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

Abstract

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%.

This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

1. Introduction

With the rapid development of China’s transportation construction, highway engineering construction projects are increasing day by day. At the same time, the situation of safety production in engineering construction is also very severe. Because highway engineering shows multiple points and long lines, many links, many parties, and long cycles, it has many cross-operations, many mechanical equipment, complex environments, large mobility of personnel, and many potential accidents. So no matter which part of the link is wrong, it may lead to a safety accident. Especially in recent years, highway engineering construction has gradually shifted to remote areas such as hills and mountains. The difficulty of construction has increased, hidden dangers have become increasingly prominent, and production safety tasks have become more onerous.

Internet of Things technology and building construction information technology have been used in civil engineering construction, such as radio frequency identification RFID technology, global positioning system (GPS), wireless sensor network, Zigbee, laser scanning technology, and handheld computer (PDA). At present, these frontier technologies are relatively lacking in theoretical research and engineering applications in the field of highway engineering construction safety management. The severe situation of highway engineering safety production in China should strengthen the research on the supervision model and countermeasures. Because illegal command, illegal operations, and labor discipline violations are the main factors that cause accidents, and the current supervision of construction safety is mostly limited to self-inspection, self-correction, and unannounced safety inspections, the “gust” supervision model lacks substantial management. Inadequate early warning processing resulted in the loss of a large amount of resources. Therefore, how to achieve “anytime, anywhere” safety supervision of the construction site with reasonable investment of funds and personnel and truly prevent the accidents in the construction of the project is an urgent problem.

In recent years, domestic and foreign researches on the application of the Internet of Things in accident early warning have been carried out. Security risk management has multiple characteristics and multiple targets, which has become a new field and an important development direction for the application of the Internet of Things. Cheng et al. [1] proposed a tunnel construction management monitoring system based on the Internet of Things. The system consists of a remote central control center, a main control unit, multiple data acquisition units, and a ZigBee terminal node. After the system is successfully initialized, the main control unit and data acquisition unit are connected to the remote central control center through the wireless network through manual or automatic mode and can also send control and query to obtain various sensor information commands of the monitoring system. The system mainly solves the problems of unstable electrical connection caused by wired communication, difficulty in wireless network connection, and high cost. Yanga et al. [2] analyzed the economic indicators of processing centers for urban road traffic problems, optimized dynamic traffic network allocation based on a continuous large-scale IoT input database, and proposed a high-performance computing model for dynamic traffic planning. This model is based on the performance of ultrahigh-performance computing. It is of great significance for real-time massive data and low-cost traffic network optimization planning and to promote the construction and development of smart cities. Luo et al. [3] established an emergency warning system for chemical parks based on the Internet in order to respond to environmental pollution accidents in chemical parks in a timely and effective manner. The operation of this system can effectively monitor the environmental quality of the entire park and its surroundings, prevent and timely control various environmental pollution accidents, and provide information support for park management. Jun et al. [4] designed monitoring and early warning and safety emergency systems for petrochemical enterprises due to incomplete monitoring, delay in accident warning, low emergency rescue efficiency, and incompatibility between monitoring systems and data sources. With this system, equipment can be monitored in real time. Equipment hazards and risks can also be analyzed automatically. This system helps petrochemical companies to achieve timely accident early warning and intelligent decision-making and scheduling of emergency rescue. In addition, it also solves the problem of incompatibility between different monitoring systems and data sources and improves the automation and intelligence level of safety production management and emergency command and dispatch of petrochemical enterprises.

This article uses advanced Internet of Things, information technology, and other technologies to warn you in a timely manner, find and eliminate hidden safety hazards and highways based on traditional early warning methods. Learn how to build a real-time early warning model for construction. Relying on the construction of highway engineering, through a comprehensive analysis of dangerous and harmful factors in the construction process of highway engineering, safety technical measures for construction preparation stage, roadbed engineering, and pavement engineering have been summarized. We have built a set of safety management and risk management systems for highway construction based on the Internet of Things technology. It is divided into three steps: analyzing early warning monitoring information, determining early warning levels, and accident response. With this system, traffic safety accidents can be realized. Early prevention and management, and for the first time, crisis management measures can be initiated. Highway Engineering Construction Safety Accident Early Warning Mechanisms can monitor the diagnosis of safety accidents, proactively control their occurrence, and replace accident handling with accident prevention. You can realize active safety management and improve the quality and efficiency of safety management work of highway engineering work.

