# Design and Analysis of Bridge Inspection System Based on Wireless Communication and Internet of Things Technology

Authors: Haidong Sun; Zhengtao Zhang; Peng Li
Journal: Wireless Communications and Mobile Computing
 (2021)

---

## Abstract

The continuous development of information technology and various electronic devices has accelerated the process of informatization and digitization, enabling the development and application of the emerging technology of wireless communication and the Internet of Things. Since the continuous occurrence of vicious bridge collapse accidents in China in recent years, the problem of bridge inspection has become a hot topic among the people. At the same time, how to apply wireless communication and the Internet of Things technology to bridge inspection systems has also become a new research topic. This article mainly studies the design and analysis of bridge detection systems based on wireless communication and Internet of Things technology. In order to expand the field of bridge detection and standard management and improve the credibility and reliability of safety problem prediction and evaluation, the bridge detection system will integrate IoT sensing, internet, remote communication, digital signal analysis and processing, big data knowledge mining, big data prediction and other technologies, design and analysis of the main structure of roads and bridges, and other multifaceted knowledge fields and build a professional intelligent digital network based on bridge inspection data collection, monitoring, analysis, evaluation, and early warning. From design to use and maintenance of the bridge, a digital neural network spanning time and space throughout the life cycle is constructed to construct a digital brain with bridge sensing points as neurons. This paper uses high-power infrared sensor equipment, satellite positioning systems, sensor equipment, and other technical equipment to achieve the purpose of data communication and exchange and realize intelligent positioning, identification, supervision, tracking, and other functions, making the wireless communication and Internet of Things reliable transmission, comprehensive perception, intelligent processing, and other capabilities very effective in the field of bridge inspection. Through the research and analysis of this article, there are more and more bridge inspection systems developed by the Internet of Things and wireless communication technology in China, and the percentage of related equipment used can reach more than 90%. The functions of the bridge inspection system are becoming more and more complete, and the results of the inspection data are also increasing.


---

## Body

## 1. Introduction

As an important traffic artery, bridges are valued by all walks of life due to their complex structure, large capital requirements, and long service life, and their safety is directly related to the safety of pedestrians. The real inspection data of bridges and their safety degree are more concerned by people [1, 2]. The use of a manual mode to detect bridges cannot be directly and effectively applied to the maintenance and inspection of some large-scale bridge projects. Therefore, it is necessary to establish an intelligent system for real-time bridge inspection to detect and evaluate the normal operation of roads and bridges in real time. During the operation period, the bearing capacity of the main structure of the bridge, the operational status, and the endurance of the main structure can be monitored in real time [3, 4]. The widespread use of network communication technology has caused great changes in the way people obtain data, especially for some units that have high requirements for data mobility and timeliness. Bridge detection based on wireless communication and Internet of Things technology systems has also been widely used [5, 6].To the research about the design and analysis of bridge detection systems based on the Internet of Things and wireless communication technology, many scholars have discussed it. For example, Zhang and others have proposed that the Internet of Things has strong penetration, large amount of actions, and good overall benefits. It is used in structural engineering inspection to promote the development of wireless communication and Internet of Things technology. It is conducive to the development of intelligent, refined, and networked structures; digital intelligent bridge crack detection methods can improve the efficiency of bridge safety diagnosis and reduce risk factors [7]. Mueller et al. said that bridges are fragile transport infrastructure elements and suggested that special attention should be paid to evacuation plans in the event of flood disasters [8]. According to a report issued by the American Society of Civil Engineers (ASCE), Seo and others pointed out that due to the increasing number of deteriorating bridges, a more efficient and cost-effective bridge inspection alternative is needed [9]. It plays a guiding role in the design and analysis of bridge inspection systems based on wireless communication and Internet of Things technology.This article analyzes the reasons for the development of the bridge inspection system and the status quo at home and abroad and introduces the basic technology, functions, and software and hardware requirements of its use. Based on the analysis of the basic data requirements of the bridge, this paper studies the types of detection-related data, data collection methods, and coding methods used. At the same time, through the standard requirements of bridge construction, this paper forms a geographic location-based monitoring, tracking, assessment, and evaluation model; the unit of authenticity verification of relevant data and the progress, quality, and safety of the inspection data are in a controllable state, providing data for the decision-making of bridge engineering on the support.


