This paper presents a data-driven approach to determine the load and flexural capacities of reinforced concrete (RC) beams strengthened with fabric cementitious matrix (FRCM) composites in flexure. A total seven machine learning (ML) models such as kernel ridge regression, K-nearest neighbors, support vector classification regression trees, random forest, gradient boosted extreme boosting (xgBoost) are evaluated propose best predictive model for FRCM-strengthened beams. Beam geometry,...

This study aims to propose a novel framework for strength prediction and multi-objective optimization (MOO) of economical environmentally sustainable ultra-high-performance concrete (UHPC) which aids in intelligent, sustainable, resilient construction. Different tree- boosting ensemble-based machine learning (ML) models are integrated form an accurate reliable model the uniaxial compressive UHPC. The optimized into super learner model, resulting robust predictive that is used as one...

Ultra-high-performance concrete (UHPC) has gained prominence owing to its exceptional physical and mechanical properties improved sustainability, making it ideal for large-scale structural applications. While numerous analytical studies have focused on predicting the stress-strain response of unconfined UHPC, there remains a lack reliable model confined which poses challenges efficient design broader adoption, particularly in seismically active regions. To bridge this gap, present study...

Stress-strain constitutive model for confined ultra-high-performance concrete (UHPC) plays a pivotal role in the design and modelling of UHPC structures. However, while extensive research exists on stress-strain responses unconfined UHPC, notable dearth studies focuses UHPC. Accordingly, this study introduces novel hybrid machine learning (ML)-based with normal-strength steel (NSS) or high-strength (HSS) spirals. Given limited experimental data available, numerical was developed validated...

Superelastic Shape Memory Alloys (SE SMAs) are unique alloys that have the ability to undergo large deformations and return their undeformed shape by removal of stresses. This study aims at assessing seismic behavior beam-column joints reinforced with SE SMAs. Two large-scale were tested under reversed cyclic loading. While first joint was regular steel rebars, SMA rebars used in second one. Both selected from a Reinforced Concrete (RC) building located high region western Canada designed...

The influence of recycled coarse aggregates (RCAs) on the fresh, hardened, and freeze-thaw durability properties aggregate concrete (RAC) is investigated in research reported this paper. Four different mixes were considered with natural three replacement levels RCA [i.e., (1) 30%, (2) 40%, (3) 50%]. fresh hardened RAC according to national standards where target strength was 35 MPa 56 days. compressive strengths determined after 3, 7, 28, 56, 120 days moist curing. results are also presented...

This paper focuses on the fragility-based seismic vulnerability assessment of retrofitted multicolumn bridge bents. Fragility curves are developed to assess relative performance various retrofit methods under both near-fault and far-field ground motions. A probabilistic demand model (PSDM) is used in generating fragility functions. Through nonlinear dynamic analysis, for bents with four different techniques, specifically carbon fiber–reinforced polymer (CFRP) jacketing, steel concrete...

In this study, two types of smart elastomeric bearings are presented using shape memory alloy (SMA) wires. Due to the unique characteristics SMAs, such as superelastic effect and recentering capability, residual deformation in SMA-based natural rubber (SMA-NRBs) is significantly reduced whereas energy dissipation capacity increased. Two different configurations SMA wires incorporated considered. The several parameters, including shear strain amplitude, type SMA, aspect ratio base isolator,...