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Kahramanmaraş Earthquake Sequence of 6th of February is the deadliest earthquake that happened in Turkey in the era of instrumental seismology, claiming more than 55 thousand lives and leaving torn down cities and towns behind. More than 450 km long lateral strike-slip fault ruptured during these catastrophic earthquakes. As a result, more than 38 thousand buildings collapsed causing life losses. Considering that the large share of the Turkish building stock consists of RC buildings, the vulnerable RC building stock is the main responsible for this picture. Deficiencies of the Turkish RC building stock are well known since they manifested themselves several times in the past earthquakes. However, considering the improvements in the seismic codes and the seismic hazard maps achieved in the last two decades, the widespread collapse of buildings constructed after year 2000 was rather unexpected. Some of the observed structural damage patterns are similar to those observed also in the pre-2000 buildings in recent earthquakes, however, some other types of damages, such as out-of-plane bending and shear failures or shear-friction capacity failure of RC walls, brittle fracture and bond-slip failure of reinforcement, tension failure of beams and slabs are usually not witnessed. This paper presents a carefully selected set of examples comparing the pre-2000 and post-2000 building damages and collapses, also referring to a detailed summary and comparison of the code developments in Turkey.

Structural damage identification based on finite element (FE) model updating has been a research direction of increasing interest over the last decade in the mechanical, civil, aerospace, etc., engineering fields. Various studies have addressed direct, sensitivity-based, probabilistic, statistical, and iterative methods for updating FE models for structural damage identification. In contrast, evolutionary algorithms (EAs) are a type of modern method for FE model updating. Structural damage identification using FE model updating by evolutionary algorithms is an active research focus in progress but lacking a comprehensive survey. In this situation, this study aims to present a review of critical aspects of structural damage identification using evolutionary algorithm-based FE model updating. First, a theoretical background including the structural damage detection problem and the various types of FE model updating approaches is illustrated. Second, the various residuals between dynamic characteristics from FE model and the corresponding physical model, used for constructing the objective function for tracking damage, are summarized. Third, concerns regarding the selection of parameters for FE model updating are investigated. Fourth, the use of evolutionary algorithms to update FE models for damage detection is examined. Fifth, a case study comparing the applications of two single-objective EAs and one multi-objective EA for FE model updating-based damage detection is presented. Finally, possible research directions for utilizing evolutionary algorithm-based FE model updating to solve damage detection problems are recommended. This study should help researchers find crucial points for further exploring theories, methods, and technologies of evolutionary algorithm-based FE model updating for structural damage detection.

There have been major advances in the field of data science in the last few decades, and these have been utilized for different engineering disciplines and applications. Artificial intelligence (AI), machine learning (ML) and deep learning (DL) algorithms have been utilized for civil structural health Monitoring (SHM) especially for damage detection applications using sensor data. Although ML and DL methods show superior learning skills for complex data structures, they require plenty of data for training. However, in SHM, data collection from civil structures can be expensive and time taking; particularly getting useful data (damage associated data) can be challenging. The objective of this study is to address the data scarcity problem for damage detection applications. This paper employs 1-D Wasserstein Deep Convolutional Generative Adversarial Networks using Gradient Penalty (1-D WDCGAN-GP) for synthetic labelled acceleration data generation. Then, the generated data is augmented with varying ratios for the training data set of a 1-D deep convolutional neural network (1-D DCNN) for damage detection application. The damage detection results show that the 1-D WDCGAN-GP can be successfully utilized to tackle data scarcity in vibration-based damage detection applications of civil structures.

In this paper, for the first time, a variable velocity strategy particle swarm optimization (VVS-PSO) is presented to solve the optimization problems ranging from numerical functions to real-world problems. VVS-PSO introduces a new term added in the velocity updating process at each iteration. This new term is controlled by a reduction linear function, which allows VVS-PSO to reach a faster convergence rate. At the same time, it also leads to enhance the accuracy level. In this way, the strategy of position updating in VVS-PSO is more flexible than that of the original PSO. This strategy will support VVS-PSO to improve the distance between the current step and the previous step and to expand the feasible search space around each particle. To illustrate the convergence rate and level of accuracy of VVS-PSO, the original PSO and 4 well-known optimization algorithms are employed to solve 23 classical benchmark functions. Then, an engineering design problem and experimental validation using a four-storey steel frame are also presented to examine the reliability of VVS-PSO for solving particular real applications. VVS-PSO finally is applied to a real 3D reinforced concrete structure for the purpose of damage assessment. First, the modal assurance criterion (MAC) method, which considers the differences between the mode shapes, is combined with the Root-Mean-Square-Error (RMSE) that registers the differences between frequencies at two states, e.g., damaged and undamaged structures, to determine the objective function. Then, VVS-PSO is used to minimize the objective function, which accounts for variables related to stiffness reduction in elements. The presented results illustrate that VVS-PSO can solve the optimization and structural damage assessment problems with very high accuracy and reliability.

