Identification of Bridge Key Performance Indicators Using Survival Analysis for Future Network-Wide Structural Health Monitoring

Stevens, Nicola-Ann; Lydon, Myra; Marshall, A.H.; Taylor, Su

doi:10.3390/s20236894

Cited by 20 publications

(5 citation statements)

References 25 publications

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“…In addition, the estimated value of the partial regression coefficient is only related to the order of survival time and has nothing to do with the numerical value of survival time. e formula for the partial likelihood function [26] is given in the following equation:…”

Section: Regression Coefficient βmentioning

confidence: 99%

A Prognostic Model for Newly Operated Highway Bridges Based on Censored Data and Survival Analysis

Huang

Duan

et al. 2022

Advances in Civil Engineering

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The service performance of reinforced concrete bridges degrades overtime under environmental and vehicle loads. Accurate bridge deterioration analysis can provide a more scientific suggestion for the formulation of road bridge maintenance, strengthening, and reconstruction plans to ensure the operational safety of road bridges. Combined with bridge inspection data from the bridge database in Henan Province, we propose a prognostic model which is based on the Cox regression model for the service performance of newly operated highway girder bridges based on survival analysis theory. The Cox regression model can not only simultaneously analyze the effects of numerous factors on bridge survival, but also handle the presence of censored data in bridge survival data, which does not require the data to meet a specific distribution type. It shows that the decay rate of the deck system, superstructure, and substructure decreases with time in service, which is consistent with the actual decay pattern of the bridge structure. To further verify the accuracy of the model, the authors built a multilayer perceptron neural network with one hidden layer and used the cross-entropy error as the loss function. It showed that the importance of the deck system, superstructure, and substructure to the decay of the bridge structure gradually decreased. The model proposed in this paper is highly applicable and reliable. Theoretically, bridge decay prediction at regional and network-wide levels can be achieved if sufficient comprehensive bridge inspection data can be collected.

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Section: Regression Coefficient βmentioning

confidence: 99%

A Prognostic Model for Newly Operated Highway Bridges Based on Censored Data and Survival Analysis

Huang

Duan

et al. 2022

Advances in Civil Engineering

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“…The probabilistic method of bridge performance degradation prediction is mainly to simulate the degradation process of each component of the bridge over time by establishing different forms of probability density functions, such as establishing a continuous-time Markov process degradation model of concrete highway bridges [ 16 , 17 ], established a semi-Markovian process based on Weibull distribution to simulate the degradation process of urban bridges [ 18 ], established a concrete highway bridge deck deterioration model based on Bayesian survival theory to explore the impact Factors in Bridge Deck Performance [ 4 ]. Stevens et al [ 19 ] proposed a new application of survival analysis based on visual inspection of sparse data based on four types of data: bridge construction type, function, number of spans, and road class. However, because these data are too superficial, it is difficult to reflect the different degradation trends of various components of the bridge, and there is a large bias caused by subjective influence.…”

Section: Introductionmentioning

confidence: 99%

Smartphone-Based and Data-Driven Superstructure State Prediction Method for Highway Bridges in Service

Duan

Zhong

et al. 2022

Sensors

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Survival analysis is a data-driven approach that is widely used in various fields of biomedical prognostic research, and it is highly reliable in the processing of time-event data. This study developed a method for evaluating the service performance of bridge superstructures using the built-in acceleration sensor of smartphones and the prediction of survival analysis theory. It will be used to assist in the daily maintenance and repair of small and medium bridges. The effects of the upper load-bearing structure, upper general structure, bearings, deck paving, expansion joints, and frequency ratio on the deterioration of the bridge superstructure were investigated. The results show that the first-order vibration frequency of the bridge can be effectively detected by the built-in acceleration sensor of the mobile phone, but its low sensitivity and high output noise make it impossible to accurately detect the higher-order frequencies of the bridge. The upper load-bearing members, the upper general structure, the bearing, the deck pavement, and the frequency ratio are all related to the changing trend of the technical condition level of the bridge superstructure.

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“…The Markov model (MM) is identified as one of the approaches for predicting the failure rate of transformers. MM has been widely implemented in civil engineering to forecast the states of bridges, pavements, stormwater piping components and steel hydraulic structures (Riveros & Arredondo, 2010;Camahan et al, 1987;Micevski et al, 2002;Stevens et al, 2020). Additionally, it is also used in electrical equipment such as switchgear and transformers (Hamoud & Yiu, 2020;Hoskins et al, 1999).…”

Section: Introductionmentioning

confidence: 99%

Transformer Population Failure Rate State Distribution, Maintenance Cost and Preventive Frequency Study Based on Markov Model

Shariffuddin¹,

Azis²,

Jasni³

et al. 2022

JST

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This work investigates the state distributions of failure rate, performance curve, maintenance cost and preventive frequency of the transformer population through the Markov Model (MM). The condition parameters data of the oil samples known as Oil Quality Analysis (OQA), Dissolved Gas Analysis (DGA), Furanic Compounds Analysis (FCA) and age were analyzed from 370 distribution transformers. This work utilized the computed failure rate prediction model of the transformer population based on MM using the nonlinear minimization technique. First, the transition probabilities for each state were adjusted based on pre-determined maintenance repair rates of 10%, 20%, and 30%. Next, the failure rate state distributions and performance curves at various states were analyzed. Finally, the maintenance costs and preventive maintenance frequency were estimated utilizing the proposed maintenance policy models and the failure rate state probabilities. The result reveals that the transition from state 2 to state 1 with a 30% pre-determined maintenance repair rate can provide an average reduction of failure rate up to 11%. Based on the failure rate state probability, an average increment of maintenance cost from RM 18.32 million to RM 251.87 million will be incurred over 30 years. In total, 85% of the transformer population must undergo maintenance every nine months to avoid reaching very poor conditions.

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Identification of Bridge Key Performance Indicators Using Survival Analysis for Future Network-Wide Structural Health Monitoring

Cited by 20 publications

References 25 publications

A Prognostic Model for Newly Operated Highway Bridges Based on Censored Data and Survival Analysis

A Prognostic Model for Newly Operated Highway Bridges Based on Censored Data and Survival Analysis

Smartphone-Based and Data-Driven Superstructure State Prediction Method for Highway Bridges in Service

Transformer Population Failure Rate State Distribution, Maintenance Cost and Preventive Frequency Study Based on Markov Model

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