## 2. Research on Design and Analysis of Bridge Inspection System Based on Wireless Communication and Internet of Things Technology

### 2.1. Research Status

#### 2.1.1. Current Status of Research in China

In the 1950s, there were no technical conditions to support the evaluation and study of the specific conditions of a bridge. At that time, the basis of judgment was mainly based on personal experience, and the safety of the main bridge was not judged by specific and accurate data [10]. After the national major basic research project “Basic Research on the Safety and Durability of Major Civil Engineering” was put forward, a higher level of requirements was put forward in the monitoring and evaluation of structural installation. Since then, many universities and research institutions have invested manpower and material resources to study bridge structures and other topics. They have achieved relatively satisfactory results.


#### 2.1.2. Status of Overseas Research

Before World War II, experts had explored the health inspection of bridges, but at that time, there were only limited tests and research; the corresponding standard procedures and systems were not produced. Since the 1950s, a large number of bridge collapse accidents and global bridge structural failure accidents have occurred, making experts aware of the importance of real-time bridge inspections, and various types of bridge inspection systems have also appeared.


### 2.2. Module Function Design

The modules designed in this system include the data collection management module, data trend analysis module, state prediction management module, state analysis evaluation module, project supervision management module, inspection information release module, and maintenance decision management module.The data trend analysis module will realize the five functions of data source setting, mining model establishment and training, data browsing, and result viewing. The setting of the data source is a prerequisite for other functions. The establishment of a mining model is the core function of the entire module. The training of the model is an extension of the core function and a prerequisite for viewing the results. Viewing the results makes the effect of the module displayed on the system operator. In front of it, the readability of the module is improved, and it is also easy to understand.


### 2.3. Module Structure Design

The bridge inspection system uses a relatively more traditional C/S software model architecture, but some B/S models are also used in some daily inspection service levels. Among them, the server side is mainly used as a container for deploying databases or multidimensional data sets and performing data analysis, while the client side deploys an application program interface for detecting data. The function of the application is to submit and display data information. When the data is submitted to the data access layer, the data access layer will perform a series of checksum processing on the data. If you need to explore the data in depth, it will first analyze the data, then access the data source and get the corresponding data from the data source, finally return to the application through the data access layer, and present the results to the user. If data mining is not required, but to interact with the source data, the data access layer will directly access the data source and return the obtained results to the application program and display it in front of the user.


## 2.1. Research Status

### 2.1.1. Current Status of Research in China

In the 1950s, there were no technical conditions to support the evaluation and study of the specific conditions of a bridge. At that time, the basis of judgment was mainly based on personal experience, and the safety of the main bridge was not judged by specific and accurate data [10]. After the national major basic research project “Basic Research on the Safety and Durability of Major Civil Engineering” was put forward, a higher level of requirements was put forward in the monitoring and evaluation of structural installation. Since then, many universities and research institutions have invested manpower and material resources to study bridge structures and other topics. They have achieved relatively satisfactory results.


### 2.1.2. Status of Overseas Research

Before World War II, experts had explored the health inspection of bridges, but at that time, there were only limited tests and research; the corresponding standard procedures and systems were not produced. Since the 1950s, a large number of bridge collapse accidents and global bridge structural failure accidents have occurred, making experts aware of the importance of real-time bridge inspections, and various types of bridge inspection systems have also appeared.


## 2.1.1. Current Status of Research in China

In the 1950s, there were no technical conditions to support the evaluation and study of the specific conditions of a bridge. At that time, the basis of judgment was mainly based on personal experience, and the safety of the main bridge was not judged by specific and accurate data [10]. After the national major basic research project “Basic Research on the Safety and Durability of Major Civil Engineering” was put forward, a higher level of requirements was put forward in the monitoring and evaluation of structural installation. Since then, many universities and research institutions have invested manpower and material resources to study bridge structures and other topics. They have achieved relatively satisfactory results.


