A comprehensive review of Bayesian statistics in natural hazards engineering

Zheng, Ye; Xie, Yazhou; Long, Xuejiao

doi:10.1007/s11069-021-04729-2

Cited by 14 publications

(6 citation statements)

References 135 publications

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“…Similarly, Bayesian techniques can quantify uncertainties of damage-sensitive features, for example, modal characteristics 224 and can merge multiple techniques in an ensemble learning form to reduce algorithm-induced false positives and negatives. 225 For more detail in Bayesian methods, especially considering natural hazards engineering, interested readers are, therefore, referred to the very recent review by Zheng et al 226 As opposed to the "black-box" nature of other ML algorithms, in contrast, since Bayesian methods assume the prior knowledge or assumption of a hypothesis, this enables for a more transparent statistical inference. Being that it represents a probabilistic distribution for both data and the model, various data types and parameters can be easily integrated for a robust and flexible classifier.…”

Section: Bayesian (Supervised)mentioning

confidence: 99%

Section: Bayesian (Supervised)mentioning

confidence: 99%

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Machine learning and structural health monitoring overview with emerging technology and high-dimensional data source highlights

Malekloo

Özer

AlHamaydeh

et al. 2021

Structural Health Monitoring

154

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Conventional damage detection techniques are gradually being replaced by state-of-the-art smart monitoring and decision-making solutions. Near real-time and online damage assessment in structural health monitoring (SHM) systems is a promising transition toward bridging the gaps between the past’s applicative inefficiencies and the emerging technologies of the future. In the age of the smart city, Internet of Things (IoT), and big data analytics, the complex nature of data-driven civil infrastructures monitoring frameworks has not been fully matured. Machine learning (ML) algorithms are thus providing the necessary tools to augment the capabilities of SHM systems and provide intelligent solutions for the challenges of the past. This article aims to clarify and review the ML frontiers involved in modern SHM systems. A detailed analysis of the ML pipelines is provided, and the in-demand methods and algorithms are summarized in augmentative tables and figures. Connecting the ubiquitous sensing and big data processing of critical information in infrastructures through the IoT paradigm is the future of SHM systems. In line with these digital advancements, considering the next-generation SHM and ML combinations, recent breakthroughs in (1) mobile device-assisted, (2) unmanned aerial vehicles, (3) virtual/augmented reality, and (4) digital twins are discussed at length. Finally, the current and future challenges and open research issues in SHM-ML conjunction are examined. The roadmap of utilizing emerging technologies within ML-engaged SHM is still in its infancy; thus, the article offers an outlook on the future of monitoring systems in assessing civil infrastructure integrity.

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Section: Bayesian (Supervised)mentioning

confidence: 99%

Section: Bayesian (Supervised)mentioning

confidence: 99%

Machine learning and structural health monitoring overview with emerging technology and high-dimensional data source highlights

Malekloo

Özer

AlHamaydeh

et al. 2021

Structural Health Monitoring

154

View full text Add to dashboard Cite

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“…Prior studies have made great progress on the concept of hazard. For example, a large body of literature took hazards as various negative social consequences caused by external emergencies where the hazard source is natural, objective, and external, and the "relational chain" between the hazard source and the social result is discontinuous, interrupted, and static [30][31][32][33]. From the sociological perspective, Kendra et al [34] defined a hazard as: "an event with time-space characteristics that causes threats and substantial losses to society or other branches of society, resulting in social structural disorder and interruption of the function of the basic survival support system of social members".…”

Section: Literature Reviewmentioning

confidence: 99%

Scenario-Based Comprehensive Assessment for Community Resilience Adapted to Fire Following an Earthquake, Implementing the Analytic Network Process and Preference Ranking Organization Method for Enriched Evaluation II Techniques

Chen

Yan

et al. 2021

Buildings

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Natural hazards bring significant influences on and socioeconomic loss to cities and communities. Historic events show that fire following earthquake (FFE) is the most influential uncertain disturbance on the urban infrastructure system. Under the FFE scenario, the concept of resilience is widely implemented to make up the shortcomings derived from the traditional disaster management methodology. Resilient cities and communities are required to improve the systemic performance in responding to the FFE. To fulfill these goals, measuring community resilience is an essential work for municipal policy makers. Therefore, this study conducted a comprehensive assessment on community resilience adapted to the FFE scenario. The systematic literature review (SLR) was employed to identify the indicators, and the analytic network process (ANP) technique was implemented to determine their weights. 20 indicators were extracted, and 4 communities that encountered FFE in China were selected for the empirical analysis. Thereafter, the preference ranking organization method for enriched evaluation (PROMETHEE) II technique was selected through using the multicriteria decision analysis (MCDA) methods selection framework to fulfill the comprehensive assessment. The results were discussed and demonstrated with graphical analysis for interactive aid (GAIA) technique. The findings revealed that the G Community won the highest score and had the strongest performance. However, H Community had the lowest score and the weakest performance. The proposed comprehensive methods could benefit the decision-makers and the policy executors achieving the community resilience adapted to the FFE scenario by improving the effective indicators.

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“…Over the last few years, artificial intelligence has gotten a lot of attention in the field of civil engineering. Regarding structural and earthquake engineering, research on machine learning can be divided into three areas including structural health monitoring [1,2], performance assessment of buildings [3][4][5][6][7], and prediction models of the mechanical behavior [8][9][10][11][12]. Estimating the response and predicting the behavior of structural members is one of the applications of machine learning techniques in civil engineering.…”

Section: Introductionmentioning

confidence: 99%

Efficiency of Hybrid Algorithms for Estimating the Shear Strength of Deep Reinforced Concrete Beams

Barkhordari

Feng

Tehranizadeh

2022

Period. Polytech. Civil Eng.

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Earthquakes occurred in recent years have highlighted the need to examine the strength of reinforced concrete (RC) members. RC beams are one of the elements of reinforced concrete structures. Due to the dramatic increase in the population and the number of medium/high-rise buildings, in recent years, the beams of buildings have been mainly designed and executed in the type of deep beams. In this study, the artificial neural network (ANN) with optimization algorithms, including particle swarm optimization (PSO), Archimedes optimization algorithm (AOA), and sparrow search algorithm (SSA), are used to determine the shear strength of reinforced concrete deep (RCD) beams. 271 samples from experimental tests are employed to develop algorithms. The results of this study, design codes equations, and previous research are compared. Comparison between the results shows that the PSO-ANN algorithm is more accurate than previous methods. Finally, SHApley Additive exPlanations (SHAP) method is utilized to explain the predictions. SHAP reveals that the beam span and the ratio of the beam span to beam depth have the highest impact in predicting shear strength.

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A comprehensive review of Bayesian statistics in natural hazards engineering

Cited by 14 publications

References 135 publications

Machine learning and structural health monitoring overview with emerging technology and high-dimensional data source highlights

Machine learning and structural health monitoring overview with emerging technology and high-dimensional data source highlights

Scenario-Based Comprehensive Assessment for Community Resilience Adapted to Fire Following an Earthquake, Implementing the Analytic Network Process and Preference Ranking Organization Method for Enriched Evaluation II Techniques

Efficiency of Hybrid Algorithms for Estimating the Shear Strength of Deep Reinforced Concrete Beams

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