Panam Zarfam

In this study, a data-efficient and interpretable framework is presented for anomaly detection in bridge structural health monitoring (SHM) systems. Addressing the limitations of data-hungry machine learning (ML) models, the proposed method enables near-real-time detection using efficient data. The framework extracts autoregressive coefficients from acceleration responses along with tactically employing the logarithm of the signal standard deviation as a complementary feature. It employs a weighted k-nearest neighbors classifier to rapidly and accurately detect anomalous behavior. Additionally, the method can localize faulty sensors, enhancing actionable insights for maintenance decisions. Its robustness was validated using acceleration data from two real-world cable-stayed bridges: Sutong Bridge (IPC-2020 dataset) and Yonghe Bridge (SMC dataset). While the latter is used for the first time as an anomaly detection benchmark, results from the former demonstrate high classification accuracy (up to 98.7%) and superior computational efficiency, outperforming previous studies by a factor of 4–8 in runtime, confirming the method’s effectiveness under varying operational conditions. Last but not least, the study demonstrates how fluctuations in ambient temperature, both increases and decreases, affect sensor outputs, leading to various anomalies in the data, an often-overlooked aspect in the SHM literature. By emphasizing diversity in training samples rather than relying solely on sheer quantity, the proposed methodology offers new insights into the applicability of ML in SHM, achieving high accuracy at a low cost.

Although many studies analyze data in structural health monitoring under noisy conditions, few focus on results in high noise levels, where detecting changes, whether clear damage or hidden deterioration, presents a significant challenge due to environmental noise. In this study, a methodology is presented that combines robust algorithms proven effective in noisy environments. This approach not only identifies changes but also localizes and assesses the severity of deterioration and damage while mitigating noise impact on the final results. The methodology employs autocorrelation as an alternative to time history responses, which serves inputs to a discrete wavelet transform. Several statistical indices are then embedded to create the features of the algorithm. Gray wolf optimization is applied as a novel feature selection method in damage detection problems, yielding outstanding results. Integrating these selected features and applying a moving maximum filter produces a superior pattern that significantly enhances the outcomes. Finally, a supervised Ensemble learner is used to classify the data. The effectiveness of the proposed method is demonstrated through three case studies: a benchmark study consisting of 17 diverse linear and nonlinear scenarios under forced vibrations, a numerical deterioration model with 5 different scenarios in response to ambient vibrations, and an experimental model with 10 predefined damage scenarios subjected to forced chirp signals. The results show high accuracy, exceeding 90% across all scenarios, including original data and data contaminated with Gaussian noise at signal to noise ratio levels of 10, 5, 1, and 0.5 decibels.

The present study evaluates primary-secondary structure interaction (PSSI) in three-dimensional (3D) single- and multi-story steel frame structures. A novel, yet practical, technique was presented to include the effects of secondary systems on the seismic response of coupled structures using concentrated mass systems. Also, a method was proposed to lower the time period of coupled structures via designing a deformable infill wall seating, contrary to the conventional wall-frame connections. The coupled structures were experimentally and numerically studied to examine the effect of secondary systems in the form of concentrated mass, infill walls, and the associated restraining wall posts. Thus, experimental models were exposed to selected ground motion excitations and the finite element (FE) models were subjected to non-linear dynamic time-history analyses. Five types of frame structures were designed, fabricated, and subjected to seismic loading via both approaches and the results were compared. An excellent agreement between the two approaches were observed. It was revealed that secondary systems noticeably increase story displacements and accelerations despite the very light weight, altering the seismic behavior of the entire system. However, free vibrations of the coupled frames were considerably eliminated when the proposed concentrated mass secondary systems were used in steel frames. Also, the input earthquake energy was immediately dissipated in structures with deformable wall seating.

This paper proposes an innovative method for determining the approximate incremental dynamic analysis curves of semi-isolated bridges, including the derivation of statistical mathematical formulas. Ultimately, an innovative method is devised based on the primary and limit state tangential stiffness values of semi-isolated bridges. The bridges selected for this study are two reinforced concrete highway bridges in the United States, which have been optimally redesigned using lead rubber bearing isolators. The established procedure can be used in future studies to extract the probabilistic responses of semiisolated bridges via a simplified and efficient method.

An adaptive fuzzy logic controller (FLC) is introduced to actuate MR damper smartly. Moreover, a novel evolutionary algorithm which was named “Dolphin echolocation” (DE) was exploited to optimize the number and arrangement of MR dampers and sensors in the sense of minimum structural responses and their cost. Furthermore, new generation structural systems should be designed based on seismic performance level. Designing based on performance requires dynamic and repetitive time history analyses. The endurance time analysis method (ETA) is a modern dynamic method based on the performance of the structure. In this research, to investigate the efficiency of Dolphin Echolocation-Fuzzy Logic Controller (DE-FLC) and ETA, several ET simulations were performed. The seismic responses of 11th-story benchmark building equipped with MR dampers were investigated in two case of seven time-history analysis and six generation of ET functions. By using Dolphin echolocation, the optimal arrangement and the number of dampers and sensors is determined. The proposed DE-FLC controller demonstrates its efficiency by reducing the excessive displacement under seismic excitations in comparison with uncontrolled case and classical FLC. Furthermore, the results demonstrate that sixth generation of ETA can simulate responses of several time-history analyzes well with proper accuracy, without necessitating any several computational burden.

The investigation of damage to buildings in terms of non-structural walls collapse in the past earthquakes have caused researchers to study the seismic behavior of walls more extensively. Furthermore, seismic design codes have considered using wall posts to prevent wall damage, however, not many studies were done on seismic behavior change ...

A lack of curvature reduces the out-of-plane stiffness of cable net façades. Therefore, they may experience large deformations and severe damage under heavy vertical loads such as earthquakes and wind. Axially movable smart supports are proposed for controlling structural vibrations by controlling tension of the cables. The equation of motion of the cable net, controlled by the smart support, is developed following a finite element method based program to solve the equation. The linear quadratic regulator control strategy is utilized to find the optimal control gain. The results show that smart supports, especially when connected to the central cables, can significantly control the vibrational amplitude of the structure.

The paper is regarding to the structural dynamics of a saddle shaped cable-net structure with two high points and two low points at the corners. The net cables are pre-stressed by stretching the four edge cables that are supported at the corner points. A series of parametric nonlinear dynamic analysis is utilized to evaluate the dynamic response of the system under the different structural masses and geometries, pretensions and alignments of cables and the amplitudes of dynamic excitation. An equivalent single-degree-of-freedom model is presented as a simplified method to estimate the dynamic response of the full finite element model. The simulation results show that the equivalent model can accurately estimate the displacement response of multi-degree-of-freedom model under dynamic excitation. Furthermore, a nonlinear active control algorithm is applied to decrease the structural response under the transient wind excitation and the results indicate the effectiveness of the proposed control algorithm.