Risk assessment and management via multi-source information fusion for undersea tunnel construction

Zhou, Hong; Zhao, Yinghui; Shen, Qiang; Liu, Yang; Cai, Hubo

doi:10.1016/j.autcon.2019.103050

Cited by 111 publications

(60 citation statements)

References 33 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…ese factors are directly hit by the COVID-19 epidemic and the correspondent lockdown policy. e risk framework raised by Zhou et al [26] is adopted in this study to explore the specific risks brought about by the COVID-19 epidemic. In this framework, the risk factors include men, machines, materials, methods, and environment.…”

Section: Research Framework and Methodologymentioning

confidence: 99%

“…e risks identified above would inevitably influence the cost, schedule, and quality of a project [25]. Zhou et al [26] further extended risk loss into casualty, economic loss, time loss, and social influence. Eshtehardian and Khodaverdi [27] and Luu et al [28] argued that factors related to owner, design, and construction, as well as external environment, were linked to project schedule delay.…”

Section: Literature Reviewmentioning

confidence: 99%

“…However, researchers have developed different thoughts and opinions on risk structures in the construction industry. Yet, among different risk structures, factors such as men, machines, materials, methods, and internal and external environment were common and major causes of risk [26,34]. Construction risks could lead to remarkable influences on projects' cost, schedule, and quality [3,35].…”

Section: Literature Reviewmentioning

confidence: 99%

See 2 more Smart Citations

Risk Identification and Responses of Tunnel Construction Management during the COVID‐19 Pandemic

Zhi-min

Liu

2020

Advances in Civil Engineering

View full text Add to dashboard Cite

The COVID-19 pandemic has brought about significant influences to the world, including tunnel construction. Based on the analysis of 12 tunnel construction projects, this paper identifies the specific risk factors related to the COVID-19 pandemic, e.g., men, materials, machines, methods, social environment, and political epidemic prevention pressure. Among these risk factors, worker availability, site accessibility, shortage of construction materials, and inadequate epidemic prevention materials caused by the lockdown policy are the most fundamental challenges encountered by the projects. Social panic and epidemic prevention policy requirements are key issues needed to be addressed before the resumption of construction work. The special circumstances caused by the COVID-19 pandemic called for flexible project management and coordination skills to raise suitable and effective response strategies, while local governments make substantial contributions in solving the difficulties. Although these measures have resulted in higher project costs, their effectiveness in catching up with project schedules is worthy of recognition. The findings of this study enrich the risk categories of tunnel construction and the risk response strategies from the perspective of a global pandemic. It implies that future construction schemes including design, budget, supply chain, and project management should consider the possible influence of an epidemic.

show abstract

Section: Research Framework and Methodologymentioning

confidence: 99%

Section: Literature Reviewmentioning

confidence: 99%

Section: Literature Reviewmentioning

confidence: 99%

See 1 more Smart Citation

Risk Identification and Responses of Tunnel Construction Management during the COVID‐19 Pandemic

Zhi-min

Liu

2020

Advances in Civil Engineering

View full text Add to dashboard Cite

show abstract

“…The above research studied the causes of construction accidents, the development process of accidents, the methods to predict accidents and reduce them, etc. According to the characteristics of randomness, latency, independence and inheritance of construction accidents (Zhou et al, 2020;Li et al, 2010;Sanni-Anibire et al, 2020), this work considers the development and evolution of safety risk in the future as a random process and then study the process from risk to accident from the perspective of random process. Although great progress has been made in probabilistic risk assessment theory (Ang & Tang, 2007;Li et al, 1993;Zhou & Ding, 2017;Williamson & Winget, 2005) and they have been widely used in the prediction and assessment of construction accidents, the current used probability theory is based on stochastic variable (Kang & Ryu, 2019;Choe & Leite, 2020;Zhang et al, 2020b;Isaac & Edrei, 2016;Andolfo & Sadeghpour, 2015;Jamot & Park, 2019;Nnaji & Karakhan, 2020), while using stochastic process theory to analyze construction safety risk problem is very rare.…”

Section: Introductionmentioning

confidence: 99%

Analysis of Stochastic Process to Model Safety Risk in Construction Industry

Zhang

Yang

2021

Journal of Civil Engineering and Management

View full text Add to dashboard Cite

There are many factors leading to construction safety accident. The rule presented under the influence of these factors should be a statistical random rule. To reveal those random rules and study the probability prediction method of construction safety accident, according to stochastic process theory, general stochastic process, Markov process and normal process are respectively used to simulate the risk-accident process in this paper. First, in the general-random-process-based analysis the probability of accidents in a period of time is calculated. Then, the Markov property of the construction safety risk evolution process is illustrated, and the analytical expression of probability density function of first-passage time of Markov-based risk-accident process is derived to calculate the construction safety probability. In the normal-process-based analysis, the construction safety probability formulas in cases of stationary normal risk process and non-stationary normal risk process with zero mean value are derived respectively. Finally, the number of accidents that may occur on construction site in a period is studied macroscopically based on Poisson process, and the probability distribution of time interval between adjacent accidents and the time of the nth accident are calculated respectively. The results provide useful reference for the prediction and management of construction accidents.

show abstract

“…Additionally, the recipe and environmental variables may not be sufficient in covering all the possible factors influencing concrete CS. It has been proved in many areas that to comprehensively evaluate the quality of an engineering product, the influences from man, material, machine, method and environment (shortened to 4M1E), should be considered [ 27 , 28 , 29 , 30 , 31 ]. Each of these factors further represents the aggregation of various detailed influential factors.…”

Section: Introductionmentioning

confidence: 99%

Comprehensive Machine Learning-Based Model for Predicting Compressive Strength of Ready-Mix Concrete

Zhou

et al. 2021

Materials

View full text Add to dashboard Cite

Considering that compressive strength (CS) is an important mechanical property parameter in many design codes, in order to ensure structural safety, concrete CS needs to be tested before application. However, conducting CS tests with multiple influencing variables is costly and time-consuming. To address this issue, a machine learning-based modeling framework is put forward in this work to evaluate the concrete CS under complex conditions. The influential factors of this process are systematically categorized into five aspects: man, machine, material, method and environment (4M1E). A genetic algorithm (GA) was applied to identify the most important influential factors for CS modeling, after which, random forest (RF) was adopted as the modeling algorithm to predict the CS from the selected influential factors. The effectiveness of the proposed model was tested on a case study, and the high Pearson correlation coefficient (0.9821) and the low mean absolute percentage error and delta (0.0394 and 0.395, respectively) indicate that the proposed model can deliver accurate and reliable results.

show abstract

Risk assessment and management via multi-source information fusion for undersea tunnel construction

Cited by 111 publications

References 33 publications

Risk Identification and Responses of Tunnel Construction Management during the COVID‐19 Pandemic

Risk Identification and Responses of Tunnel Construction Management during the COVID‐19 Pandemic

Analysis of Stochastic Process to Model Safety Risk in Construction Industry

Comprehensive Machine Learning-Based Model for Predicting Compressive Strength of Ready-Mix Concrete

Contact Info

Product

Resources

About