Design method for polyurethane-modified asphalt by using Kriging-Particle Swarm Optimization algorithm

Lu, Pengzhen; Yang, Kai; Tian, Jie; Ma, Yiheng; Huang, Simin; Zhou, Chenhao

doi:10.1016/j.engappai.2022.105609

Cited by 7 publications

(2 citation statements)

References 37 publications

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“…Additionally, a variety of other methodologies are also available for OSP, such as the following: energy-efficient sensor deployment [ 265 ], backup sensor-based fault-tolerance SHM method [ 266 ], mixed variable pattern search algorithm [ 267 ], frequency domain-based OSP technique [ 268 ], three-phase sensor placement approach [ 269 ], Gram–Schmidt orthogonalization procedure [ 270 ], e-Estimator algorithms [ 271 ], and wave propagation-based local interaction simulation approach [ 272 ]. SHM problems can also be analyzed using other computational methodologies, including the guided water strider algorithm [ 273 ], grasshopper optimization algorithm (GOA) [ 274 ], improved imperialist competitive algorithm [ 275 ], atom search algorithm (ASO) [ 276 ], equilibrium optimizer algorithm [ 277 ], grey wolf optimizer algorithm [ 278 ], balancing composite motion optimization [ 279 ], Q-learning evolutionary algorithm [ 280 ], Kriging-particle swarm optimization algorithm [ 281 ], and topology optimization [ 282 ]. More details of these methodologies can be found in the associated studies.…”

Section: Optimization Algorithmsmentioning

confidence: 99%

A Systematic Review of Optimization Algorithms for Structural Health Monitoring and Optimal Sensor Placement

Hassani

Dackermann

2023

Sensors

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In recent decades, structural health monitoring (SHM) has gained increased importance for ensuring the sustainability and serviceability of large and complex structures. To design an SHM system that delivers optimal monitoring outcomes, engineers must make decisions on numerous system specifications, including the sensor types, numbers, and placements, as well as data transfer, storage, and data analysis techniques. Optimization algorithms are employed to optimize the system settings, such as the sensor configuration, that significantly impact the quality and information density of the captured data and, hence, the system performance. Optimal sensor placement (OSP) is defined as the placement of sensors that results in the least amount of monitoring cost while meeting predefined performance requirements. An optimization algorithm generally finds the “best available” values of an objective function, given a specific input (or domain). Various optimization algorithms, from random search to heuristic algorithms, have been developed by researchers for different SHM purposes, including OSP. This paper comprehensively reviews the most recent optimization algorithms for SHM and OSP. The article focuses on the following: (I) the definition of SHM and all its components, including sensor systems and damage detection methods, (II) the problem formulation of OSP and all current methods, (III) the introduction of optimization algorithms and their types, and (IV) how various existing optimization methodologies can be applied to SHM systems and OSP methods. Our comprehensive comparative review revealed that applying optimization algorithms in SHM systems, including their use for OSP, to derive an optimal solution, has become increasingly common and has resulted in the development of sophisticated methods tailored to SHM. This article also demonstrates that these sophisticated methods, using artificial intelligence (AI), are highly accurate and fast at solving complex problems.

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Section: Optimization Algorithmsmentioning

confidence: 99%

A Systematic Review of Optimization Algorithms for Structural Health Monitoring and Optimal Sensor Placement

Hassani

Dackermann

2023

Sensors

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“…For instance, the development of more accurate and efficient feature extraction techniques, as well as the integration of multiple data sources [28], could further enhance the performance and robustness of pavement distress identification models [29]. Additionally, developing real-time asphalt distress identification systems could help identify and address pavement distresses before they become severe, thereby reducing maintenance costs and enhancing road safety [30].…”

Section: Related Workmentioning

confidence: 99%

Automated Pavement Crack Detection Using Deep Feature Selection and Whale Optimization Algorithm

Alshawabkeh,

Wu,

Dong

et al. 2023

CMC

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Pavement crack detection plays a crucial role in ensuring road safety and reducing maintenance expenses. Recent advancements in deep learning (DL) techniques have shown promising results in detecting pavement cracks; however, the selection of relevant features for classification remains challenging. In this study, we propose a new approach for pavement crack detection that integrates deep learning for feature extraction, the whale optimization algorithm (WOA) for feature selection, and random forest (RF) for classification. The performance of the models was evaluated using accuracy, recall, precision, F1 score, and area under the receiver operating characteristic curve (AUC). Our findings reveal that Model 2, which incorporates RF into the ResNet-18 architecture, outperforms baseline Model 1 across all evaluation metrics. Nevertheless, our proposed model, which combines ResNet-18 with both WOA and RF, achieves significantly higher accuracy, recall, precision, and F1 score compared to the other two models. These results underscore the effectiveness of integrating RF and WOA into ResNet-18 for pavement crack detection applications. We applied the proposed approach to a dataset of pavement images, achieving an accuracy of 97.16% and an AUC of 0.984. Our results demonstrate that the proposed approach surpasses existing methods for pavement crack detection, offering a promising solution for the automatic identification of pavement cracks. By leveraging this approach, potential safety hazards can be identified more effectively, enabling timely repairs and maintenance measures. Lastly, the findings of this study also emphasize the potential of integrating RF and WOA with deep learning for pavement crack detection, providing road authorities with the necessary tools to make informed decisions regarding road infrastructure maintenance.

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Research on Morphological Characteristics and Performance Relationship of SBS-Modified Asphalt

Zhou,

2024

Int. J. Pavement Res. Technol.

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Design method for polyurethane-modified asphalt by using Kriging-Particle Swarm Optimization algorithm

Cited by 7 publications

References 37 publications

A Systematic Review of Optimization Algorithms for Structural Health Monitoring and Optimal Sensor Placement

A Systematic Review of Optimization Algorithms for Structural Health Monitoring and Optimal Sensor Placement

Automated Pavement Crack Detection Using Deep Feature Selection and Whale Optimization Algorithm

Research on Morphological Characteristics and Performance Relationship of SBS-Modified Asphalt

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