In past seismic events, earthquakes have often caused significant damage to buildings. It is noteworthy that most of the existing buildings are reinforced concrete structures. Therefore, in order to mitigate the damage caused by earthquakes, it is important to conduct damage assessment of reinforced concrete structures. Considering that damage at the material level is the fundamental cause of component and structural performance degradation, indices based on material damage often have advantages in reflecting and evaluating component and structural damage. This paper proposes a damage constitutive model for concrete based on existing research results. Then, aiming at the shortcomings of current research on steel bar damage constitutive models, a steel bar damage constitutive model under cyclic loading is proposed, reflecting various failure modes of steel bars under seismic actions. Based on this, a multi-level damage index system from materials to components to structures is established. Through multi-level experimental simulations and finite element analysis, the accuracy of the proposed damage indices is validated, and performance indices for components and structures are provided. These indices can effectively reflect the damaged state of components and entire structures and can be used to guide seismic design, damage assessment, and strengthening design.

Multi-objective optimization allows satisfying multiple decision criteria concurrently, and generally yields multiple solutions. It has the potential to be applied to structural damage identification applications which are oftentimes under-determined. How to achieve high-quality solutions in terms of accuracy, diversity, and completeness is a challenging research subject. The solution techniques and parametric selections are believed to be problem specific. In this research, we formulate a reinforcement learning hyper-heuristic scheme to work coherently with the single-point search algorithm MOSA/R (Multi-Objective Simulated Annealing Algorithm based on Re-seed). The four low-level heuristics proposed can meet various optimization requirements adaptively and autonomously using the domination amount, crowding distance, and hypervolume calculations. The new approach exhibits improved and more robust performance than AMOSA, NSGA-II, and MOEA/D when applied to benchmark test cases. It is then applied to an active damage interrogation scheme for structural damage identification where solution diversity/completeness and accuracy are critically important. Results show that this approach can successfully include the true damage scenario in the solution set identified. The outcome of this research can potentially be extended to a variety of applications.

Structural damage detection is of very importance to improve reliability and safety of civil structures. A novel sensor data-driven structural damage detection method is proposed in this paper by combining continuous wavelet transform (CWT) with deep convolutional neural network (DCNN). In this method, time-frequency images are obtained by CWT from original one-dimensional sensor signals. And, DCNN is designed to mine structural damage features from the time-frequency images and distinguish different structural damage condition. The proposed method is carried out on three-story building structure dataset and steel frame dataset. The experimental results show that the proposed method has the high accuracy and robustness of the damage detection compared with other existing machine learning methods.

Most data-driven structural damage detection methods are built upon the assumption that enough labeled data is available and both training and test data have the same underlying distribution, which limit their successful applications in practical engineering. To solve the problem, a novel structural damage detection method is proposed by using deep convolutional transfer learning. In the method, one-dimensional deep convolutional neural network and two-dimensional deep convolutional neural network are combined to mine more fine-grained features with spatiotemporal characteristic from raw vibration data. And, a novel domain adaptation technology, combining multikernel maximum mean discrepancy and local maximum mean discrepancy, is developed to align the distribution of global domains and relevant subdomains among different domains, which could mine more fine-grained features for each category and improve the transfer performance. Transfer experiments on two different structures are implemented to verify the effectiveness of the proposed method. Furthermore, a new solution is found by taking advantage of the damage knowledge learnt from other structure to implement damage detection when very small damage samples are available. The results show that the proposed method achieved superior detection performance over the existing popular methods.

Structural health monitoring (SHM) is the most direct and advanced method for understanding the evolution laws of structures and ensuring structural safety. The essence of SHM lies in diagnosing structural health by analyzing monitoring data. Since the introduction of machine learning paradigm for SHM, using machine learning methods to analyze the monitoring data, identify, and evaluate structural health status has become a prominent research topic in this field. For complex bridge structures, diagnosing structural health based on highly incomplete monitoring data presents an inherent high-dimensional problem. Machine learning methods are particularly well-suited for addressing these issues due to their capabilities in effective feature extraction, efficient optimization, and robust scalability. This article provides a brief review of the developments in machine learning-based structural health diagnosis, including data cleaning, structural modal parameters estimation, structural damage identification, digital twin technology, and structural reliability assessment. Additionally, the paper discusses related open questions and potential directions for future research.

The aim of this study is to develop a machine learning network to estimate the fundamental vibration period values of existing reinforced concrete (RC) buildings with damaged structural and non-structural elements. By considering the proposed machine learning network, changes in the fundamental vibration period of RC buildings due to potential damage states on structural members and infill walls are estimated. In this context, first of all, the level of reduction in stiffness caused by different damage levels in different types of structural elements is determined. Afterwards, an extensive database composed of 16,000 different building simulations with varying geometrical and mechanical properties is generated. 3D numerical models of these simulations are formed, and the fundamental vibration period values of the generated numerical models are determined. For each numerical model, a variant model at a certain damage state is also created by assigning predefined damage parameters to both structural and non-structural components. To this end, damage factor coefficients are used in stiffness matrices. An artificial neural network model is developed, and the created database is used in training and testing the artificial neural network model. The performance of the proposed artificial neural network (ANN) is determined using ambient vibration tests conducted on both undamaged buildings from the literature and damaged buildings during the Samos earthquake (2020) in the scope of this study. As a result, it has been shown that the proposed ANN is quite successful and can be used as an alternative method for determining the period values of undamaged—damaged RC buildings without the need to generate complex 3D numerical models.