2. Proposed Method

2.1. Highway Engineering and Its Safety Management Theory

2.1.1. Highway Engineering

Highway engineering [5–7] includes subgrade engineering, pavement engineering, bridge engineering, culvert engineering, tunnel engineering, drainage system engineering, safety structures, traffic safety engineering facilities, housing construction, greening, and mechanical and electrical engineering. The highway engineering safety management system is a relatively complex system consisting of several elements, each of which constitutes a closed-loop operating body. Any one element must maintain its own benign condition in order for the system to ensure a benign operation. However, this system is not static. With the continuous innovation and application of new technologies, new processes, and new management modes, the elements of the system will be continuously enriched, and the closed cycle body will continue to expand its connotation.

2.1.2. Safety Management Theory of Highway Engineering

The principles of highway engineering safety management mainly include safety system theory, safety cybernetics, safety information theory, accident prediction and prevention principles, and accident mutation theory. This article mainly introduces theories about accident prediction and prevention.

(1) Principle of Accident Prediction and Prevention. Accidents have the characteristics of causality, contingency, inevitability, and reproducibility. Accidents are a random phenomenon. The investigation of individual cases is uncertain, but for most accidents, it shows a certain law. According to the timeliness of prevention, the accident prevention model can be divided into two types: after-the-fact and predictive. The postevent model is to put rectification measures after an accident or disaster, mainly to avoid similar accidents; the forecast model is a proactive and proactive risk response strategy adopted before an accident or disaster. Including the five steps of formulating safety production goals, predicting safety production issues, finding key issues, preparing safety production plans, implementing safety production plans, evaluating safety production processes, and establishing new safety production goals.

(2) Accident Mutation Theory. Sudden change is a sudden change in the process of continuous development and the relationship between sudden changes and continuous changes. The occurrence of an accident can be understood as the sudden change of the system from a safe state to an accident state due to the qualitative change of the system caused by the continuous change of some parameters in the system. The human factor mainly refers to the safety education, management ability, physical fitness, resilience, and safety awareness of construction workers. The main factors include machine failure, construction conditions, safety protection devices, and the degree of mechanization in the construction process.

(3) Theory of Cause of Accident. The progress of science and technology has made people’s understanding of accidents more and more profound, and they have a clearer understanding of the accidental and inevitability of accidents, especially the necessity of similar accidents. Therefore, we began to actively look for the law of incidents, analyze the characteristics of accidents, explore ways to find and eliminate accidents, and minimize the possibility of accidents as much as possible. The cause theory is to study and analyze a large number of accidents, make exhaustive discussions on the relationship between people, things, and the environment, and propose theories, methods, and strategies for preventing accidents. From domestic and foreign perspectives, experts and scholars have studied numerous accident occurrence rules and related theories are not exhaustive. The most representative of them are domino theory and comprehensive factor theory.

2.2. Internet of Things

2.2.1. Technical Framework of the Internet of Things

The general definition of the Internet of Things [8–10] is to use RFID, infrared sensors, GPS, laser scanners, and other information sensing devices to connect any item with the Internet according to agreed protocols for information exchange and communication to achieve a network of intelligent identification, positioning, tracking, monitoring, and management. Its technology system includes perception layer technology, network layer technology, application layer technology, and public technology. Figure 1 shows the technical architecture of the Internet of Things.

(1) Perception layer: data collection and perception are mainly used to collect physical events and data that occur in the physical world, including various physical quantities, identity, location information, audio, and video data. Data acquisition for the Internet of Things involves technologies such as sensors, RFID, multimedia information acquisition, two-dimensional code, and real-time positioning.

(2) Network layer: the network layer completes a wide range of information communication. It mainly uses existing WAN communication systems (such as PSTN networks, 2G/3G mobile networks, and the Internet) to quickly, reliably secure the information perceived by the perception layer. The ground is transmitted to various places on the earth, enabling the items to communicate over a long distance and a wide range.

