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 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.

scholarly journals Intelligent Informatization Early Warning Analysis of Agricultural Economy Based on Support Vector Sequential Regression Model

2021 ◽  
Vol 2021 ◽  
pp. 1-9
Author(s):  
Ying Yang
Keyword(s):  
Agricultural Economic ◽  
Early Warning ◽  
Science And Technology ◽  
Internet Technology ◽  
Economic Research ◽  
Support Vector ◽  
Agricultural Economy ◽  
Early Warning Model ◽  
Warning Model ◽  
Sequential Regression

The development of science and technology has laid a solid foundation for the economic informatization of agriculture, and at the same time it brought technical guarantee for the development of agriculture, and the development of agriculture has provided an important material foundation for the development of science and technology. How to identify and deeply study agricultural economic informatization, give early warning to risk information, and ensure the steady development of the whole industry has become a key issue in the application of Internet technology in the field of agricultural development. This paper studies the present situation of agricultural economy informatization development process and applies support vector machine to forecast regional economic development level. The warning limit of agricultural economic growth rate is obtained on the basis of warning situation and warning indicator in early warning index system. The economic early warning model is established based on the support vector sequential regression method, and then the data is trained by MATLAB software to verify the rationality of the early warning model, and the accuracy and corresponding error of the model are given. Experimental results show that the prediction accuracy is 99.3%, the error is less than 0.05, and the prediction effect is relatively ideal, for agricultural economic intelligence information to provide accurate warning and agricultural economic research agricultural commercial development to provide support.

scholarly journals Forecast and Early Warning of Regional Bus Passenger Flow Based on Machine Learning

2020 ◽  
Vol 2020 ◽  
pp. 1-11
Author(s):  
Wusheng Liu ◽  
Qian Tan ◽  
Wei Wu
Keyword(s):  
Machine Learning ◽  
Early Warning ◽  
Integrated Circuit ◽  
Large Scale ◽  
Effective Control ◽  
Support Vector ◽  
Short Term ◽  
Early Warning Model ◽  
Passenger Flow ◽  
Warning Model

This paper mainly forecasts the short-term passenger flow of regional bus stations based on the integrated circuit (IC) card data of bus stations and puts forward an early warning model for regional bus passenger flow. Firstly, the bus stations were aggregated into virtual regional bus stations. Then, the short-term passenger flow of regional bus stations was predicted by the machine learning (ML) method of support vector machine (SVM). On this basis, the early warning model for regional bus passenger flow was developed through the capacity analysis of regional bus stations. The results show that the prediction accuracy of short-term passenger flow could be improved by replacing actual bus stations with virtual regional bus stations because the passenger flow of regional bus stations is more stable than that of a single bus station. The accurate prediction and early warning of regional bus passenger flow enable urban bus dispatchers to maintain effective control of urban public transport, especially during special and large-scale activities.

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