while improved resistance under acidic conditions was observed, with only 7.71% mass loss after 180 days of exposure to 5% H₂SO₄ solution. Furthermore, the optimized mix retained over 65% of its compressive strength after exposure to 600 °C, indicating good residual mechanical performance at elevated temperatures. Microstructural analysis revealed a dense and cohesive matrix with a refined pore structure. These findings suggest that the combined use of CaCO₃ and waste garnet can provide a potentially eco-efficient approach for producing high-performance SCM. The improved workability, strength, and durability indicate potential suitability for applications such as repair mortars and precast elements, where both flowability and long-term performance are required.
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Enhancing Strength and Ductility of Rubberized Concrete Using Low-Cost Glass Jackets
(2026-04-01)
Saingam, Panumas
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Noman, Muhammad
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Chatveera, Burachat
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Sua-Iam, Gritsada
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Mehmood, Tahir
This study examines the compressive behavior and analytical modelling of natural and rubberized concretes (RuC) confined with low-cost glass chopped-strand mat (GCSM) jackets. A total of forty-two cylindrical specimens were tested under axial compression to assess the influence of rubber particle size, confinement configuration, and the number of GCSM layers. The RuC mixes were prepared by replacing 20% of fine aggregate by volume with crumb rubber of two size fractions: coarse (2.0 mm, retained on #10 sieve) and fine (0.425 mm, retained on #40 sieve). Both full- and strip-wrapping schemes were applied using two, four, and six layers of GCSM. The results demonstrated that GCSM jackets significantly enhanced the mechanical performance of both NAC and RuC specimens. Full wrapping provided the highest confinement efficiency, increasing compressive strength by up to 115% for NAC and 90% for RuC, while the ultimate axial strain increased by more than 1300% compared with unconfined specimens. Strip wrapping also improved performance, producing strength gains of 25–45% and strain increases of 250–500%. Analytical stress–strain models were developed through regression analysis, showing strong correlation with the experimental results (R2 = 0.80–0.99). The proposed GCSM jacket system demonstrates high potential as a sustainable and economical alternative for strengthening and retrofitting rubberized concretes, offering improved ductility and energy absorption while supporting circular material utilization. It is noted that the confinement ratio, size of rubberized aggregates, and their percentage replacement of rubberized aggregates should be consistent with the values used in this work in order to use the proposed analytical expressions.
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Advancing Masonry Engineering: Effective Prediction of Prism Strength via Machine Learning Techniques
(2026-04-01)
Saingam, Panumas
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Chatveera, Burachat
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Nawaz, Adnan
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Ali, Muhammad Hassan
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Choudhary, Sandeerah
Masonry buildings have shaped construction history since about 6500 BCE. They offer durability, strength, and cost effectiveness, especially in developing countries. Yet assessing compressive strength during construction remains challenging due to the constituent materials soil, cement, and stone, complicating standardization worldwide. In the present study, an innovative model based on a machine learning algorithm is put forth to predict the compressive strengths of prisms. Some important factors considered as input to the algorithm based on traditional methods are the brick and mortar strengths, prism geometry, mortar bed thickness, and empirically derived height-to-thickness (t) (h/t) ratios. Three different ANN algorithms are coded and trained on the input data, and they are based on the Levenberg–Marquardt algorithm, the resilient backpropagation algorithm, and the conjugate gradient algorithm. The optimal ANN model trained using the conjugate gradient Polak–Ribière algorithm (traincgp) achieves superior performance, with R2 = 0.9881, R2 = 0.9927, RMSE = 0.9914 MPa, MAE = 0.6039 MPa, MAPE = 20.9141%, VAF = 0.9881, and WI = 0.9970. Sensitivity analysis shows the height-to-thickness (h/t) ratio is the dominant influence on compressive strength, consistent with structural mechanics. The primary contributions are the systematically curated, richly parameterized dataset and its use to produce robust, physically interpretable predictions with established ANN methods.
