Various techniques have been employed to detect damage in civil engineering structures. Apart from the model-based approach, which demands the frequent updating of its corresponding finite element method (FEM)-built model, data-driven methods have gained prominence. Environmental and operational effects significantly affect damage detection due to the presence of damage-related trends in their analyses. Time-domain approaches such as autoregression and metrics such as the Mahalanobis squared distance have been utilized to mitigate these effects. In the realm of machine learning (ML) models, their effectiveness relies heavily on the type and quality of the extracted features, making this aspect a focal point of attention. The objective of this work is therefore to deploy and observe potential feature extraction approaches used as input in training fully data-driven damage detection machine learning models. The most damage-sensitive segment (MDSS) feature extraction technique, which potentially treats signals under multiple conditions, is also proposed and deployed. It identifies potential segments for each feature coefficient under a defined criterion. Therefore, 680 signals, each consisting of 8192 data points, are recorded using accelerometer sensors at the Los Alamos National Laboratory in the USA. The data are obtained from a three-story 3D building frame and are utilized in this research for a mainly data-driven damage detection task. Three approaches are implemented to replace four missing signals with the generated ones. In this paper, multiple fast Fourier and wavelet-transformed features are employed to evaluate their performance. Most importantly, a power spectral density (PSD)-based feature extraction approach that considers the maximum variability criterion to identify the most sensitive segments is developed and implemented. The performance of the MDSS selection technique, proposed in this work, surpasses that of all 18 trained neural networks (NN) and recurrent neural network (RNN) models, achieving more than 80% prediction accuracy on an unseen prediction dataset. It also significantly reduces the feature dimension. Furthermore, a sensitivity analysis is conducted on signal segmentation, overlapping, the treatment of a training dataset imbalance, and principal component analysis (PCA) implementation across various combinations of features. Binary and multiclass classification models are employed to primarily detect and additionally locate and identify the severity class of the damage. The collaborative approach of feature extraction and machine learning models effectively addresses the impact of environmental and operational effects (EOFs), suppressing their influences on the damage detection process.

Simplicity and optimality are commonly associated with the particle swarm optimization (PSO) algorithm. As a result, numerous variants and hybrids of PSO have been developed and implemented to address structural optimization problems. The undeniable importance of the initialization technique in determining the overall performance of a given optimization algorithm cannot be overstated. Optimization algorithms, such as PSO, typically rely on a random, uniformly distributed initialization. Through multiple iterations and updates, these algorithms aim to achieve optimal results. The underlying assumption behind such an initialization approach is that a fair or reasonable arrangement of particles is best accomplished through randomization, and thus the entire optimization process is iterated based on this assumption. However, this initialization technique raises concerns regarding the attainment of optimality and convergence, leaving room for further examination. In this paper, we challenge this assumption by introducing a priority concept. The key idea is that particles should not be initialized randomly since randomness alone does not guarantee a reasonable allocation of design variable values in iterative optimization. This can lead to misguided velocity updates and ultimately, a time-consuming pursuit of optimality. To address this issue, we formulate priority criteria (PC) and propose an enhanced PSO variant called Priority Criteria PSO (PCPSO). The PC can be incorporated into any PSO variant or hybrid without impacting the parameter settings, constraints, and penalty approaches of the respective algorithms. A case study involving 2D reinforced concrete frames was conducted to compare the performance of the ordinary PSO algorithm with the PCPSO. The results clearly demonstrate that the introduction of the PC leads to a significant cost reduction when compared to PSO with an inertia damping factor. Additionally, the PCPSO algorithm exhibits accelerated convergence. Furthermore, to alleviate the computational burden associated with structural analysis at each iteration, a reanalysis approach called Combined Approximations (CA) is mathematically formulated and implemented.

As an optimization that starts from a randomly selected structure generally does not guarantee reasonable optimality, the use of a systemic approach, named the ground structure, is widely accepted in steel-made truss and frame structural design. However, in the case of reinforced concrete (RC) structural optimization, because of the orthogonal orientation of structural members, randomly chosen or architect-sketched framing is used. Such a one-time fixed layout trend, in addition to its lack of a systemic approach, does not necessarily guarantee optimality. In this study, an approach for generating a candidate ground structure to be used for cost or weight minimization of 3D RC building structures with included slabs is developed. A multiobjective function at the floor optimization stage and a single objective function at the frame optimization stage are considered. A particle swarm optimization (PSO) method is employed for selecting the optimal ground structure. This method enables generating a simple, yet potential, real-world representation of topologically preoptimized ground structure while both structural and main architectural requirements are considered. This is supported by a case study for different floor domain sizes.