## 2.1.2. Status of Overseas Research

Before World War II, experts had explored the health inspection of bridges, but at that time, there were only limited tests and research; the corresponding standard procedures and systems were not produced. Since the 1950s, a large number of bridge collapse accidents and global bridge structural failure accidents have occurred, making experts aware of the importance of real-time bridge inspections, and various types of bridge inspection systems have also appeared.


## 2.2. Module Function Design

The modules designed in this system include the data collection management module, data trend analysis module, state prediction management module, state analysis evaluation module, project supervision management module, inspection information release module, and maintenance decision management module.The data trend analysis module will realize the five functions of data source setting, mining model establishment and training, data browsing, and result viewing. The setting of the data source is a prerequisite for other functions. The establishment of a mining model is the core function of the entire module. The training of the model is an extension of the core function and a prerequisite for viewing the results. Viewing the results makes the effect of the module displayed on the system operator. In front of it, the readability of the module is improved, and it is also easy to understand.


## 2.3. Module Structure Design

The bridge inspection system uses a relatively more traditional C/S software model architecture, but some B/S models are also used in some daily inspection service levels. Among them, the server side is mainly used as a container for deploying databases or multidimensional data sets and performing data analysis, while the client side deploys an application program interface for detecting data. The function of the application is to submit and display data information. When the data is submitted to the data access layer, the data access layer will perform a series of checksum processing on the data. If you need to explore the data in depth, it will first analyze the data, then access the data source and get the corresponding data from the data source, finally return to the application through the data access layer, and present the results to the user. If data mining is not required, but to interact with the source data, the data access layer will directly access the data source and return the obtained results to the application program and display it in front of the user.


## 3. Research on Design and Analysis of Bridge Inspection System Based on Wireless Communication and Internet of Things Technology

### 3.1. Collect User Needs

Due to the abnormality of the IoT sensor or the damage in the transmission process, the data transmitted to the database may appear empty, zero, beyond the normal range, or the value within a certain range is constant. This paper takes the real-time monitoring data of a certain bridge as an example to extract the three main structural state parameters of the bridge, namely, temperature, vertical deflection, and suspender cable force.


### 3.2. Outline Design

#### 3.2.1. Data Collection Layer

The data acquisition layer is mainly the sensor system and the data acquisition and transmission system. The purpose of the sensor system is to realize the collection and conversion of various information and data in the bridge detection process. In order to achieve this purpose, the sensor system is equipped with special bridges for environmental monitoring, geometric monitoring, load monitoring, and dynamic and static effect monitoring. The function of the data acquisition and transmission system is to collect the signals output by the sensors, regulate the working sequence and frequency of all sensors, and transmit the collected data to the data processing and analysis system through radio transmission. The sensor system uses wireless communication technology to connect with the data acquisition and transmission system.


#### 3.2.2. Business Logic Layer

The business logic layer is the unit of responsibility for data sorting and analysis. Its main job is to classify the original data so that they can better meet the specific needs of users. With the support of sufficient data, the stress conditions of the bridge under various conditions are collected. The needs of users are different, and the central task of the business logic layer will also change accordingly. From this perspective, the business logic layer can be regarded as a collection of multiple data processing subsystems, and the logic followed by its work is the externalized expression of user demands.


## 3.1. Collect User Needs

Due to the abnormality of the IoT sensor or the damage in the transmission process, the data transmitted to the database may appear empty, zero, beyond the normal range, or the value within a certain range is constant. This paper takes the real-time monitoring data of a certain bridge as an example to extract the three main structural state parameters of the bridge, namely, temperature, vertical deflection, and suspender cable force.


