Asian Journal of Civil Engineering最新文献

{"title":"Fire resistance evaluation of clay and concrete masonry units under elevated temperatures using mechanical, chemical, and microstructural characterization techniques","authors":"Chantal Mohammed,&nbsp;Alana Ramnarine,&nbsp;Daniel White,&nbsp;Jeffrey Smith,&nbsp;Rajiv Budhooram","doi":"10.1007/s42107-026-01755-y","DOIUrl":"10.1007/s42107-026-01755-y","url":null,"abstract":"<div><p>Limited studies have directly compared the fire performance of clay and concrete masonry units using integrated mechanical, chemical, and microstructural characterization under identical elevated-temperature conditions. This study addressed this gap by exposing both materials to temperatures ranging from 50 °C to 1000 °C and evaluating their response using compressive and flexural strength testing, FTIR, SEM, BET surface area analysis, ion migration, and moisture retention measurements. Concrete masonry units exhibited significant thermal degradation, with compressive strength decreasing from 25.4 to 16 MPa, flexural strength reducing from 1.42 to 0.03 kN, surface area declining from 14.7 to 1.2 m²/g, and ion migration reaching 2.751 ppm. ANOVA confirmed a statistically significant temperature effect (F = 20.3, <i>p</i> = 8.18 × 10⁻⁷). In contrast, clay masonry units maintained stable compressive strength (18.8–21.07 MPa), showed minimal flexural variation (0.36–0.67 kN), lower ion migration (1.161 ppm), and negligible shrinkage (0.0–0.05%), while exhibiting sintering-induced densification at elevated temperatures. These results demonstrate that clay masonry units possess superior thermal stability and post-fire structural reliability compared to concrete masonry units, providing quantitative evidence to support fire-resilient material selection and sustainable post-fire reuse strategies.</p></div>","PeriodicalId":8513,"journal":{"name":"Asian Journal of Civil Engineering","volume":"27 10","pages":"5029 - 5037"},"PeriodicalIF":0.0,"publicationDate":"2026-08-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148962310","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Enhancing soybean crude urease-calcite precipitation for soil improvement: effects of seed pretreatments and extraction methods on strengthening sandy soil","authors":"Heriansyah Putra,&nbsp;Zayyaan Nabiila Khairunnisa,&nbsp;Gagas Afdzal Madani,&nbsp;Erizal Erizal,&nbsp;Aswin Lim,&nbsp;Tri Harianto,&nbsp;Dede Heri Yuli Yanto,&nbsp;Lutfian Rusdi Daryono","doi":"10.1007/s42107-026-01756-x","DOIUrl":"10.1007/s42107-026-01756-x","url":null,"abstract":"<div><p>Soybean crude urease-calcite precipitation (SCU-CP) is an environmentally sustainable bio-grouting technique that employs soybean extract as a urease biosource to initiate calcium carbonate precipitation. However, conventional extraction methods frequently result in elevated organic matter content, which can hinder the efficacy and distribution of calcite within the soil. Furthermore, the seed coat of soybeans contains lignocellulosic compounds that contribute to the mass of the organic precipitates. This study evaluated the impact of various soybean seed pretreatments and extraction methods on precipitation parameters and their role in enhancing the strength of sandy soil. The treatments involved the use of soybeans without seed coats, preparation of soybeans under dormancy break conditions, use of two distinct extraction methods (a No. 400 sieve and pressurized filtration), and incorporation of ethanol alongside distilled water. The findings indicated that the removal of the soybean seed coat effectively enhanced the purity of the SCU-CP solution, with an average reduction in organic content of 3.0%. Pressurized filtration also reduced the organic content by 21.54% at a soybean concentration of 20 g/L and by 59.33% at a concentration of 100 g/L. The use of dehulled soybean extract with sieve No. 400 enhanced the unconfined compressive strength (UCS) of the soil by up to 9.83% compared to that of the extract from soybeans with hulls. This study demonstrated that soybean pretreatment can optimize the effectiveness of the calcite precipitation method for soil improvement.</p></div>","PeriodicalId":8513,"journal":{"name":"Asian Journal of Civil Engineering","volume":"27 10","pages":"5039 - 5055"},"PeriodicalIF":0.0,"publicationDate":"2026-08-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148962139","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Advanced image processing and deep learning methods for automated crack detection in civil engineering structures: a comprehensive review","authors":"Logaraja Ramasamy,&nbsp;Ponmalar Vijayan","doi":"10.1007/s42107-026-01758-9","DOIUrl":"10.1007/s42107-026-01758-9","url":null,"abstract":"<div><p>Crack detection plays a critical role in the structural health monitoring of civil engineering infrastructure to ensure safety, durability, and maintenance planning. Traditional manual inspection methods are time-consuming, labor-intensive, and prone to human error. Recently, automated crack detection systems have been significantly enhanced using image processing, computer vision, and deep learning techniques. This review article provides a comprehensive analysis of image processing and deep learning-based crack detection techniques for civil engineering