Building engineering safety risk assessment and early warning mechanism construction based on distributed machine learning algorithm

Liu, Hongmei; Tian, Guiliang

doi:10.1016/j.ssci.2019.08.022

Cited by 25 publications

(19 citation statements)

References 19 publications

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“…China's local government debt management level, growth rate, and policy structure are the main factors that affect the level of debt risk. Government debt risks are divided into two categories, including currency depreciation risk and government debt default risk [1], and both of these two types of risks are attributed to the expansion of government debt scale. At the same time, the government debt default risk will also cause losses to the government's credit.…”

Section: Introductionmentioning

confidence: 99%

Risk Assessment of Government Debt Based on Machine Learning Algorithm

Chen

2021

Complexity

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Government debt risk is an important factor affecting macroeconomic stability and public expectation. The key to its prevention and control lies in early warning and early prevention. This paper builds an effective government debt risk assessment system based on machine learning algorithm. According to forming the performance of local government debt risk and its internal and external influencing factors, this study applies the analytic hierarchy process, entropy method, and BP neural network method to construct the local government risk assessment index system, which includes the primary and secondary indexes including the explicit debt risk, the contingent implicit debt risk, and the financial and economic operation risk. Using this system, this study carries on the government debt risk comprehensive weight assignment, the fiscal revenue forecast, the default probability calculation, the safety scale forecast, and finally the government debt risk assessment of the validity analysis. The system can provide signal guidance and policy reference for finance to cope with risks in advance, arrange the priority order of debt repayment, optimize the structure of fiscal revenue and expenditure, etc.

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Section: Introductionmentioning

confidence: 99%

Risk Assessment of Government Debt Based on Machine Learning Algorithm

Chen

2021

Complexity

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“…Machine learning may assist project stakeholders to decrease risk by identifying risks, measuring their impact, and using predictive analytics. Therefore, many studies were undertaken to apply machine learning to predict and assess risks of construction projects [8][9][10][11][12][13][14][15][16][17][18]. For example, the study of [13] used machine learning to address the soft-margin support vector machines for supervised machine learning classification using n-fold cross-validation.…”

Section: A Assessing and Reducing Construction Project Risksmentioning

confidence: 99%

“…In addition, the study of [14] used distributed computing and an expanded cloud model to assess not only the overall state of construction safety but also potential security issues based on information feedback. It is possible to understand the objective existence of hazards in the construction process, which has significant guiding relevance for the construction.…”

Section: A Assessing and Reducing Construction Project Risksmentioning

confidence: 99%

“…Hence, construction safety is a top priority on all job sites, and machine learning offers a high-tech solution to this problem. That is why applying machine learning in construction safety has been attracted numerous researches to date [8,14,[19][20][21][22][23][24][25][26][27][28]. In the study [21], neural network and decision tree analyses were implemented to assess the unsafe act of not anchoring harnesses while working on a scaffold of 40 migrant workers, whereas, with an accident data from the Singapore construction industry, a neural network analysis was performed on a quantitative occupational safety and health management system audit [22].…”

Section: B Safety Management For Construction Sitesmentioning

confidence: 99%

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Research Trends on Machine Learning in Construction Management: A Scientometric Analysis

Van

Quoc

2021

JASTT

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Machine learning plays a vital role in construction industry which could make improve project’s safety, productivity, and quality. Many studies have attempted to explore the potential opportunities to adopt this technology in different aspects of the construction sector. However, no comprehensive study to review the global research trends on this technological advancement in construction management domain. The goal is to investigate and summarize the state-of-the-art knowledge body in this topic in a systematic manner. To achieve this, this paper considered 161 studies on machine learning in construction management related to bibliographic records retrieved from the Scopus database by adopting scientometric analysis approach. This paper found that since 2014, there has been a considerable increase in the number of publications on this domain. Researchers from the United States, China, and Australia have been the main contributors to this research area through regional analysis. This study also revealed that approximately 34% of all countries in the world are engaged in this domain research. In addition, five main aspects in construction management have been applied machine learning techniques, namely, assess and reduce risk, safety management for construction sites, cost estimation and prediction, Schedule management, and building energy demand prediction. Furthermore, three potential construction management research areas that can apply this technology were proposed for further studies. The findings will help both professionals and researchers more understanding how machine learning knowledge is evolving and its role played in the construction management domain, and this study thus offers a useful reference point to how can develop this area in the future.

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“…e potential collisions in the operating environment depend on the location and movement information of the tower crane signalman macroscopic observations [18]. However, human observational error is one of the causes of tower crane safety accidents [19,20]. Chen and Luo [21] considered the accuracy features of the localization system and the constraint for decision making and then proposed the precision and recall evaluation model of position-related safety status decision making.…”

Section: Background and Related Workmentioning

confidence: 99%

Exploring the Impact of Wind Loads on Tower Crane Operation

Jin

Liu

Zheng

et al. 2020

Mathematical Problems in Engineering

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The construction industry continues to be one of the industries with the highest accident rate. Tower cranes have always been the central component of many construction operations. With recent advancements in tower cranes, more efforts have been made for their safe operation. The impact of natural environmental factors on the operation of tower cranes cannot be ignored, especially the impact of the wind load, which can be found in construction regulations worldwide. Therefore, a large number of recent studies have focused on exploring different approaches to improving the impact of loads, such as improved feedback controllers and collocated sensors. However, there are few studies on the impact of the flexible cable pendulum system under wind load. To quantify the impact in this paper, first, the swing model of the hanging and the model of sling fluctuation are proposed as the base. Second, the flexible cable pendulum model is designed for the impact of tower cranes under wind load. By field experiments, the applicability of the model in practical use is analyzed. A case of the Crane Beach Hydropower Station is investigated as a demonstration. With MATLAB programming, the variation in the hanging impulse with wind speed is shown, indicating that the maximum wind speed should be 12 m/s if the factors that people can control are considered. When considering the factors that can be harmful to humans, the maximum wind speed is 17 m/s. With the proposed model, the impacts of wind load on tower crane operation can be quantified without actual deployment, offering a more straightforward quantitative tool for safe regulations and engineering management.

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Building engineering safety risk assessment and early warning mechanism construction based on distributed machine learning algorithm

Cited by 25 publications

References 19 publications

Risk Assessment of Government Debt Based on Machine Learning Algorithm

Risk Assessment of Government Debt Based on Machine Learning Algorithm

Research Trends on Machine Learning in Construction Management: A Scientometric Analysis

Exploring the Impact of Wind Loads on Tower Crane Operation

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