## 3.2. Outline Design

### 3.2.1. Data Collection Layer

The data acquisition layer is mainly the sensor system and the data acquisition and transmission system. The purpose of the sensor system is to realize the collection and conversion of various information and data in the bridge detection process. In order to achieve this purpose, the sensor system is equipped with special bridges for environmental monitoring, geometric monitoring, load monitoring, and dynamic and static effect monitoring. The function of the data acquisition and transmission system is to collect the signals output by the sensors, regulate the working sequence and frequency of all sensors, and transmit the collected data to the data processing and analysis system through radio transmission. The sensor system uses wireless communication technology to connect with the data acquisition and transmission system.


### 3.2.2. Business Logic Layer

The business logic layer is the unit of responsibility for data sorting and analysis. Its main job is to classify the original data so that they can better meet the specific needs of users. With the support of sufficient data, the stress conditions of the bridge under various conditions are collected. The needs of users are different, and the central task of the business logic layer will also change accordingly. From this perspective, the business logic layer can be regarded as a collection of multiple data processing subsystems, and the logic followed by its work is the externalized expression of user demands.


## 3.2.1. Data Collection Layer

The data acquisition layer is mainly the sensor system and the data acquisition and transmission system. The purpose of the sensor system is to realize the collection and conversion of various information and data in the bridge detection process. In order to achieve this purpose, the sensor system is equipped with special bridges for environmental monitoring, geometric monitoring, load monitoring, and dynamic and static effect monitoring. The function of the data acquisition and transmission system is to collect the signals output by the sensors, regulate the working sequence and frequency of all sensors, and transmit the collected data to the data processing and analysis system through radio transmission. The sensor system uses wireless communication technology to connect with the data acquisition and transmission system.


## 3.2.2. Business Logic Layer

The business logic layer is the unit of responsibility for data sorting and analysis. Its main job is to classify the original data so that they can better meet the specific needs of users. With the support of sufficient data, the stress conditions of the bridge under various conditions are collected. The needs of users are different, and the central task of the business logic layer will also change accordingly. From this perspective, the business logic layer can be regarded as a collection of multiple data processing subsystems, and the logic followed by its work is the externalized expression of user demands.


## 4. Related Technology

### 4.1. Internet of Things Technology

#### 4.1.1. RFID

RFID is composed of electronic tags, readers, and antennas. It has the characteristics of noncontact operation, no manpower, but more remote identification, and convenient application.


### 4.2. The Data Fusion Technology

In layman’s terms, data fusion technology is like sorting equipment in a contemporary industry, responsible for classifying, summarizing, and collecting raw data. Although it originated in the military field, it has already shined in commercial and civilian fields at this stage. In the field of bridge safety monitoring, data fusion technology is mainly applied to the screening and classification of original data and intelligent assembly of related data according to attributes. It allows the user to see the desired result more intuitively.


### 4.3. Data Mining Technology

#### 4.3.1. The Multidimensional Data Set

The concept of the multidimensional data sets is developed based on tables and views modeled in common data source views, which are general measures (factual data) and dimensions (which can cover many aspects that we are interested in measuring, such as time, product, and customer) and pass its definition. The data measures and dimensions in the multidimensional data set are derived from the tables and views in the real data source view on which the multidimensional data set is based.


#### 4.3.2. Data Source View

The data source view refers to a logical representation of the basic data source, which contains the model of the logical architecture used by the multidimensional data sets and dimensions as well as the data mining structure. It can be generated from one or more data sources and can also include relationships, calculated columns, and queries that do not exist in the basic data source and data independent of the basic data source. The data source view is transparent to the general client application.


### 4.4. Related Algorithms

#### 4.4.1. Time Series Algorithm Based on Automatic Regression Decision Tree

In this article, the time series algorithm studied is based on decision numbers, linear regression, and Bayesian algorithm. The main functions implemented are the following: it can effectively relearn from data, it supports correct prediction, and it is easy to explain. The time series data set is transformed into data suitable for regression analysis. The transformed data set is used to obtain the target variable in the form of a decision tree. This decision tree has linear regression on its leaves. Therefore, a segmented linear model is constructed. In the automatic regression model, the structure of the decision number is determined by Bayesian technology. In the analysis process, the “predictor variable” and the “target variable” correspond to the predicted value and current value in the time series, respectively.


