1,721,107 research outputs found
Model updating of frame structure using equilibrium optimizer (EO) and cuckoo search (CS) algorithms
No full textA chimp optimization algorithm (ChOA) for vibration-based damage detection of a damaged steel truss
No full textApplication of slime mould optimization algorithm on structural damage identification of suspension footbridge
No full textStructural health monitoring (SHM) of civil engineering infrastructure, especially of bridges, has played a vital role in ensuring the safety as well as the integrity of the whole structure. SHM system enables inspectors to detect structural damage while it is still at its primal stage, thus reduces significantly the maintenance cost of the structure. Many researchers have focused on applying optimization techniques to improve the effectiveness of damage detection tool, especially those of natural-inspired algorithms. In this paper, we introduce an application of slime mould algorithm (SMA)—a novel metaheuristic optimization algorithm to solve the damage detection problem of a suspension footbridge. The highly accurate result shows that SMA is effective not only in detecting but also in localizing damages on the bridge
Advancements and emerging trends in integrating machine learning and deep learning for SHM in mechanical and civil engineering: a comprehensive review
No full textThe safety of structures heavily relies on the crucial role of structural health monitoring (SHM), reliability, and longevity of mechanical and civil infrastructure. Traditional methods of SHM often rely on manual inspection and monitoring techniques, which can be time-consuming, expensive, and prone to human error. In recent years, the integration of machine learning (ML) and deep learning (DL) techniques has shown great promise in revolutionizing SHM by enabling automated and accurate monitoring of structural conditions. This review paper provides a comprehensive analysis of the application of ML and DL algorithms, such as artificial neural networks (ANN), convolutional neural networks (CNN), and deep neural networks (DNN), in SHM. It explores the various approaches and methodologies employed in the field, including supervised, unsupervised, and reinforcement learning techniques. The paper discusses the advantages and limitations of ML and DL in SHM, highlighting their ability to handle large volumes of data, extract complex features, and provide real-time monitoring and predictive capabilities. Moreover, it addresses the challenges associated with implementing ML and DL in SHM, including data limitations, model complexity, interpretability, and the integration of domain knowledge. By reviewing a wide range of studies and applications, this paper aims to provide valuable insights into the current state-of-the-art, emerging trends, and future directions in ML and DL-based SHM
An efficient improved Gradient Boosting for strain prediction in Near-Surface Mounted fiber-reinforced polymer strengthened reinforced concrete beam
No full textThe Near-Surface Mounted (NSM) strengthening technique has emerged as a promising alternative to traditional strengthening methods in recent years. Over the past two decades, researchers have extensively studied its potential, advantages, and applications, as well as related parameters, aiming at optimization of construction systems. However, there is still a need to explore further, both from a static perspective, which involves accounting for the non-conservation of the contact section resulting from the bond-slip effect between fiber-reinforced polymer (FRP) rods and resin and is typically neglected by existing analytical models, as well as from a dynamic standpoint, which involves studying the trends of vibration frequencies to understand the effects of various forms of damage and the efficiency of reinforcement. To address this gap in knowledge, this research involves static and dynamic tests on simply supported reinforced concrete (RC) beams using rods of NSM carbon fiber reinforced polymer (CFRP) and glass fiber reinforced polymer (GFRP). The main objective is to examine the effects of various strengthening methods. This research conducts bending tests with loading cycles until failure, and it helps to define the behavior of beam specimens under various damage degrees, including concrete cracking. Dynamic analysis by free vibration testing enables tracking of the effectiveness of the reinforcement at various damage levels at each stage of the loading process. In addition, application of Particle Swarm Optimization (PSO) and Genetic Algorithm (GA) is proposed to optimize Gradient Boosting (GB) training performance for concrete strain prediction in NSM-FRP RC. The GB using Particle Swarm Optimization (GBPSO) and GB using Genetic Algorithm (GBGA) systems were trained using an experimental data set, where the input data was a static applied load and the output data was the consequent strain. Hybrid models of GBPSO and GBGA have been shown to provide highly accurate results for predicting strain. These models combine the strengths of both optimization techniques to create a powerful and efficient predictive tool
Damage Identification in Thin Steel Beams Containing a Horizontal Crack Using the Artificial Neural Networks
No full textThis investigation presents damage identification in thin steel beams containing a horizontal crack using artificial neural networks. In this way, finite element modeling of the cracked beam is developed to generate natural frequencies corresponding to various horizontal cracks scenarios. Then, the artificial neural network is used to create a predictor model for localizing horizontal cracks in steel beams. Results of the current paper show that The proposed technique is an effective method for detecting horizontal crack damage in steel beams. The regression index obtained in this study is equal to 0.979
Going Beyond Counting First Authors in Author Co-citation Analysis
Full text linkThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
Variations on the Author
Full text link“Variations on the Author” discusses two of Eduardo Coutinho’s recent films (Um Dia na Vida, from 2010, and Últimas Conversas, posthumously released in 2015) and their contribution to the general question of documentary authorship. The director’s filmography is characterized by a consistent yet self-effacing form of authorial self-inscription: Coutinho often features as an interviewer that rather than express opinions propels discourses; an interviewer that is good at listening. This mode of self-inscription characterizes him as an author who is not expressive but who is nonetheless markedly present on the screen. In Um Dia na Vida, however, Coutinho is completely absent form the image, while Últimas Conversas, on the contrary, includes a confessional prologue that moves the director from the margins to the center of his films. This article examines the ways in which these works stand out in the filmography of a director who offers new insights into the notion of cinematic authorship
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