Structural reanalysis involves repeated evaluation of structural responses under iterative design changes. It is a major computational burden in structural optimization, sensitivity analysis, and health monitoring. The three-layer architecture, which comprises the stiffness, displacement, and force layers, is motivated by the governing structural mechanics relationship $F=K·U$, which establishes stiffness and displacement as natural intermediate quantities for predicting internal forces. This physics-informed hierarchy reduces dependence on large training datasets while preserving predictive accuracy across all response quantities. The framework predicts member-level stiffness statistics, nodal displacements, and internal forces through three sequential layers: stiffness, displacement, and force. Each layer enriches the feature set of the layer above. Sensitivity-based secondary inputs are derived analytically from closed-form expressions relating cross-sectional dimensions to stiffness and displacement changes. This embeds structural mechanics knowledge directly into the feature engineering process without additional analyses. Member stiffness matrices are recovered as submatrices of the global stiffness matrix, encoding inter-member structural context into each member’s representation. The framework is implemented on a six-floor, three-bay reinforced concrete frame of 42 members. Training uses 1890 data points from 45 finite element iterations. The Random Forest model achieves R² scores of 0.99, 0.98, and 0.91 for axial force, shear force, and bending moment, respectively, on unseen validation data. Once trained on 45 FE iterations, the framework evaluates any number of candidate cross-sectional configurations in a single batch inference pass, enabling a shift from sequential solver-driven reanalysis to model-driven batch optimization. The proposed framework offers a scalable, interpretable, and physics-consistent alternative to both classical reanalysis methods and purely data-driven models, with direct applicability to structural size optimization and structural health monitoring workflows.

The topology optimization of reinforced concrete (RC) building frames is relatively underexplored compared to steel structures, partly due to the lack of a systematic approach to generate and select ground structures (GS). Existing methods often use less systematic GS strategies, limiting efficient exploration of the vast and sparse design space shaped by large bay widths and story heights. This work addresses this gap by providing a comprehensive and systematic pipeline tailored for RC frames. The key contributions are: (1) development of a GS generation framework that systematically enumerates all feasible RC frame configurations within user-defined constraints, (2) introduction of a candidate GS selection map, a surrogate-based tool employing graph-based Latin Hypercube Sampling (LHS) and sparse Gaussian Process (GP) models, which predicts compliance early and strategically guides candidate selection, significantly reducing computational cost while serving as a reference for understanding design parameter influences; and (3) implementation of an integrated topology optimization pipeline applying particle swarm optimization (PSO) to selected candidates, achieving efficient compliance minimization with reduced computational effort. The complete workflow - which spans GS generation, surrogate-based candidate selection, and iterative optimization - is implemented and validated in two design domains with width-to-height aspect ratios of 1:1 and 1:1.5 and generates 438,984 and 104,032 different frame configurations respectively. The selected candidates undergo PSO-based optimization, yielding designs with volume fractions below 0.55 and preserving critical floor beams, demonstrating the framework’s ability to enable the design of structurally efficient RC frames. The framework is designed to be extensible, with direct applicability to broader RC design scenarios including three-dimensional frames and nonlinear analysis in future work.

The topology optimization of reinforced-concrete (RC) building frames is relatively underexplored compared to steel structures, partly due to the lack of a systematic approach to generate and select ground structures (GS). Existing methods often use less systematic GS strategies, limiting efficient exploration of the vast and sparse design space shaped by large bay widths and story heights. This work addresses this gap by providing a comprehensive and systematic pipeline tailored for RC frames. The key contributions are: (1) development of a GS generation framework that systematically enumerates all feasible RC frame configurations within user-defined constraints, (2) introduction of a candidate GS selection map, a surrogate-based tool employing graph-based Latin hypercube sampling (LHS) and sparse Gaussian Process (GP) models, which predicts compliance early and strategically guides candidate selection, reducing computational cost by limiting full finite-element evaluations to the order of ${10}^3$ out of up to ${10}^5$ generated frames while serving as a reference for understanding design-parameter influences; and (3) implementation of an integrated topology-optimization pipeline applying particle swarm optimization (PSO) to selected candidates, achieving efficient compliance minimization with reduced computational effort. The complete workflow—which spans GS generation, surrogate-based candidate selection, and iterative optimization—is implemented and validated in two design domains with width-to-height aspect ratios of 1:1 and 1:1.5 and generates 438,984 and 104,032 different frame configurations, respectively. These selected candidates undergo PSO-based optimization, yielding designs with volume fractions below 0.55 and preserving critical floor beams, demonstrating the framework’s ability to identify structurally efficient stiffness-driven RC frame topologies for early-stage screening. The framework is designed as an extensible foundation that can be coupled with more detailed member-level design checks and nonlinear RC analysis in future work, rather than replacing full reinforced-concrete design procedures.