#### 4.4.2. Deflection Correlation Graph (DCG)

Deflection is the linear displacement of the rod axis in the direction perpendicular to the axis or the linear displacement of the plate and shell midplane in the direction perpendicular to the midplane when a force is applied or a nonuniform temperature changes.Consider a healthy bridge systemH. Making 0≤e,f<1. For M,N∈H,M≠N, let θ∨M,N,1−e,θ^M,N,1−e denote the confidence interval of 1−e. The 1−e correlation between M and N is a random variable, which is defined as
(1)cdeg1−eM,N=1−12∗θ^M,N,1−e−θ∨M,N,1−e.The deflection correlation graph of the system is expressed asICG1−e,1−fH, which is a random graph with interval labels (H,G, label), as shown in
(2)G=M,N:cedg1−eM,N≥1−f,(3)labelM,N=θ∨M,N,1−e,θ^M,N,1−e.Since then, the graph with edge labels obtained according to this definition is called a DCG.


## 4.1. Internet of Things Technology

### 4.1.1. RFID

RFID is composed of electronic tags, readers, and antennas. It has the characteristics of noncontact operation, no manpower, but more remote identification, and convenient application.


## 4.1.1. RFID

RFID is composed of electronic tags, readers, and antennas. It has the characteristics of noncontact operation, no manpower, but more remote identification, and convenient application.


## 4.2. The Data Fusion Technology

In layman’s terms, data fusion technology is like sorting equipment in a contemporary industry, responsible for classifying, summarizing, and collecting raw data. Although it originated in the military field, it has already shined in commercial and civilian fields at this stage. In the field of bridge safety monitoring, data fusion technology is mainly applied to the screening and classification of original data and intelligent assembly of related data according to attributes. It allows the user to see the desired result more intuitively.


## 4.3. Data Mining Technology

### 4.3.1. The Multidimensional Data Set

The concept of the multidimensional data sets is developed based on tables and views modeled in common data source views, which are general measures (factual data) and dimensions (which can cover many aspects that we are interested in measuring, such as time, product, and customer) and pass its definition. The data measures and dimensions in the multidimensional data set are derived from the tables and views in the real data source view on which the multidimensional data set is based.


### 4.3.2. Data Source View

The data source view refers to a logical representation of the basic data source, which contains the model of the logical architecture used by the multidimensional data sets and dimensions as well as the data mining structure. It can be generated from one or more data sources and can also include relationships, calculated columns, and queries that do not exist in the basic data source and data independent of the basic data source. The data source view is transparent to the general client application.


## 4.3.1. The Multidimensional Data Set

The concept of the multidimensional data sets is developed based on tables and views modeled in common data source views, which are general measures (factual data) and dimensions (which can cover many aspects that we are interested in measuring, such as time, product, and customer) and pass its definition. The data measures and dimensions in the multidimensional data set are derived from the tables and views in the real data source view on which the multidimensional data set is based.


## 4.3.2. Data Source View

The data source view refers to a logical representation of the basic data source, which contains the model of the logical architecture used by the multidimensional data sets and dimensions as well as the data mining structure. It can be generated from one or more data sources and can also include relationships, calculated columns, and queries that do not exist in the basic data source and data independent of the basic data source. The data source view is transparent to the general client application.


## 4.4. Related Algorithms

### 4.4.1. Time Series Algorithm Based on Automatic Regression Decision Tree

In this article, the time series algorithm studied is based on decision numbers, linear regression, and Bayesian algorithm. The main functions implemented are the following: it can effectively relearn from data, it supports correct prediction, and it is easy to explain. The time series data set is transformed into data suitable for regression analysis. The transformed data set is used to obtain the target variable in the form of a decision tree. This decision tree has linear regression on its leaves. Therefore, a segmented linear model is constructed. In the automatic regression model, the structure of the decision number is determined by Bayesian technology. In the analysis process, the “predictor variable” and the “target variable” correspond to the predicted value and current value in the time series, respectively.