(3) Application layer: the application layer completes the final interaction between the item and the person. The first two layers collect the information of the item on a large scale and summarize it in the application layer for unified analysis and decision-making. It is used to support cross-industry, cross-application, and cross-system. The synergy, sharing, and intercommunication of information between them will improve the comprehensive utilization of information and serve humanity to the greatest extent.

(4) Public technology layer: the public technology layer does not belong to a specific level of the Internet of Things technology but has a relationship with the three layers of the Internet of things technology architecture. It includes identification and analysis, security technology, network management, and quality of service (QOS) management.

[figure omitted; refer to PDF]

When the management instructions of the road construction enterprise safety production early warning management organization were issued, the construction enterprise safety production early warning management system began to operate. For highway construction enterprises, the safety production early warning system should operate as follows:

(1) The early warning agency monitors, diagnoses, and evaluates the safety production system and production process to identify the symptoms of accidents, to ensure that production is kept away from alert or crisis conditions, and to provide a reference for further prevention and control.

(2) If it is found that the enterprise has entered a crisis state and the enterprise has entered a safety management emergency procedure as a whole, an emergency management team must be established, and the early warning and supervision agencies shall jointly formulate emergency countermeasures and emergency plans, with responsibility to the people and scientific advancement. At this time, the functional safety management work is all under the responsibility of the emergency leadership team, and the enterprise’s production safety management activities are also under the full responsibility of the emergency leadership team until the crisis is eliminated and safety production is fully restored.

(3) The early warning agency should also check the safety early warning work at all levels and have the responsibility for organizing, coordinating, and directing the work safety early warning and prevention work of the entire highway construction enterprise. For highway companies, the implementation of safety production early warning management is conducive to the establishment of a countermeasure database and can provide countermeasures for highway construction.

(4) The early warning department should also check the status of early warning work of safety management at all levels and be responsible for the organization of the early warning of the entire highway construction enterprise, the organization, coordination, and command of the precontrol work. The countermeasure database mainly includes two systems. The first is a system that summarizes, compares, and evaluates the existing successful countermeasures and failed countermeasures and mainly provides reference for future work. The second is a precontrol model countermeasure system, which is used for highway construction mainly to forecast the company's future production safety situation.

3. Experiment

The experiment in this article compares the real-time warning model based on the Internet of Things with the traditional warning model. This paper combines the NS-2 simulation method with the work process of the early warning system and analyzes and evaluates the early warning accuracy and false alarm rate of the real-time early warning model based on the Internet of Things. The construction monitoring road area is set to 800 meters ∗ 1000 meters, the simulation time is 1000 seconds, and 50 vehicle sensor nodes, 10 cluster head nodes, and 1 base station are randomly deployed. In the simulation, the vehicle sensor node and cluster head node are set to have the same transmit power and data communication radius. When data are transmitted on the Internet of Things, the channel rate is 1 Mbps, the propagation delay is 2 μs, the size of the encapsulated data packet is 8190 bits, the size of the control information data packet fed back by the base station is 400 bits, and the time warning phase is assumed to be 5 Stages. The warning distance in the space is 500 meters.

The highway engineering line is long; the terrain geology, hydrological conditions, site construction environment are complicated, the weather is harsh, and many obstacles may be encountered. Therefore, the requirements for electronic tags are higher, especially for its read-write distance and antiinterference ability. Passive electronic tags are susceptible to interference, and the maximum read-write distance is 10 m, which is not suitable for highway site construction needs. To cope with the shortcomings of passive electronic tags, active electronic tags are often used to sense the security situation. The maximum reading distance of the active electronic tag can reach 100 m, and it has strong antiinterference ability. It uses its own radio frequency energy to send data to the reader and has strong adaptability to the complex construction environment of highway engineering. The system writes an electronic product code for each electronic tag, combining the electronic tag with construction tools and equipment. The reader antenna obtains the induced current, the electronic tag enters the reader signal range, the energy is activated, and it sends its own encoded information to the reader. It aggregates the data through the ZigBee sensor network, sends it to the front-end alarm unit, and sends relevant information to the alarm unit to determine the safety status of the project construction.

