Bayesian-Based Hybrid Simulation Approach to Project Completion Forecasting for Underground Construction

Zhang, Sherong; Du, Chengbo; Sa, Wenqi; Wang, Chao; Wang, Gaohui

doi:10.1061/(asce)co.1943-7862.0000764

Cited by 22 publications

(7 citation statements)

References 16 publications

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“…It is widely applied in various subjects, such as diagnostics (Nikovski, 2000), ecology (McCann, Marcot, & Ellis, 2006), geography (Rivas, Matías, Taboada, & Argüelles, 2007), construction safety (Zhou, Fang, & Wang, 2008), green production (Zhu & Deshmukh, 2003), environment modeling (Aguilera, Fernández, Fernández, Rumí, & Salmerón, 2011), project risk management (Zhang, Du, Sa, Wang, & Wang, 2013), climate change (Richards et al, 2013), etc. BN is a kind of network structured directed acyclic graph, which expresses the risk factors and events with nodes in prior probability, and their relationships with directed edges in conditional probability.…”

Section: Features Of Bayesian Network Based Risk Assessment Methodsmentioning

confidence: 99%

A Bayesian Network-Based Risk Assessment Framework for the Impact of Climate Change on Infrastructure

Wang

2016

Construction Research Congress 2016

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In the last few decades, global warming and climate change have had great impact on infrastructures. Increasingly frequent extreme weather conditions, such as heat wave, severe cold, floods, and earthquakes, significantly change the construction and operation process of infrastructures. Huge social and economic lost raises the awareness to address and alleviate possible risks resulted from climate change. Proper assessment approach is needed to appropriately evaluate the added risks from climate change. This research discusses the features of climate change risk assessment, and Bayesian Networks method is proven to be an effective tool to construct the assessment model due to its features. This paper proposes to develop a Bayesian Networks-based risk assessment framework to evaluate the impact of climate change on the infrastructures. The risk assessment steps are presented using a case study of high speed railway operation.

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Section: Features Of Bayesian Network Based Risk Assessment Methodsmentioning

confidence: 99%

A Bayesian Network-Based Risk Assessment Framework for the Impact of Climate Change on Infrastructure

Wang

2016

Construction Research Congress 2016

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“…Chung, Mohamed, and Abourizk (2006) applied Bayesian updating technology to update the distribution of simulation parameters, which improved the simulation accuracy. Zhang, Du, Sa, C. Wang, and G. Wang (2014) adopted the Bayesian method to constantly update the duration distributions of unfinished project activities and simulated the completion probability in underground cavern group projects. Guan, Zhong, Ren, Song, and Chu (2018) applied the fuzzy set theory to describe simulation parameters updated by the Bayesian method.…”

Section: Study Bymentioning

confidence: 99%

Construction Phase Oriented Dynamic Simulation: Taking RCC Dam Placement Process as an Example

Zhong

et al. 2019

Journal of Civil Engineering and Management

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Construction simulation has been widely applied in schedule analysis. However, traditional simulation is based on static models built in the planning or design phase, which focuses on overall project-level schedule analysis. To provide activity-level simulation for on-site schedule management, a construction phase oriented dynamic simulation method is proposed, which takes roller compacted concrete (RCC) dam placement process as an example. Considering various innerlayer and inter-layer activities and different construction organization modes, a detailed placement process simulation model is built. Based on construction data collected by real-time monitoring, a construction activity modeling method is given. Additionally, Dirichlet process mixture (DPM) models are applied for simulation parameter updates, which endows density estimation with considerable flexibility and robustness. A fast inference algorithm is also proposed to realize the fast posterior computation of DPM models. The proposed method is tested by an RCC dam project in southwest China. The results show that the proposed method can reflect the dynamic features of the actual placement process in the construction phase and provide accurate schedule predictions for on-site construction management.

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“…Each of these articles, which are the results of research study, has investigated the topic of an increase in time and cost. Some of them have investigated this problem in a quantitative manner [1,[16][17][18][19][20][21][22][23] while some others have taken a qualitative approach [24][25][26][27][28][29][30][31].…”

Section: Literature Reviewmentioning

confidence: 99%

A Framework to Predict Time and Cost Risks based on Project Factors

Jalili

Skitmore

2018

Civ Eng J

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The occurrence and severity of risks are directly associated with the project factors, and in the case of determining a logical and significant correlation between project factors and risks, the final conditions of the project based on the initial and definite project factors can be predicted. In this research, a final list of 112 risks was prepared using the views of experts. Then, the risks’ weight was determined based on their impact on time and cost and according to the repetition of each risk in the earlier studies, as well as the use of 630industry experts using online web-based forms. Afterwards, 21 project factors that could affect the probability of occurrence and severity of risks related to time and cost were identified, and these factors were prioritized for each risk using the views of 25 experts. Then, applying the Pareto Principle, 14 project factors were identified. In all 4cases, including calculating the weight of risks in the technical literature as well as the experts’ views by time and cost, 14 selected factors were the same, but the weight and effects were different. The results indicate that considering these factors, the risks of time and cost increase from the initial estimate can be accurately predicted.

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Bayesian-Based Hybrid Simulation Approach to Project Completion Forecasting for Underground Construction

Cited by 22 publications

References 16 publications

A Bayesian Network-Based Risk Assessment Framework for the Impact of Climate Change on Infrastructure

A Bayesian Network-Based Risk Assessment Framework for the Impact of Climate Change on Infrastructure

Construction Phase Oriented Dynamic Simulation: Taking RCC Dam Placement Process as an Example

A Framework to Predict Time and Cost Risks based on Project Factors

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