### 4.4.2. Deflection Correlation Graph (DCG)

Deflection is the linear displacement of the rod axis in the direction perpendicular to the axis or the linear displacement of the plate and shell midplane in the direction perpendicular to the midplane when a force is applied or a nonuniform temperature changes.Consider a healthy bridge systemH. Making 0≤e,f<1. For M,N∈H,M≠N, let θ∨M,N,1−e,θ^M,N,1−e denote the confidence interval of 1−e. The 1−e correlation between M and N is a random variable, which is defined as
(1)cdeg1−eM,N=1−12∗θ^M,N,1−e−θ∨M,N,1−e.The deflection correlation graph of the system is expressed asICG1−e,1−fH, which is a random graph with interval labels (H,G, label), as shown in
(2)G=M,N:cedg1−eM,N≥1−f,(3)labelM,N=θ∨M,N,1−e,θ^M,N,1−e.Since then, the graph with edge labels obtained according to this definition is called a DCG.


## 4.4.1. Time Series Algorithm Based on Automatic Regression Decision Tree

In this article, the time series algorithm studied is based on decision numbers, linear regression, and Bayesian algorithm. The main functions implemented are the following: it can effectively relearn from data, it supports correct prediction, and it is easy to explain. The time series data set is transformed into data suitable for regression analysis. The transformed data set is used to obtain the target variable in the form of a decision tree. This decision tree has linear regression on its leaves. Therefore, a segmented linear model is constructed. In the automatic regression model, the structure of the decision number is determined by Bayesian technology. In the analysis process, the “predictor variable” and the “target variable” correspond to the predicted value and current value in the time series, respectively.


## 4.4.2. Deflection Correlation Graph (DCG)

Deflection is the linear displacement of the rod axis in the direction perpendicular to the axis or the linear displacement of the plate and shell midplane in the direction perpendicular to the midplane when a force is applied or a nonuniform temperature changes.Consider a healthy bridge systemH. Making 0≤e,f<1. For M,N∈H,M≠N, let θ∨M,N,1−e,θ^M,N,1−e denote the confidence interval of 1−e. The 1−e correlation between M and N is a random variable, which is defined as
(1)cdeg1−eM,N=1−12∗θ^M,N,1−e−θ∨M,N,1−e.The deflection correlation graph of the system is expressed asICG1−e,1−fH, which is a random graph with interval labels (H,G, label), as shown in
(2)G=M,N:cedg1−eM,N≥1−f,(3)labelM,N=θ∨M,N,1−e,θ^M,N,1−e.Since then, the graph with edge labels obtained according to this definition is called a DCG.


## 5. Design and Analysis of Bridge Inspection System Based on Wireless Communication and Internet of Things Technology, Experimental Research, and Analysis

### 5.1. Analysis of Some Equipment Usage

Most of the use of Internet of Things technology is inseparable from sensing instruments; therefore, the use of sensor equipment in bridge detection can reflect the advantages and disadvantages of the Internet of Things technology in bridge detection. Some of the sensing instruments installed in bridge detection systems at home and abroad are more like Table1.Table 1
Comparison of some sensing instruments installed in bridge detection systems at home and abroad.
HomeOverseasAnemometer74.83%71.37%Thermometer91.62%42.86%Displacement meter74.91%71.35%Vibrating wire strain gauge33.29%28.52%Fiber optic sensor33.28%28.57%According to the analysis in Figure1, it can be concluded that China is very concerned about the detection of bridges and actively uses new technologies to find solutions. Compared with foreign countries, the number of bridges that use sensing equipment for detection in China is 1.2 times more than that in foreign countries, indicating that the Internet of Things technology is used to a high degree in domestic bridge inspection.Figure 1
Comparison of some sensing instruments installed in bridge detection systems at home and abroad.


