Shahid Beheshti UniversityInterdisciplinary Journal of Civil Engineering3092-72772120260110Effect of Natural and Artificial Supplementary Cementitious Materials (SCMs) on the Mechanical and Durability Properties of Precast Concrete Structures: A Review Study12210706810.48308/ijce.2026.244622.1023ENMohammad RezaShapouriFaculty of Civil, Water and Environmental Engineering, Shahid Beheshti University, Tehran, IranAliTaheriFaculty of Civil, Water and Environmental Engineering, Shahid Beheshti University, Tehran, IranMahmoud RezaShiravandFaculty of Civil, Water and Environmental Engineering, Shahid Beheshti University, Tehran, IranJournal Article20251113Portland cement production is a major source of CO₂ emissions, posing significant environmental challenges. In this context, the usage of Supplementary Cementitious Materials (SCMs) has gained attention as an effective strategy to lower cement consumption and produce sustainable, high-performance concrete. These materials play a key role in boosting concrete durability and strength by improving microstructure, diminishing porosity, and forming secondary binding phases. Nevertheless, their widespread application in the precast concrete industry—which requires high early strength, suitable setting time, and desirable workability at early ages—faces challenges. Accordingly, this study reviews research on the influence of SCMs, including fly ash, slag, microsilica, natural pozzolans, and metakaolin, on the properties of fresh concrete, early-age characteristics, as well as long-term mechanical behavior and durability. Solutions for mix optimization are inspected, such as multi-component systems, high-range water reducers, and accelerated curing methods including steam curing, to compensate for the decline in early strength. The results from these studies suggest that through careful selection of SCM type, determination of an optimal replacement percentage, and implementation of a suitable curing process, it is possible to generate precast concrete elements. These elements fulfill technical and economic requirements and offer environmental benefits, representing an important step toward sustainable developmenthttps://ijce.sbu.ac.ir/article_107068_bf6f3030b3abe6226b4456495a2f01d7.pdfShahid Beheshti UniversityInterdisciplinary Journal of Civil Engineering3092-72772120260117Machine Learning-Based Land Cover Mapping for Environmental and Urban Planning Applications: A Cloud Computing Framework Using Multi-Source Geospatial Data234610706910.48308/ijce.2026.244728.1024ENAlirezaHabibi RaziDepartment of Remote Sensing & GIS, SR. C., Islamic Azad University, Tehran, Iran0009-0004-8183-7220ZahraAziziDepartment of Geoinformation and Geomatics Engineering, Faculty of Civil, Water and Environmental Engineering, Shahid Beheshti University, Tehran, Iran0000-0001-8572-7134HosseinAghamohammadiDepartment of Civil Engineering, SR. C., Islamic Azad University, Tehran, Iran0000-0002-9497-6295Ali AsgharAlesheikhDepartment of Geospatial Information Systems, Faculty of Geodesy and Geomatics Engineering, K. N. Toosi University of Technology, Tehran, Iran0000-0001-9537-9401AlirezaGharagozlouDepartment of Geoinformation and Geomatics Engineering, Faculty of Civil, Water and Environmental Engineering, Shahid Beheshti University, Tehran, Iran0000-0003-2569-8639Journal Article20251201Accurate land cover information is essential for environmental management, urban planning, and sustainable development. This study presents a geospatial data fusion framework for land cover mapping using Google Earth Engine (GEE). The framework integrates Landsat 9 imagery, five spectral indices (NDVI, NDWI, NDBI, BSI, and SAVI), and ALOS Digital Surface Model (DSM) data. The methodology was applied in Hamedan Province, Iran, to classify four land cover classes: water bodies, vegetation, urban areas, and bare lands. Five supervised machine learning algorithms—Random Forest (RF), Support Vector Machine (SVM), Classification and Regression Tree (CART), Gradient Tree Boosting (GTB), and Minimum Distance (MD)—were evaluated. Classification performance was assessed using overall accuracy, producer accuracy, user accuracy, and the Kappa coefficient. Results showed that integrating spectral and topographic features improved class separability and classification accuracy. RF achieved the best performance with an overall accuracy of 98% and a Kappa coefficient of 0.97, followed by GTB. In contrast, MD produced lower accuracy and was more affected by spectral confusion. The findings demonstrate the effectiveness of cloud-based machine learning and multi-source geospatial data fusion for accurate land cover mapping and support applications in environmental monitoring, resource management, and spatial planning.https://ijce.sbu.ac.ir/article_107069_a2069fd776157f67810cd09ab733b5a3.pdfShahid Beheshti UniversityInterdisciplinary Journal of Civil Engineering3092-72772120260101Comparison of Peak Seismic Displacement Obtained from Simplified Analysis for Lead Rubber Bearing (LRB) Isolators with Nonlinear Response History Analysis476110708710.48308/ijce.2026.107087ENMobinaZinatiFaculty of Civil, Water, and Environmental Engineering, Shahid Beheshti University, Tehran, IranNematHassaniFaculty of Civil, Water, and Environmental Engineering, Shahid Beheshti University, Tehran, IranMohammadjavadHamidiaFaculty of Civil, Water, and Environmental Engineering, Shahid Beheshti University, Tehran, IranJournal Article20251111Seismic isolation systems are widely used to reduce earthquake-induced structural demands by increasing the fundamental period and providing additional energy dissipation. Among available isolation devices, Lead Rubber Bearings (LRBs) are commonly employed because they combine lateral flexibility with hysteretic damping. Simplified displacement-based design procedures are frequently used in practice to estimate the