scholarly journals Early Warning for the Construction Safety Risk of Bridge Projects Using a RS-SSA-LSSVM Model

2021 ◽  
Vol 2021 ◽  
pp. 1-14 ◽  
Author(s):  
Gang Li ◽  
Ruijiang Ran ◽  
Jun Fang ◽  
Hao Peng ◽  
Shengmin Wang
Keyword(s):  
Early Warning ◽  
Construction Safety ◽  
Bridge Engineering ◽  
Bridge Construction ◽  
Safety Risk ◽  
Early Warning Model ◽  
Safety Risks ◽  
Proposed Model ◽  
Warning Model ◽  
Early Warnings

Bridge engineering is an important component of the transportation system, and early warnings of construction safety risks are crucial for bridge engineering construction safety. To solve the challenges faced by early warnings risk and the low early warning accuracy in bridge construction safety, this study proposed a new early-warning model for bridge construction safety risk. The proposed model integrates a rough set (RS), the sparrow search algorithm (SSA), and the least squares support vector machine (LSSVM). In particular, the initial early warning factors of bridge construction safety risk from five factors (men, machines, methods, materials, and environment) were selected, and the RS was used to reduce the attributes of 20 initial early warning factors to obtain the optimized early warning factor set. This overcame the problem of multiple early warning factors and reduced the complexity of the subsequent prediction model. Then, the LSSVM with the strongest nonlinear modelling ability was selected to build the bridge construction early-warning model and adopted the SSA to optimize the LSSVM parameter combination, improving the early warning accuracy. The Longlingshan Project in Wuhan and the Shihe Bridge Project in Xinyang, China, were then selected as case studies for empirical research. Results demonstrated a significant improvement in the performance of the early-warning model following the removal of redundancy or interference factors via the RS. Compared with the standard LSSVM, Back Propagation Neural Network and other traditional early-warning models, the proposed model exhibited higher computational efficiency and a better early warning performance. The research presented in this article has important theoretical and practical significance for the improvement of the early warning management of bridge construction safety risks.

Construction Delay Analysis and early Warning Model Based on Design Structure Matrix

2013 ◽  
Vol 357-360 ◽  
pp. 2199-2206
Author(s):  
De Yin Liu ◽  
Jian Guo Chen
Keyword(s):  
Early Warning ◽  
Construction Projects ◽  
Design Structure Matrix ◽  
Delay Analysis ◽  
Model Case ◽  
Structure Matrix ◽  
Early Warning Model ◽  
Design Structure ◽  
Proposed Model ◽  
Warning Model

Schedule delays frequently occur in construction projects and bring unexpected consequence. Conducting an elaborate delay analysis and providing early warning are crucial for project performance. In this paper, factors are classified into internal risks and external risks based on the chain effect of schedule delays, and the design structure matrix (DSM) is employed to develop the delay analysis and early warning model. Case study has been performed to prove the effectiveness of the proposed model, finally, sensitivity analysis also draws conclusion the internal risks between tasks should be paid more attention than the external in environment.

scholarly journals Real-Time Warning Model of Highway Engineering Construction Safety Based on Internet of Things

2021 ◽  
Vol 2021 ◽  
pp. 1-10
Author(s):  
Haoran Song ◽  
Hao Yu ◽  
Dianliang Xiao ◽  
Yuexiang Li
Keyword(s):  
Internet Of Things ◽  
Real Time ◽  
Early Warning ◽  
Construction Safety ◽  
The Internet ◽  
Engineering Construction ◽  
Highway Engineering ◽  
Early Warning Model ◽  
Warning Model ◽  
The Internet Of Things

