This study analyzes the progression, utilization, and inherent challenges of traditional non-linear static procedures (NSPs) such as the capacity spectrum method, the displacement coefficient method, and the N2 method for evaluating seismic performance in structures. These methods, along with advanced versions such as multi-mode, modal, adaptive, and energy-based pushover analysis, help determine seismic demands, enriching our grasp on structural behaviors and guiding design choices. While these methods have improved accuracy by considering major vibration modes, they often fall short in addressing intricate aspects such as bidirectional responses, torsional effects, soil-structure interplay, and variations in displacement coefficients. Nevertheless, NSPs offer a more comprehensive and detailed analysis compared to rapid visual screening methods, providing a deeper understanding of potential vulnerabilities and more accurate predictions of structural performance. Their efficiency and reduced computational demands, compared to the comprehensive nonlinear response history analysis (NLRHA), make NSPs a favored tool for engineers aiming for swift seismic performance checks. Their accuracy and application become crucial when gauging seismic risks and potential damage across multiple structures. This paper underscores the ongoing refinements to these methods, reflecting the sustained attention they receive from both industry professionals and researchers.

Nonlinear Static Analysis otherwise known as pushover analysis will be used in this study to evaluate the seismic performance of an existing reinforced concrete (RC) hospital structure. This method aids in determining the structure’s ability to withstand lateral loads and calculating its local and global deformation requirements. The study begins with a thorough analysis of the geometry, materials, and structural elements of the structure, followed by a review of pertinent building regulations and codes. A finite element model in three dimensions of the hospital building is created, encapsulating the main features of the structure’s behavior under seismic loading. The lateral force method of analysis and static pushover analysis is then carried out and compared, and the findings are used to pinpoint crucial weak places, potential failure mechanisms, and regions needing additional research or fortification. Recommendations are given to improve the seismic performance of the current RC hospital building based on the pushover analysis’s findings. These adjustments can be made to the structural system via retrofitting techniques or to non-structural elements. For engineers, architects, and legislators concerned with the seismic assessment and renovation of hospital buildings and other crucial infrastructure, the findings from this study are valuable.

This study proposes a digital twin (DT) and machine learning (ML) framework to predict nonlinear pushover responses of reinforced concrete (RC) shear walls using analytically derived data. Two hundred SAP2000 layered shell models were analyzed, and monotonic lateral capacity curves were processed via SPO2FRAG for bilinear parameter extraction. Key response features—initial stiffness (K₀), yield displacement (\(\delta ^{y}\)), and post-yield stiffness ratio (\(\alpha\))were identified. Ten input variables including wall geometry, material properties, reinforcement ratios, axial load, and opening ratio were used to train Random Forest regressors for predicting the pushover curve descriptors. Model accuracy was validated using nested cross-validation, yielding mean test R² values of 0.996 for \({K}_{0}\), 0.995 for \(\delta ^{y}\), and 0.925 for \(\alpha\), while uncertainty measures (Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), 95% confidence intervals) supported robustness. The DT surrogate reconstructs pushover curves in under 2 seconds per specimen, supporting rapid parametric analysis and seismic scenario assessments without the need for repetitive finite element simulations. The study also documents model limitations and outlines guidance for extending the approach to shear-dominated walls and experimental validation.

This study evaluates the performance of SAP2000, and AxisVM in conducting pushover analyses, with a focus on the manual (user) definition of plastic hinges versus the automatic definitions provided by the software. Three structures, namely a single column subjected to a point load, a 2D reinforced concrete (RC) frame model, and a 3D RC model, are considered. It explores the performance of the three models in different conditions, including, application of the different software, the use of user defined (manual) hinges, and automatically generated hinges to assess their response to various analytical excitations. The need for engineers to focus on understanding the application of defining the hinge properties according to existing guidelines, including FEMA-356, ASCE 41-13/17, and Eurocodes, is highly emphasized, in addition to the existing methods as found in the literature. This is achieved by considering and comparing the capacity curves generated by the software. The results vary in some instances, especially when comparing results from user-defined and automatic hinges. It is also found that when used with precision, the results are almost similar, it is also important to use software that allows ultimate precision for better results.

Reinforced concrete (RC) shear walls are primary lateral force-resisting elements in seismic regions; however, architectural openings and height variations reduce stiffness, strength, and deformation capacity, increasing seismic vulnerability and losses. This study evaluates five cantilever RC shear wall configurations: three 3.0~m-high walls (solid, centrally perforated, and staggered openings) and two solid walls with heights of 2.0~m and 4.0~m. Layered-shell models are developed in OpenSees and validated against SAP2000 through comparison of global capacity curves. Nonlinear static pushover analyses are performed to estimate performance points using ASCE/SEI~41, the ATC-40 Capacity Spectrum Method, and the N2 method, and are benchmarked against Incremental Dynamic Analysis (IDA) using the scaled LACC North ground-motion record up to 3.0~g. IDA-based drift--spectral acceleration relationships are used to derive lognormal fragility functions for Immediate Occupancy, Life Safety, Collapse Prevention, and Failure limit states, which are integrated with seismic hazard and consequence models to estimate expected annual loss and 50-year discounted life-cycle costs. Results show that solid walls achieve the highest stiffness and strength, while centrally perforated walls experience the earliest degradation, with staggered openings exhibiting intermediate behavior. Openings shift fragility curves toward lower intensities, resulting in two- to three-fold increases in seismic losses. The study demonstrates that opening layout governs seismic performance and highlights the value of fragility- and life-cycle-based metrics beyond prescriptive code limits.

Growing global demand for resilient and sustainable infrastructure requires seismic retrofit strategies that simultaneously enhance structural performance and minimise environmental impact. This study presents a multi-objective optimisation framework that integrates circular economy (CE) indicators with seismic performance evaluation for the retrofit design of reinforced concrete (RC) shear walls. Nonlinear finite element simulations were carried out in SeismoStruct to evaluate a 125 mm thick component-level RC shear wall strengthened using three techniques: carbon-fibre-reinforced polymer (CFRP) overlays, externally bonded steel plates, and RC jacketing. Structural performance was assessed through nonlinear pushover and cyclic analyses interpreted using the Eurocode 8 N2 method and the ASCE 41–23 displacement coefficient method, while environmental and circularity performance was quantified using the Material Circularity Index (MCI), the Carbon Equivalence Damage Index (CEDI), and a Design for Circularity (DfC) score. A Non-dominated Sorting Genetic Algorithm II (NSGA-II) was used to generate Pareto-optimal retrofit solutions across four objectives: seismic capacity, embodied carbon, retrofit cost, and material circularity. Results show lateral-capacity increases of approximately 50–75% for steel strengthening and RC jacketing, while CFRP overlays leave the flexural capacity unchanged but double the shear capacity: steel plate strengthening provides the highest structural capacity and circularity performance ($\mathrm{MCI}=0.45$, $\mathrm{DfC}=8.7/10$), RC jacketing yields the greatest stiffness gain but the lowest circularity ($\mathrm{MCI}=0.08$), and CFRP overlays offer the most balanced structural, environmental, and economic performance. The framework was validated on a six-storey RC building containing 200 mm thick shear walls, confirming that integrating seismic assessment with circular economy indicators supports more sustainable and resilient retrofit decision-making.