Journal of Civil Engineering Beyond Limits (CEBEL)

Birkan Şimşek Abdussamet Arslan

This study numerically examines the flexural behavior of reinforced concrete beams retrofitted using a hybrid system comprising textile-reinforced mortar (TRM) and polyurea coating through finite element analysis. Nine beam models were generated in ABAQUS to analyze how strengthening levels influence stiffness, crack control, load capacity, and ductility parameters. Numerical results indicated that one TRM layer with polyurea coating enhances peak load by nearly 150–180%, while two TRM layers achieve an increase of about 200-270%, depending on the concrete strength used. The initial stiffness for strengthened beams also improves significantly to nearly 2–3 times that of unstrengthened models. This study is purely numerical and has not been calibrated with any experimental program; hence results should be interpreted in this light. All interfaces (steel-concrete, concrete-TRM, and TRM-polyurea) were modeled using perfect bond assumptions (embedded + tie), representing an upper-bound strengthening response without slip or debonding. Under these idealized conditions, the hybrid TRM-polyurea system provides consistent improvements in crack confinement, peak load, stiffness, and deformation stability for all levels of concrete strength.

https://doi.org/10.36937/cebel.2026.11094

Md. Belal Hossain Shanzida Akter Puspo Marufa Meem Md Mahabub Rahman

The main aim of this research is to study the strength improvement in pond ash by adding different materials like sand, cement, jute fiber. The study also seeks to determine the appropriate proportion for use of pond ash as an engineering material. The research aims were achieved through a series of laboratory experiments. X-ray fluorescence (XRF) was used to identify the pond ash, which was found to be Class F according to AASHTO M295. Pond ash of class F has less cementitious properties, therefore 1%, 2%, 3% and 4% cement had been added with 78.5%, 79.5%, 80.5%, and 81.5% of pond ash, respectively. Furthermore, these pond ash mixtures were also mixed with 0.5% jute fibers of size 12 mm (mixing percentage of sandy soil was added to these mixing ratios was 20%,18%,16% and14%). The results showed a significant variation in the engineering properties after adding admixtures to pond ash. The results showed a significant variation in the engineering properties after adding admixtures to pond ash. The MDD of the mix was decreased from 1.63 to 1.575 gm/cc through the set of proportions, whereas the OMC was increased from 15% to 17.4%. The unconfined compressive (UC) strength of raw pond ash increased significantly with the addition of jute fiber, cement and sand as also with prolongation in curing period. The strength was increased from 113 kN/m² on the 1st day up to 943.82 kN/m² at 28 days, indicating the positive effect of curing time and material optimization on the strength. Mixes of 3% and 4% cement with pond ash have much higher compressive strength at 7, 14 and 28 days curing making them suitable for subgrade, subbase or embankment, but the mixes of cement (1-2%) are suitable for non-immersed condition.

https://doi.org/10.36937/cebel.2026.11087

Melek Akgül Orhan Gazi Odacıoğlu Amar Alguain

The destructive power of major earthquakes and urban transformation processes significantly affect the type and quantity of construction waste. Construction and demolition waste (CDW), which increases in quantity every year, faces both storage and disposal problems. Sustainable solution strategies and alternatives should be developed for the replacement of CDW. In this context, the effects of replacing the aggregate with different proportions of concrete waste, ceramic waste, and glass waste—among the most important construction and demolition wastes—were evaluated for mortar in the experimental study. CEM-I 42.5R cement was used in the study, and the water-cement-aggregate ratio by weight was kept constant at 1-1-4. A total of 90 cubic specimens measuring 50 x 50 x 50 mm were produced for 1 reference and 9 replacement scenarios. A series of physical measurements and compressive strength tests and microstructure analysis were performed on all specimens cured in saturated water at 20 ± 2 °C for 7 and 28 days. Experimental results show that all series, except for the series with 15% concrete aggregate substitution in mortar mixes, have higher compressive strength than the reference series. Therefore, the compressive strength provides acceptable performance at the substitution rates used for concrete, ceramic, and glass waste. Therefore, this study, based on compressive strength analyses, demonstrates that concrete, glass, and ceramic waste aggregates can be reused in masonry mortars.

https://doi.org/10.36937/cebel.2026.11098

Elmurad Khurshudov Abdussamet Arslan

Because of the presence of active fault zones, Turkey possesses a strong seismic hazard potential. The significance of machine learning and deep learning-based techniques in understanding seismic behavior is discussed in the paper. The traditional techniques followed in earthquake prediction are also discussed in the paper. The data set required for the analysis was provided by the Kandilli Observatory and Earthquake Research Institute (KRDAE). The data set contained 66,105 earthquake data from 1900 to 2024. LSTM, Random Forest and GRU are some of the seven different techniques taken into consideration while making the assessment. The performance of the models was also compared using MAE, MSE, and RMSE. The mean squared error (MSE) calculated for the Random Forest algorithm was found to be 0.1335. The results show that deep learning and artificial intelligence-based techniques are effective in simulating small and medium-sized earthquake magnitudes and also offer a meaningful approach for predicting the magnitudes, frequencies, and locations of earthquakes.

https://doi.org/10.36937/cebel.2026.11114

Sebghatullah Jueyendah Elif Agcakoca Zeynep Yaman

The accurate prediction of compressive strength (CS) is a fundamental requirement for structural safety, yet conventional empirical models remain limited in their ability to capture the complex nonlinear response of cementitious materials. Hybrid machine learning (ML) and deep learning (DL) models are investigated for predicting the CS of recycled powder mortar (RPM) using 10-fold cross-validation. Material type (classified as 'kind'), particle size, water/binder (W/B), and mass replacement ratio (MRR) are used as input variables, with CS as the target response. Model performance was quantified using RMSE, R², MAE, and MAPE, and analysis of variance (ANOVA) was conducted to examine the statistical significance of the input variables. The autoencoder–ML model achieved its best performance in fold 8, with R² = 0.978 (RMSE = 1.696), indicating the ability of hybrid optimization to represent nonlinear CS behavior. The interpretable hybrid ML–DL framework achieved high accuracy and robust generalization across folds, with error and stability analyses confirming unbiased predictions. MRR is the dominant factor controlling CS, with suggested optimal ranges; this influence is consistently supported by ALE, PDP, SHAP, and ICE analyses. Feature importance and PDP demonstrate that kind exerts a meaningful secondary effect on output, reflecting differences in composition, particle morphology, and reactivity, while its subtler influence in ALE and ICE analyses arises from its categorical nature and indirect mechanistic role. The framework offers a robust, interpretable tool for predicting CS, supporting data-driven mix design and sustainable mortar optimization.

https://doi.org/10.36937/cebel.2026.11118