Dynamic bayesian updating approach for predicting bridge condition based on Indonesia-bridge management system (I-BMS)

Soetjipto, Jojok Widodo; Adi, Tri Joko Wahyu; Anwar, Nadjadji

doi:10.1051/matecconf/201819502019

Cited by 3 publications

(4 citation statements)

References 8 publications

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“…The simulation is processed after the SPI prediction output data is retrieved from BN modeling and compared to the SPI schedule data. The validation model uses the match/nomatch approach [11]. The model compares the prediction results of the proposed model with the benchmark data (project SPI timeline) in terms of the classification "match" or "no match.…”

Section: Methodsmentioning

confidence: 99%

“…In this context, the Bayesian method can help plan and manage projects more effectively. Many studies have been conducted on Bayesian applications, including seismic hazard studies, soil corrosivity prediction, forensic assessment of bridge collapses, diagnostics of nuclear power plants, and others [11][12] [13]. To improve the accuracy of project time, performance predictions must involve factors that affect project performance.…”

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confidence: 99%

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Prediction Of Project Schedule Performance Index for Trans South Java Road Project Lot 7 Blitar Regency Using Bayesian Network

Wicaksono,

Soetjipto,

Halik

2023

U Karst

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The south coast of Indonesia has considerable potential and competitiveness in the field of tourism. This encourages the government to strive to improve tourism infrastructure through the construction of the South Coast Road. In its implementation, project reporting must be measured and controlled to assist project management in identifying problems and factors that affect project activities. This research aims to identify the most influential factors on Schedule Performance Index and develop a prediction model using the Bayesian Network. 5 main factors that affect project performance, such as Heavy Equipment, materials, Implementation and Work Relations, Labor, and Environment are used to are set 13 scenarios to detect the behavior of each factor appropriately. The factor is confirmed to the respondent to ensure that the factor occurs in the project. The study's results obtained Bayesian Network approach can be used to assess the condition of the JLS Lot 7 Blitar Schedule Performance Index (SPI) with an accuracy of around 80%. The dominant factor affecting SPI is the condition of the Heavy Equipment. The condition of the Heavy Equipment will affect the condition of SPI so closely that the contractor must maintain the performance of the heavy equipment so that the project performance is always in good condition. The identification results are expected to help in better decision-making and project risk management.

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Section: Methodsmentioning

confidence: 99%

mentioning

confidence: 99%

Prediction Of Project Schedule Performance Index for Trans South Java Road Project Lot 7 Blitar Regency Using Bayesian Network

Wicaksono,

Soetjipto,

Halik

2023

U Karst

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“…The chain disaster scheme can be seen in Fig 2. This disaster chain modeling can use several model options, namely bayesian or dynamic bayesian [21], markov-chain [22], artificial intelligence [23], System Dynamic [24], [25] etc.…”

Section: Framework For Model Of Regional Drrmentioning

confidence: 99%

A literature review: model of disaster risk reduction for decision support system

2021

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Disaster is a threat to human life. Many losses are caused by disasters, namely loss of life, injured people, loss of homes, and others. In addition, the frequency and intensity of disasters are also increasing every year. Therefore, research on Disaster Risk Reduction (DRR) is needed both to reduce disaster risk and to manage the disaster. The purpose of this research is to develop an appropriate DRR model in an area to assist decision-making in making policy. This research was compiled based on literature studies from various reputable journals to be used as a reference in the preparation of the right model. Then proceed with the development of a framework to model an efficient and effective DRR. The steps for making a holistic DRR model have been identified and the test design for the model has been determined, namely simulation, validation, and scenario. The recommendation given from this study is the preparation of a DSS (Decision Support System) as a tool for decision-makers to make policies regarding DRR-based regional development. This discussion will be continued in the next research by including case studies in certain urban areas.

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“…In recent years, it has been widely used in establishing bridge defect detection models [18], evaluating traffic and load on bridges [19], and optimizing bridge asset management systems [20]. For example, meta-heuristic algorithm [21], extreme gradient boosting methods [22], and dynamic Bayesian updating approaches [23] were used to establish several automatic bridge management sorting systems. The relevant parameters and design standards were input by users; the system then calculated the maintenance frequency and maintenance cost under the given constraints, such as location, bridge type, and design life [24,25].…”

Section: Introductionmentioning

confidence: 99%

A Machine Learning Framework for Predicting Bridge Defect Detection Cost

et al. 2021

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Evaluating the cost of detecting bridge defects is a difficult task, but one that is vital to the lifecycle cost analysis of bridges. In this study, a detection cost sample database was established based on practical engineering data, and a bridge defect detection cost prediction model and software were developed using machine learning. First, the random forest method was adopted to evaluate the importance of the seven main factors affecting the detection cost. The most important indicators were selected, and the recent GDP growth rate was employed to account for the impact of social and economic developments on the detection cost. Combining a genetic algorithm with a multilayer neural network, a detection cost prediction model was established. The predictions given by this model were found to have an average relative error of 3.41%. Finally, an intelligent prediction software for bridge defect detection costs was established, providing a reliable reference for bridge lifecycle cost analysis and the evaluation of defect detection costs during the operation period.

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Dynamic bayesian updating approach for predicting bridge condition based on Indonesia-bridge management system (I-BMS)

Cited by 3 publications

References 8 publications

Prediction Of Project Schedule Performance Index for Trans South Java Road Project Lot 7 Blitar Regency Using Bayesian Network

Prediction Of Project Schedule Performance Index for Trans South Java Road Project Lot 7 Blitar Regency Using Bayesian Network

A literature review: model of disaster risk reduction for decision support system

A Machine Learning Framework for Predicting Bridge Defect Detection Cost

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