Consequences of Heuristic Distortions on SHM-Based Decision

Verzobio, Andrea; Bolognani, Denise; Quigley, John; Zonta, Daniele

doi:10.1007/978-3-030-74258-4_8

Cited by 3 publications

(2 citation statements)

References 25 publications

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“…In this section, the concept of VoI for SHM-based decision problems is reviewed, following a similar path as in Zonta et al (2014) and Bolognani et al (2018). The reader can find recent examples of SHM-based decision problems in Flynn and Todd (2010a), Flynn and Todd (2010b), Flynn et al (2011, Bolognani et al (2017), Verzobio et al (2018), Verzobio et al (2019).…”

Section: Value Of Information For Shm-based Decisionmentioning

confidence: 99%

Quantifying the benefit of structural health monitoring: can the value of information be negative?

Verzobio

Bolognani

Quigley

et al. 2021

Structure and Infrastructure Engineering

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Section: Value Of Information For Shm-based Decisionmentioning

confidence: 99%

Quantifying the benefit of structural health monitoring: can the value of information be negative?

Verzobio

Bolognani

Quigley

et al. 2021

Structure and Infrastructure Engineering

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show abstract

“…In other words, the interpretation of data using visual inspections can differ from outcomes acquired from a rational process (Korteling et al, 2018). In psychology, this difference is called cognitive bias which directly affects SHM-based decision making (Berthet, 2021;Verzobio et al, 2021). Besides, emotional or neutral interpretations cause decision biases (Schoth and Liossi, 2017).…”

Section: Introductionmentioning

confidence: 99%

From Cognitive Bias Toward Advanced Computational Intelligence for Smart Infrastructure Monitoring

et al. 2022

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Visual inspections have been typically used in condition assessment of infrastructure. However, they are based on human judgment and their interpretation of data can differ from acquired results. In psychology, this difference is called cognitive bias which directly affects Structural Health Monitoring (SHM)-based decision making. Besides, the confusion between condition state and safety of a bridge is another example of cognitive bias in bridge monitoring. Therefore, integrated computer-based approaches as powerful tools can be significantly applied in SHM systems. This paper explores the relationship between the use of advanced computational intelligence and the development of SHM solutions through conducting an infrastructure monitoring methodology. Artificial Intelligence (AI)-based algorithms, i.e., Artificial Neural Network (ANN), hybrid ANN-based Imperial Competitive Algorithm, and hybrid ANN-based Genetic Algorithm, are developed for damage assessment using a lab-scale composite bridge deck structure. Based on the comparison of the results, the employed evolutionary algorithms could improve the prediction error of the pre-developed network by enhancing the learning procedure of the ANN.

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Designing a Structural Health Monitoring System Accounting for Temperature Compensation

et al. 2021

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Structural health monitoring is effective if it allows us to identify the condition state of a structure with an appropriate level of confidence. The estimation of the uncertainty of the condition state is relatively straightforward a posteriori, i.e., when monitoring data are available. However, monitoring observations are not available when designing a monitoring system; therefore, the expected uncertainty must be estimated beforehand. This paper proposes a framework to evaluate the effectiveness of a monitoring system accounting for temperature compensation. This method is applied to the design process of a structural health monitoring system for civil infrastructure. In particular, the focus is on the condition-state parameters representing the structural long-term response trend, e.g., due to creep and shrinkage effects, and the tension losses in prestressed concrete bridges. The result is a simple-to-use equation that estimates the expected uncertainty of a long-term response trend of temperature-compensated response measurements in the design phase. The equation shows that the condition-state uncertainty is affected by the measurement and model uncertainties, the start date and duration of the monitoring activity, and the sampling frequency. We validated our approach on a real-life case study: the Colle Isarco viaduct. We verified whether the pre-posterior estimation of expected uncertainty, performed with the experimented approach, is consistent with the real uncertainty estimated a posteriori based on the monitoring data.

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Consequences of Heuristic Distortions on SHM-Based Decision

Cited by 3 publications

References 25 publications

Quantifying the benefit of structural health monitoring: can the value of information be negative?

Quantifying the benefit of structural health monitoring: can the value of information be negative?

From Cognitive Bias Toward Advanced Computational Intelligence for Smart Infrastructure Monitoring

Designing a Structural Health Monitoring System Accounting for Temperature Compensation

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