structures. It provides a critical review of various methods, including edge detection, thresholding, morphological operations, Convolutional Neural Networks (CNNs), Fully Convolutional Networks (FCNs), U-shaped convolutional networks (U-Net), You Only Look Once (YOLO), and Digital Image Correlation (DIC). The performances of the different algorithms were compared in terms of accuracy, precision, computational efficiency, and crack characterization capability. Moreover, publicly available datasets, preprocessing methods, segmentation techniques, and evaluation metrics are discussed. This study also highlights key challenges, including variations in illumination, noisy backgrounds, limited datasets, and computational complexity. Finally, future research directions involving hybrid AI models, transfer learning, UAV-based inspection, and real-time crack monitoring are discussed. This review provides useful information for researchers and practitioners working on structural health monitoring and intelligent infrastructure systems.</p></div>","PeriodicalId":8513,"journal":{"name":"Asian Journal of Civil Engineering","volume":"27 10","pages":"4745 - 4783"},"PeriodicalIF":0.0,"publicationDate":"2026-08-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148962285","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Experimental study on the flexural behavior of reinforced concrete beams with RCA under biaxial loading","authors":"Hiba Abdulhussein Sahib,&nbsp;Jamal Abdulsamad Khudhair,&nbsp;Zahir M. N. Hassan","doi":"10.1007/s42107-026-01754-z","DOIUrl":"10.1007/s42107-026-01754-z","url":null,"abstract":"<div><p>This study investigates the flexural behavior of reinforced concrete beams containing recycled concrete aggregate (RCA) under inclined loading conditions. Four concrete mixtures with RCA replacement ratios of 0%, 30%, 50%, and 75% were developed and tested. Beams with square (250 × 250 mm) and rectangular (150 × 350 mm) cross-sections were subjected to inclined loading at angles of 0°, 45°, and 68°. The experimental results included first cracking load, ultimate load, mid-span deflection, failure mode, crack pattern development, steel strain response, ductility index, and energy absorption capacity. The results showed that increasing RCA content generally reduced cracking and ultimate loads due to weaker interfacial transition zones (ITZ) and lower stiffness. For square beams, higher load inclination angles improved load-carrying capacity and energy dissipation. In rectangular beams, inclined loading transferred part of the load toward the direction of lower flexural stiffness, resulting in reductions in both cracking and ultimate loads. Failure mode observations indicated predominantly flexural behavior for all specimens, although crack inclination and compression damage increased with loading angle. Moderate RCA replacement (30%) slightly improved ductility and energy absorption, whereas higher replacements (50%–75%) significantly decreased these parameters. Overall, the performance of reinforced concrete beams was influenced by RCA content, cross-sectional geometry, and load inclination under inclined loading conditions.</p></div>","PeriodicalId":8513,"journal":{"name":"Asian Journal of Civil Engineering","volume":"27 10","pages":"5013 - 5027"},"PeriodicalIF":0.0,"publicationDate":"2026-08-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148962072","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Porous geopolymer construction materials for water-containment and treatment infrastructure: a critical review of pore engineering, durability, filtration mechanisms, and sustainability","authors":"Houda Habib,&nbsp;Marianne Saba,&nbsp;Ibrahim Elnaml,&nbsp;Elsayed Adel Fahmy,&nbsp;Ahmed Mohammed Youssef,&nbsp;Joseph Assaad,&nbsp;Ali Jahami","doi":"10.1007/s42107-026-01760-1","DOIUrl":"10.1007/s42107-026-01760-1","url":null,"abstract":"<div><p>Porous geopolymers are being developed as multifunctional components that can combine hydraulic transport, contaminant retention, chemical resistance, and construction-scale formability. This critical review evaluates their use in water-containment systems and in tertiary treatment, defined here as post-secondary polishing for removal of residual suspended matter, nutrients, dissolved metals, trace organics, and microorganisms before discharge or reuse. The synthesis links precursor chemistry and batch variability to gel assemblage, pore connectivity, tortuosity, anisotropy, surface charge, permeability, and mechanical integrity. Chemical foaming, sacrificial templating, freeze casting, pressing, retardation-induced porosity, and additive manufacturing provide different degrees of pore control, but no universal strength–porosity relationship applies across low-calcium N-A-S-H and calcium-rich C-(A)-S-H/N-A-S-H systems. Adsorption and ion exchange remain the most mature mechanisms for dissolved metals and dyes, whereas photocatalytic and antimicrobial functions require stable modifiers and explicit leaching assessment. The review further shows that total porosity and short-term removal efficiency are insufficient design criteria; construction deployment requires permeability retention, anisotropic transport characterization, resistance to hydraulic cleaning, chemical and ultraviolet stability, regeneration performance, modular manufacturability, and service-life evidence under real wastewater chemistry. A tiered reporting framework and provisional engineering screening envelope are proposed. The principal need is a chemistry-aware, performance-based route that connects pore engineering to structural reliability, treated-water quality, life-cycle impact, and scalable manufacture.