effective properties and displacement demands of LRB systems; however, their accuracy relative to nonlinear dynamic analysis remains an important concern. This study evaluates the accuracy of a simplified iterative design procedure for LRB isolation systems through comparison with nonlinear time-history analyses performed in OpenSeesPy. The isolated structure was modeled as an equivalent single-degree-of-freedom (SDOF) system. A parametric investigation was conducted using six characteristic strength ratios (μ = 0.03–0.30) and ten post-yield stiffness ratios (α = 0.05–0.50), resulting in sixty isolator configurations. Effective mechanical properties were first determined using the displacement-based iterative procedure. Nonlinear time-history analyses were then performed using 44 FEMA P695 ground-motion records scaled to the design earthquake spectrum for downtown Los Angeles. Peak displacement demands obtained from nonlinear analyses were statistically evaluated and compared with the design displacements predicted by the simplified procedure. Results showed that the simplified method consistently underestimated displacement demands, particularly for low characteristic strength ratios. The average prediction error exceeded 100% for μ = 0.03 and decreased to approximately 29% for μ = 0.30. The characteristic strength ratio was identified as the primary factor affecting prediction accuracy, while the influence of the post-yield stiffness ratio was relatively small. The findings indicate that simplified procedures are useful for preliminary design, but nonlinear time-history analysis is necessary for reliable estimation of displacement demands in seismically isolated structures.https://ijce.sbu.ac.ir/article_107087_a5909966438aef212c3aedcb719229e8.pdfShahid Beheshti UniversityInterdisciplinary Journal of Civil Engineering3092-72772120260110Comparative Assessment of CMR in Steel Seismic Force-Resisting Systems: A FEMA P-695 Statistical Analysis628410708810.48308/ijce.2026.107088ENParsaRashvandFaculty of Civil, Water and Environmental Engineering, Shahid Beheshti University, Tehran, IranPouya H.KhosraviFaculty of Civil, Water and Environmental Engineering, Shahid Beheshti University, Tehran, IranMohammadjavadHamidiaFaculty of Civil, Water and Environmental Engineering, Shahid Beheshti University, Tehran, IranNematHassaniFaculty of Civil, Water and Environmental Engineering, Shahid Beheshti University, Tehran, IranJournal Article20251015Evaluating the seismic collapse performance of steel structural systems is one of the primary objectives of the FEMA P-695 guideline. In this guideline, the collapse margin ratio (CMR) is defined as a quantitative measure of collapse capacity. Despite extensive research on the seismic performance of steel structural systems, a systematic statistical comparison among special moment-resisting frames (SMFs), special concentrically braced frames (SCBFs), and eccentrically braced frames (EBFs) has not yet been reported within the FEMA P-695 framework. To address this gap, 108 steel archetypes corresponding to a common seismic hazard level (<em>S<sub>MT</sub></em> = 0.5g) are selected and evaluated. The collapse margin ratio (CMR) values are statistically examined using descriptive and inferential statistical methods to identify significant differences in collapse performance among the three structural systems. Using one-way analysis of variance (ANOVA) and Tukey’s post-hoc test, the differences in mean CMR among the three systems are examined. Results indicated that the effect of structural system type on CMR is statistically significant (F = 8.19, p < 0.001, <em>η</em>² =0.135). The mean CMR values for SMF, SCBF, and EBF are found to be 2.34, 2.06, and 1.79, respectively. Tukey’s test revealed that only the difference between SMF and EBF is significant at the 95% confidence level, while SCBF exhibits an intermediate performance. These findings underscore the importance of selecting an appropriate structural system during the preliminary design phase and can serve as a quantitative basis for revising Iran’s seismic design provisions.https://ijce.sbu.ac.ir/article_107088_de5974f3c29de46bee3f427d6c273799.pdfShahid Beheshti UniversityInterdisciplinary Journal of Civil Engineering3092-72772120260101Assessment of the Seismic Performance Improvement Effect of Shape Memory Alloys in Tall Buildings with Combined Shear Core and Peripheral Shear Walls8510410715210.48308/ijce.2026.244919.1025ENHosseinPourrezaFaculty of Civil, Water, and Environmental Engineering, Shahid Beheshti University, Tehran, IranHamidSaffariFaculty of Civil, Water, and Environmental Engineering, Shahid Beheshti University, Tehran, IranJournal Article20251215In the past two decades, the use of Shape Memory Alloys (SMAs) has significantly contributed to improving the seismic performance of structures. Not only conventional low-rise structures, but also high-rise buildings have benefited from these performance enhancements. Among the advantages of SMAs in buildings are the reduction of structural irregularities and the provision of self-centering capabilities. This study investigates the effects of SMA application in a high-rise building equipped with both a central shear core and perimeter shear walls located at the four corners of the structure. The problem was examined under two scenarios: the use of conventional steel reinforcement and reinforcement enhanced with SMA, employed as the longitudinal reinforcement of the boundary elements of the shear core. Three earthquake acceleration time-history records with the best compatibility and consistency with the site conditions and regional seismicity were selected for the analysis. It is noteworthy that accidental torsion can lead to considerable torsional irregularity even in regular high-rise buildings.https://ijce.sbu.ac.ir/article_107152_6595411c4a75d1e88be4d8f3e5c185b8.pdf