Real-time and effective early warning of highway engineering construction sites is the key to ensuring the safety of highway engineering construction. At present, highway engineering construction safety early warning is limited by the experience of relevant personnel at the site and the dynamic changes of the project site environment. Therefore, the creation of a more active, smarter, and more effective real-time early warning model for construction safety is a strong complement to current research and has important theoretical and practical implications. The Internet of Things is the third wave of the information industry after computers, the Internet, and mobile communication networks. It is of great significance to promote the development of science and technology, economic growth, and social progress. Aiming at the shortcomings of the inadequate safety management methods for highway engineering construction in China, the inefficient efficiency of safety production supervision and management, and the emphasis on single and sporty supervision methods, a real-time early warning model for highway engineering construction safety based on the Internet of Things technology was constructed. By quantifying, scoring, and statistics of the safety situation during the construction process, the model achieves the goals of real-time monitoring, early warning, and handling hidden safety hazards. It overcomes problems such as untimely and unscientific safety issues in the past and effectively improves China’s highway engineering construction. The experimental comparison between the real-time early warning model and the traditional early warning model in this paper shows that the accuracy of the early warning model proposed in this paper is improved by nearly 5%, and the false alarm rate is reduced by nearly 4%.

scholarly journals The Level of Public Acceptance to the Development of a Coastal Flooding Early Warning System in Jakarta

2021 ◽  
Vol 13 (2) ◽  
pp. 566
Author(s):  
Nelly Florida Riama ◽  
Riri Fitri Sari ◽  
Henita Rahmayanti ◽  
Widada Sulistya ◽  
Mohamad Husein Nurrahmat
Keyword(s):  
Path Analysis ◽  
Early Warning ◽  
Early Warning System ◽  
Warning System ◽  
Coastal Flooding ◽  
Coastal Areas ◽  
Public Acceptance ◽  
Community Attitudes ◽  
Early Warning Model ◽  
Warning Model

Coastal flooding is a natural disaster that often occurs in coastal areas. Jakarta is an example of a location that is highly vulnerable to coastal flooding. Coastal flooding can result in economic and human life losses. Thus, there is a need for a coastal flooding early warning system in vulnerable locations to reduce the threat to the community and strengthen its resilience to coastal flooding disasters. This study aimed to measure the level of public acceptance toward the development of a coastal flooding early warning system of people who live in a coastal region in Jakarta. This knowledge is essential to ensure that the early warning system can be implemented successfully. A survey was conducted by distributing questionnaires to people in the coastal areas of Jakarta. The questionnaire results were analyzed using cross-tabulation and path analysis based on the variables of knowledge, perceptions, and community attitudes towards the development of a coastal flooding early warning system. The survey result shows that the level of public acceptance is excellent, as proven by the average score of the respondents’ attitude by 4.15 in agreeing with the establishment of an early warning system to manage coastal flooding. Thus, path analysis shows that knowledge and perception have a weak relationship with community attitudes when responding to the coastal flooding early warning model. The results show that only 23% of the community’s responses toward the coastal flooding early warning model can be explained by the community’s knowledge and perceptions. This research is expected to be useful in implementing a coastal flooding early warning system by considering the level of public acceptance.

scholarly journals Safety Warning Model of Coal Face Based on FCM Fuzzy Clustering and GA-BP Neural Network

Symmetry ◽  
2021 ◽  
Vol 13 (6) ◽  
pp. 1082
Author(s):  
Fanqiang Meng
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Early Warning ◽  
Back Propagation ◽  
Back Propagation Neural Network ◽  
Coal Face ◽  
Early Warning Model ◽  
Working Face ◽  
The Neural Network ◽  
Warning Model

Risk and security are two symmetric descriptions of the uncertainty of the same system. If the risk early warning is carried out in time, the security capability of the system can be improved. A safety early warning model based on fuzzy c-means clustering (FCM) and back-propagation neural network was established, and a genetic algorithm was introduced to optimize the connection weight and other properties of the neural network, so as to construct the safety early warning system of coal mining face. The system was applied in a coal face in Shandong, China, with 46 groups of data as samples. Firstly, the original data were clustered by FCM, the input space was fuzzy divided, and the samples were clustered into three categories. Then, the clustered data was used as the input of the neural network for training and prediction. The back-propagation neural network and genetic algorithm optimization neural network were trained and verified many times. The results show that the early warning model can realize the prediction and early warning of the safety condition of the working face, and the performance of the neural network model optimized by genetic algorithm is better than the traditional back-propagation artificial neural network model, with higher prediction accuracy and convergence speed. The established early warning model and method can provide reference and basis for the prediction, early warning and risk management of coal mine production safety, so as to discover the hidden danger of working face accident as soon as possible, eliminate the hidden danger in time and reduce the accident probability to the maximum extent.

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