### 5.2. Analysis of Recovery Data Experiment Results

The function of restoring data is to find out unreasonable data based on the structure of data mining and replace unreliable data with the value predicted by data mining. Through the use of the existing bridge detection system, the reliability of the recovery data of the existing bridge detection system is tested. The following is to compare the data before and after the recovery of the tie rod cable force. The specific results of the comparison are shown in Table2. The middle data is the data measured at zero points of the day.Table 2
Summary of comparison of raw and recovered data.
Raw dataData after recovery2021/1/1182822021/1/2543842021/2/0983832021/2/2381812021/3/0921822021/3/238383As shown in Figure2, the existing bridge inspection system can create relatively complete data for the recovery function. The recovered data does not have a large jump from the original data, eliminating unreasonable data and transforming unreasonable data into more accurate data based on predictions, which shows that the existing bridge detection system can provide more accurate data for bridge detection and provides a direction for the design of a bridge monitoring system based on the Internet of Things technology.Figure 2
Comparison of raw and restored data.


## 5.1. Analysis of Some Equipment Usage

Most of the use of Internet of Things technology is inseparable from sensing instruments; therefore, the use of sensor equipment in bridge detection can reflect the advantages and disadvantages of the Internet of Things technology in bridge detection. Some of the sensing instruments installed in bridge detection systems at home and abroad are more like Table1.Table 1
Comparison of some sensing instruments installed in bridge detection systems at home and abroad.
HomeOverseasAnemometer74.83%71.37%Thermometer91.62%42.86%Displacement meter74.91%71.35%Vibrating wire strain gauge33.29%28.52%Fiber optic sensor33.28%28.57%According to the analysis in Figure1, it can be concluded that China is very concerned about the detection of bridges and actively uses new technologies to find solutions. Compared with foreign countries, the number of bridges that use sensing equipment for detection in China is 1.2 times more than that in foreign countries, indicating that the Internet of Things technology is used to a high degree in domestic bridge inspection.Figure 1
Comparison of some sensing instruments installed in bridge detection systems at home and abroad.


## 5.2. Analysis of Recovery Data Experiment Results

The function of restoring data is to find out unreasonable data based on the structure of data mining and replace unreliable data with the value predicted by data mining. Through the use of the existing bridge detection system, the reliability of the recovery data of the existing bridge detection system is tested. The following is to compare the data before and after the recovery of the tie rod cable force. The specific results of the comparison are shown in Table2. The middle data is the data measured at zero points of the day.Table 2
Summary of comparison of raw and recovered data.
Raw dataData after recovery2021/1/1182822021/1/2543842021/2/0983832021/2/2381812021/3/0921822021/3/238383As shown in Figure2, the existing bridge inspection system can create relatively complete data for the recovery function. The recovered data does not have a large jump from the original data, eliminating unreasonable data and transforming unreasonable data into more accurate data based on predictions, which shows that the existing bridge detection system can provide more accurate data for bridge detection and provides a direction for the design of a bridge monitoring system based on the Internet of Things technology.Figure 2
Comparison of raw and restored data.


## 6. Conclusions

The Internet of Things technology, data processing, and analysis technology occupy more and more shares in the construction of smart transportation, and they have been widely used in road traffic and road bridge monitoring in many cities. This thesis covers multiple fields such as the Internet of Things, communications, electronics, data processing, and analysis and can effectively promote the development of related industries such as terminal manufacturing, industrial software design, and development. This paper takes integrated information services as the entry point and key industry applications as a breakthrough point to form multilevel, all-round, and serialized products and services, which can effectively realize the multiplication of the information industry, the Internet of Things industry, the big data industry, and the cloud computing industry, planning to obtain a huge industry-driven effect. The development of bridge detection systems can promote technological innovation in industries such as electronics, communications, and the Internet of Things and open up new huge markets for the modern information industry, big data, cloud computing, communications, and electronic technology.



---

*Source: 3410844-2021-10-13.xml*