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Predicting Flexural Strength of FRP-Strengthened Waste Aggregate Concrete Beams with Machine Learning: A Step Towards Sustainability
(2026-04-01)
Sangthongtong, Arissaman
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Chatveera, Burachat
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Sua-iam, Gritsada
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Nawaz, Adnan
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Mehmood, Tahir
Using waste materials in the manufacture of concrete has many environmental advantages. However, it can be difficult to estimate structural performance, especially when beams are reinforced with fiber-reinforced polymers (FRP). In order to provide a data-driven approach to sustainable structural design, this work explores the use of machine learning (ML) approaches to forecast the flexural strength of FRP-strengthened waste aggregate concrete beams. A total number of 92 experimental datasets were used to develop and assess four ML algorithms: Random Forest (RF), Decision Tree (DT), Neural Network (NN), and Extreme Gradient Boosting (XGBoost). Regression plots, Taylor diagrams, statistical measures (R2R^2R2, RMSE, MAE, MSE), and explainable AI (XAI) tools, including SHAP, LIME, and partial dependence plots (PDPs), were used to evaluate the model’s performance. RF outperformed NN in terms of predictive accuracy, while XGBoost exhibited similar performance to RF. The most significant predictors, according to a SHAP analysis, were beam length and fiber length, with the lower followed by steel tensile strength, fiber width, and concrete compressive strength. LIME offered local interpretability for individual predictions, but PDPs demonstrated optimal parameter ranges and a nonlinear feature strength relationship. The findings provide engineers with a strong decision-support tool for designing green infrastructure, since they show that ensemble-based models can accurately represent the intricate, nonlinear dynamics controlling flexural behavior in sustainable FRP-strengthened waste aggregate concrete beams.
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From Experiment to Prediction: Machine Learning Solutions for Concrete Strength Assessment with Steel Clamps
(2026-02-01)
Saingam, Panumas
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Chatveera, Burachat
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Sua-Iam, Gritsada
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Chaimahawan, Preeda
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Suthumma, Chisanuphong
This study examines the confined compressive strength (Fcc) of circular, square, and rectangular column geometries under varying confinement conditions. Results indicate that circular columns have the highest Fcc values, exceeding those of square and rectangular shapes. Increased confinement through clamps significantly enhances compressive strength. Five machine learning models, Linear Regression, Decision Tree, Random Forest, AdaBoost, and Gradient Boosting, were used to predict Fcc based on geometric and confinement parameters. Linear Regression and Decision Tree models achieved moderate predictive performance, with R2 values of 0.84 and 0.83, respectively, and relatively higher error measures (RMSE, MAE, and MAPE), indicating limited ability to capture complex nonlinear relationships in the data. In contrast, ensemble-based methods demonstrated superior performance. The Random Forest model improved the coefficient of determination to 0.90 while substantially reducing all error metrics, reflecting enhanced generalization through bagging. The boosting-based approaches yielded the best results, with AdaBoost achieving the highest R2 value of 0.99 and the lowest RMSE, MAE, and MAPE among all models, followed closely by Gradient Boosting with an R2 of 0.98. These results confirm that ensemble learning techniques, particularly boosting algorithms, yield more accurate and robust predictions than single learners for the problem studied. Data visualization techniques, including Regression Error Characteristic curves (REC) and SHapley Additive exPlanations (SHAP) value analysis, highlighted model performance and feature importance, emphasizing the roles of confinement and geometry in compressive strength. This research demonstrates the potential of machine learning to optimize structural engineering design and suggests further exploration of alternative shapes and confinement strategies to enhance structural integrity.
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Synergistic effect of recycled E-waste fiber and polyvinyl alcohol on the properties of green concrete incorporating recycled concrete aggregate
(2025-10-01)
Chatveera, Burachat
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Ejaz, Ali
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Hanif, Muhammad Adnan
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Saingam, Panumas
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Hussain, Qudeer
The growing demand for sustainable construction highlights the need for innovative concrete solutions using waste materials. Although recycled concrete aggregate (RCA), polyvinyl alcohol (PVA), and recycled electronic waste fibers (E-waste fibers) have been studied individually, their combined effects remain underexplored. This study addresses this gap by investigating the synergistic effects of coarse RCA (CRCA) and E-waste fibers on the fresh, mechanical, durability, thermal, and economic properties of green concrete. Fly ash replaced 20 % of cement, and PVA was added at 1 % by cement weight. Results showed that increasing CRCA content reduced workability and strength due to porosity. However, incorporating 4.5 % E-waste fibers significantly improved mechanical performance by bridging microcracks. Higher fiber contents negatively affected durability and workability. Thermal conductivity decreased with more CRCA and fibers, enhancing insulation. Economic analysis confirmed that 4.5 % E-waste fiber offers cost-effective performance. This study supports the sustainable use of electronic and construction waste in concrete.

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