Road safety assessment and risks prioritization using an integrated SWARA and MARCOS approach under spherical fuzzy environment

Ghoushchi, Saeid Jafarzadeh; Haghshenas, Sina Shaffiee; Ghiaci, Ali Memarpour; Guido, Giuseppe; Vitale, Alessandro

doi:10.1007/s00521-022-07929-4

Cited by 64 publications

(14 citation statements)

References 74 publications

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“…Step 1. A decision matrix is assigned to every expert where linguistic variable from reference [80] are employed to assess the importance of criteria. Let 𝐴 ̃𝑗𝑘 = (µ jk , v jk , π jk ) is the SFN for a criterion assessment 𝑗 by expert 𝑘.…”

Section: Methodology 31 Sf-swaramentioning

confidence: 99%

“…Once the significance of criteria is established, the same qualified experts construct the initial decision grid employing linguistic variables to ascertain the most suitable strategy through the SF-WASPAS approach based on these criteria. The initial phase encompasses the conversion of linguistic variables into spherical fuzzy numbers, following the scale outlined in [80]. Following this, expert opinions are consolidated using the SWAM operator to derive expert weights.…”

Section: Rank Of Strategiesmentioning

confidence: 99%

See 1 more Smart Citation

Integrated Spherical Decision-Making Model for Managing Climate Change Risks in Africa

Bouraima,

Ibrahim,

Qiu

et al. 2024

J. Soft. Comput. Decis. Anal.

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Decision-makers, researchers, practitioners, and stakeholders often struggle with selecting and prioritizing strategies to manage climate change risks. While recent research extensively explores this issue, the emphasis has largely been on regions other than Africa. This is significant, considering Africa’s anticipated exposure to various and severe impacts of climate change. This study applied a two-stage model that integrates the Step-Wise Weight Assessment Ratio Analysis (SWARA) and Weighted Aggregated Sum Product Assessment (WASPAS) methods within a unique framework under the influence of spherical fuzzy (SF) conditions. In the initial stage, SF-SWARA determines the relative importance of the criteria, while the subsequent stage involves the SF-WASPAS method to rank the strategies. While the most critical challenges are limited access to finance and inadequacies in climate data, scenarios, and impact models, the solution to be considered is the promotion of a well-coordinated capacity-building programme. Furthermore, a comprehensive sensitivity analysis was conducted to validate the applicability of the proposed model. This research not only identifies and explains the challenges associated with climate change risks management in the African context but also significantly contributes to the body of knowledge by outlining and prioritizing the strategies required to address these challenges.

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Section: Methodology 31 Sf-swaramentioning

confidence: 99%

Section: Rank Of Strategiesmentioning

confidence: 99%

Integrated Spherical Decision-Making Model for Managing Climate Change Risks in Africa

Bouraima,

Ibrahim,

Qiu

et al. 2024

J. Soft. Comput. Decis. Anal.

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show abstract

“…This is despite the fact that valuable research has been done based on the diagnosis and classification of different types of damage, particularly with the assistance of data-based SHM systems based on AI that have the ability to generalize to unpredicted conditions and different configurations. Pattern recognition is a well-known field that has been used in many machine learning methods [109][110][111][112][113][114][115][116][117][118]. For a SHM system's pattern recognition system to function successfully, it requires data from sensors with access to the physical environment.…”