4. Discussion

4.1. Information Processing

After the system obtaining the information, it needs to be analyzed and processed, so as to more fully grasp the safety status of highway engineering construction. The front-end alarm unit uses cloud computer technology to perform distributed calculations on security conditions. After reading the tag information, query and decode the relevant information and analyze it to get the items represented by the tag. According to the factors such as item type and separation distance, the risk assessment is performed by the LEC method. Risk R = L × E × C, R is the magnitude of risk, L is the probability of an accident (values according to Table 1), and E is the exposure of personnel. The frequency of accidents (values according to Table 2) C is the severity of the consequences of the accident (values according to Table 3). Based on the above values, the system calculates the results, evaluates the danger level, and automatically processes them (as shown in Table 4).

Table 1

Possibility of accident (L).

Point value	L
10	Must happen
6	Quite likely
3	Possible, but not often
1	Less likely
0.5	Very unlikely
0.2	Extremely unlikely
0.1	Practically impossible

Table 2

How often people are exposed to hazardous environments (E).

Point value	E
10	Continuous exposure
6	Daily working hours exposed
3	Once a week
2	Once a month
1	Several times a year
0.5	Very rare

Table 3

The severity of the consequences of the accident (C).

Point value	C
100	Catastrophe, many deaths
40	Disaster, several deaths
15	Very serious, 1 person died
7	Severely disabled
3	Severe injury
1	Minor injury

Table 4

Classification and treatment of danger levels (R).

Point value	Degree of danger	Processing method
≥320	Extremely dangerous to continue working	The alarm is immediately issued through the front-end alarm, and the owner, supervision, and construction unit are notified via SMS at the same time, and recorded to the background server
160–320	Highly dangerous and needs immediate improvement
70–160	Significantly dangerous and needs improvement
20–70	More dangerous, need attention	Immediate alarm via front alarm
≥20	Slightly dangerous and acceptable

4.2. Performance Comparison

Figures 5 and 6 show the error performance curves of the model in this paper and the traditional model, which shows that the early warning model in this paper has faster convergence than traditional warning, which illustrates the real-time early warning model of highway construction safety based on the Internet of Things proposed in this paper. Figure 7 shows the early warning accuracy rates of the two early warning models under different sample sets. Experiments show that the accuracy rate of this model is nearly 5% higher than the traditional early warning model, and the false alarm rate is reduced by nearly 4%.

[figure omitted; refer to PDF][figure omitted; refer to PDF][figure omitted; refer to PDF]

5. Conclusions

Highway engineering has a long construction period, a complex construction environment, many cross-operations, and great potential safety hazards. If effective management measures are neglected, safety accidents may occur, resulting in large property losses and even serious casualties. To prevent safety accidents, it is necessary to take effective safety supervision measures. Although many units focus on self-inspection and self-correction, and increase safety supervision, the results achieved are not satisfactory. Early warning processing is not in place, and a lot of manpower and material resources may be wasted. Therefore, under the limited manpower and financial arrangements, timely and effective safety supervision of the construction site and the effective prevention of safety accidents are important goals of the construction unit.

The Internet of Things technology can track, monitor, and manage in real time, and it is applied to the safety management of highway engineering construction. It can overcome the shortcomings of traditional safety management methods, can promptly warn, find, and eliminate hidden safety hazards, and ensure the safety of highway engineering construction. This paper proposes a real-time early warning model for highway construction safety based on the Internet of Things and suggests that the model can be combined with wireless monitoring technology to further improve the real-time and early warning accuracy of the model.

The intelligent monitoring system is applied to the safety management of highway engineering construction, which not only meets the needs of safety management, but also tracks and early warns the safety protection facilities of construction workers on-site. It can detect and deal with the potential safety hazards as early as possible, eliminate the safety accidents in the bud state, minimize the possible losses caused by various risks, and achieve effective control of highway construction safety accidents. And it can ensure that the construction of the project is safe, smooth, and stable, reduce unnecessary losses, and help improve the efficiency of the project. At the same time, the system also plays a positive role in promoting the modernization, informationization, and intelligentization of highway engineering construction safety management. Therefore, it is worth popularizing and applying this intelligent supervision system in the safety management work of similar highway projects.

Acknowledgments

This work was supported by National Key R & D Program of China (No. 2017YFC0805303).

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