</p><h3>Graphic abstract</h3><div><figure><div><div><picture><img></picture></div></div></figure></div></div>","PeriodicalId":8513,"journal":{"name":"Asian Journal of Civil Engineering","volume":"27 10","pages":"4785 - 4806"},"PeriodicalIF":0.0,"publicationDate":"2026-08-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s42107-026-01760-1.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148962074","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Behaviour of high grade concrete with recycled refractory brick aggregates and alccofine: an experimental and machine leaning based evaluation","authors":"Amgoth Rajender,&nbsp;Sakchi Sah,&nbsp;Kapildeo P. Yadav,&nbsp;Amiya K. Samanta","doi":"10.1007/s42107-026-01752-1","DOIUrl":"10.1007/s42107-026-01752-1","url":null,"abstract":"<div><p>The extensive use of natural resources in construction and development projects across the globe poses serious environmental challenges and ecological imbalance and has led to the search for alternative construction materials. In view of this, the present study investigates performance of novel M40 grade concrete blend containing Portland Slag Cement, Alccofine as constant partial replacement (15%) for cement, and Recycled Refractory Brick (RRB) as a partial substitution for natural fine aggregate (10, 20, 30, 40, and 50%), with and without Polypropylene Fibre (PPF) (1% of binder content). The behaviour of the proposed concrete blend has been evaluated through destructive and non-destructive tests, including compressive strength, ultrasonic pulse velocity, rebound hammer, after 7 days and 28 days of normal water curing. To assess the microstructural integrity and the development of hydration products in the concrete matrix, scanning electron microscopy analysis has also been conducted. Experimental outcomes and microstructural evidence indicate that 40% RRB with 15% Alccofine as a binder replacement yielded optimal performance, followed by 30% RRB. The high pozzolanic activity of Alccofine and its fineness contribute to strength enhancement, while PPF Failure pattern observations and SEM analysis suggest improved crack-control capability and fiber-matrix interaction. The experimental compressive strength performance has also been validated using machine learning approaches. Among the proposed models, the random forest performed best. In the author's opinion, the proposed novel blend with moderate RRB and Alccofine produces eco-friendly M40-grade concrete while protecting natural resources and effectively utilizing waste.</p></div>","PeriodicalId":8513,"journal":{"name":"Asian Journal of Civil Engineering","volume":"27 10","pages":"5081 - 5098"},"PeriodicalIF":0.0,"publicationDate":"2026-08-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148962391","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Risk-aware surrogate-assisted multi-objective design optimization of profiled steel-concrete composite slabs","authors":"Pinal C. Patel,&nbsp;Vijaykumar R. Panchal,&nbsp;Rupesh Kumar Tipu","doi":"10.1007/s42107-026-01757-w","DOIUrl":"10.1007/s42107-026-01757-w","url":null,"abstract":"<div><p>Profiled steel-concrete composite slabs require a balance among self-weight, service performance, and ultimate load resistance. Direct finite element (FE)-based optimization is computationally expensive because every candidate requires a nonlinear analysis. This study presents a risk-aware surrogate-assisted framework that incorporates calibrated prediction uncertainty directly into multi-objective optimization. An audited dataset of 5000 ANSYS design points was used to train XGBoost models for service deflection and steel-deck stress and heteroscedastic deep ensembles for ultimate capacity and concrete compressive strain. Calibration on held-out residuals produced uncertainty-adjusted screening bounds. NSGA-II used upper-bound predictions for service and strain constraints and a lower-bound prediction for the capacity objective. The service models achieved test <span>(R^2)</span> values of 0.974 for deflection and 0.970 for deck stress, while the ultimate-response models achieved values of 0.751 for capacity and 0.689 for concrete strain. Across 30 independent NSGA-II runs, the final normalized hypervolume was <span>(0.8940pm 0.0032)</span>, and 29 runs satisfied the adopted terminal-stability criterion. Mean-only optimization produced 66 of 200 candidates that failed the adopted surrogate-bound criteria after uncertainty-aware rechecking, whereas all 200 risk-aware candidates satisfied those criteria. An equal-weight closest-to-ideal rule selected solution RA-P088 as the representative compromise, with a mass of 723.4 kg, conservative deflection of 0.2394 mm, and conservative capacity of 326.48 kN. FE re-analysis of this candidate also satisfied the adopted screening limits. The framework supports rapid trade-off screening and targeted FE confirmation of shortlisted designs but does not replace project-specific structural verification.