Section: Shm Of Bridge and Aimentioning

confidence: 99%

The State of the Art of Artificial Intelligence Approaches and New Technologies in Structural Health Monitoring of Bridges

et al. 2022

Self Cite

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The challenges of urban administration are growing, as the population, automobiles, and cities rise. Making cities smarter is thus one of the most effective solutions to urban issues. A key feature of the “smart cities” of today is that they use cutting-edge technology in their infrastructure and services. With strategic planning, the smart city utilizes its resources in the most efficient manner. With reduced expenses and enhanced infrastructure, smart cities provide their residents with more and better services. One of these important urban services that can be very helpful in managing cities is structural health monitoring (SHM). By combining leading new technologies like the Internet of Things (IoT) with structural health monitoring, important urban infrastructure can last longer and work better. A thorough examination of recent advances in SHM for infrastructure is thus warranted. Bridges are one of the most important parts of a city’s infrastructure, and their building, development, and proper maintenance are some of the most important aspects of managing a city. The main goal of this study is to look at how artificial intelligence (AI) and some technologies, like drone technology and 3D printers, could be used to improve the current state of the art in SHM systems for bridges, including conceptual frameworks, benefits and problems, and existing methods. An outline of the role AI and other technologies will play in SHM systems of bridges in the future was provided in this study. Some novel technology-aided research opportunities are also highlighted, explained, and discussed.

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“…The feature extraction can be implemented by signal processing techniques to identify, locate and predict structural health in bridges [31][32][33][34]. On the other hand, several machine learning algorithms have been deployed in the process of pattern recognition [35][36][37][38][39][40][41][42][43][44]. The machine learning algorithms have been deployed in different stages of structures' life-time including design optimization, performance assessment, maintenance, SHM, damage detection and construction [45][46][47][48][49].…”

Section: Introductionmentioning

confidence: 99%

Algorithms and Techniques for the Structural Health Monitoring of Bridges: Systematic Literature Review

Sonbul

Rashid

2023

Sensors

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Structural health monitoring (SHM) systems are used to analyze the health of infrastructures such as bridges, using data from various types of sensors. While SHM systems consist of various stages, feature extraction and pattern recognition steps are the most important. Consequently, signal processing techniques in the feature extraction stage and machine learning algorithms in the pattern recognition stage play an effective role in analyzing the health of bridges. In other words, there exists a plethora of signal processing techniques and machine learning algorithms, and the selection of the appropriate technique/algorithm is guided by the limitations of each technique/algorithm. The selection also depends on the requirements of SHM in terms of damage identification level and operating conditions. This has provided the motivation to conduct a Systematic literature review (SLR) of feature extraction techniques and pattern recognition algorithms for the structural health monitoring of bridges. The existing literature reviews describe the current trends in the field with different focus aspects. However, a systematic literature review that presents an in-depth comparative study of different applications of machine learning algorithms in the field of SHM of bridges does not exist. Furthermore, there is a lack of analytical studies that investigate the SHM systems in terms of several design considerations including feature extraction techniques, analytical approaches (classification/ regression), operational functionality levels (diagnosis/prognosis) and system implementation techniques (data-driven/model-based). Consequently, this paper identifies 45 recent research practices (during 2016–2023), pertaining to feature extraction techniques and pattern recognition algorithms in SHM for bridges through an SLR process. First, the identified research studies are classified into three different categories: supervised learning algorithms, neural networks and a combination of both. Subsequently, an in-depth analysis of various machine learning algorithms is performed in each category. Moreover, the analysis of selected research studies (total = 45) in terms of feature extraction techniques is made, and 25 different techniques are identified. Furthermore, this article also explores other design considerations like analytical approaches in the pattern recognition process, operational functionality and system implementation. It is expected that the outcomes of this research may facilitate the researchers and practitioners of the domain during the selection of appropriate feature extraction techniques, machine learning algorithms and other design considerations according to the SHM system requirements.

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Road safety assessment and risks prioritization using an integrated SWARA and MARCOS approach under spherical fuzzy environment

Cited by 64 publications

References 74 publications

Integrated Spherical Decision-Making Model for Managing Climate Change Risks in Africa

Integrated Spherical Decision-Making Model for Managing Climate Change Risks in Africa

The State of the Art of Artificial Intelligence Approaches and New Technologies in Structural Health Monitoring of Bridges

Algorithms and Techniques for the Structural Health Monitoring of Bridges: Systematic Literature Review

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