</p></div>","PeriodicalId":8513,"journal":{"name":"Asian Journal of Civil Engineering","volume":"27 10","pages":"5057 - 5080"},"PeriodicalIF":0.0,"publicationDate":"2026-07-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148962167","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Correction: Multi response optimization of basalt fiber reinforced high performance concrete incorporating GGBS and silica fume using response surface methodology","authors":"P. A. Sudam,&nbsp;U. R. Darla,&nbsp;M. K. R. Gottam,&nbsp;G. Himala Kumari,&nbsp;P. V. R. Narendra,&nbsp;M. Sai Babu,&nbsp;C. V. Bharathi,&nbsp;Tejeswara Rao Maganti","doi":"10.1007/s42107-026-01747-y","DOIUrl":"10.1007/s42107-026-01747-y","url":null,"abstract":"","PeriodicalId":8513,"journal":{"name":"Asian Journal of Civil Engineering","volume":"27 9","pages":"4697 - 4697"},"PeriodicalIF":0.0,"publicationDate":"2026-07-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148807512","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Rapid on-site prediction of concrete compressive strength using integrated machine learning and non-destructive testing","authors":"Swati,&nbsp;Ravindra Nagar,&nbsp;Rajesh Gupta","doi":"10.1007/s42107-026-01753-0","DOIUrl":"10.1007/s42107-026-01753-0","url":null,"abstract":"<div><p>Accurate prediction of concrete compressive strength (<i>f</i>_<i>c</i>) is crucial for construction quality control and structural safety assessment. While non-destructive testing (NDT) methods offer practical evaluation, combining them with mix parameters can enhance prediction accuracy. This study aims to develop a robust machine learning (ML) framework integrating NDT methods rebound hammer (RH) and ultrasonic pulse velocity (UPV) with mix design parameters and curing age precise early strength prediction. An experimental dataset of 360 samples from 24 concrete mixtures (M20-M35) was collected, covering curing ages from 3 to 56 days. Six machine learning (ML) models were evaluated using comprehensive mix parameters and NDT measurements. The extra tree regressor (ETR) outperformed other models, achieving exceptional accuracy (R² =0.99, RMSE = 0.53). SHAP analysis revealed curing age as the most influential parameter, followed by water- cement ratio and NDT measurements. The proposed model demonstrates promising strength prediction capability as early as 3 days under laboratory conditions. The data-driven approach may contribute to improved quality assessment and construction planning and shows potential for future practical applications following validation using field data.</p></div>","PeriodicalId":8513,"journal":{"name":"Asian Journal of Civil Engineering","volume":"27 10","pages":"4993 - 5011"},"PeriodicalIF":0.0,"publicationDate":"2026-07-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148962057","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Integrating machine learning and experimental characterization for predicting the compressive strength of dune-sand-based high-performance concrete","authors":"M’hamed Mahdad,&nbsp;Aghiles Hammas,&nbsp;Abderrachid Hamrani,&nbsp;Kaci Chalah,&nbsp;Seifeddine Friha,&nbsp;Liza Saher","doi":"10.1007/s42107-026-01751-2","DOIUrl":"10.1007/s42107-026-01751-2","url":null,"abstract":"<div><p>Dune sand (DS) is readily accessible in southeastern Algeria and can be serves as a viable substitute for poorly available of crushed sand (CS) widely used in Saharan construction. This study aims to the valorization of dune sand collected from Mih-Ouensa of El Oued region which is weakly used in concrete field, for development of high-performance concrete based this raw material (HPC-DS). In order to identify the main composition of used DS, physico-chemical analyses were conducted by X-ray diffraction (XRD), X-ray fluorescence (XRF) and SEM/EDX observations. Based on the characterized materials, several HPC-DS mixtures were formulated with different contents of DS, CS, Portland cement, and silica fume (SF). The experimental data were obtained by the application of compressive strength test on the 100 mm cubic specimens. In addition to the experimental analysis, nine supervised machine learning algorithms were applied to forecast the compressive strength of HPC-DS based on the mixture composition. The obtained results demonstrated that the addition of dune sand up to 40% as a partial substitute for crushed sand improved the mechanical properties of HPC-DS, while greater dune sand content resulted in a significant decrease in strength. Furthermore, of all assessed models, the Extra Trees Regressor achieved the highest predictive accuracy (R² = 0.9243), exhibiting robust alignment with the experimental trends. Feature-importance analysis indicated that hydration age is the primary factor influencing compressive strength, followed by superplasticizer content and dune sand addition. Moreover, a positive correlation was statically deduced between the hydration age and dune sand content.</p></div>","PeriodicalId":8513,"journal":{"name":"Asian Journal of Civil Engineering","volume":"27 10","pages":"4973 - 4991"},"PeriodicalIF":0.0,"publicationDate":"2026-07-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148962384","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}