STUDY ON THE APPLICATION OF DEEP

LEARNING TECHNOLOGY AND BIM MODEL

IN THE QUALITY MANAGEMENT OF BRIDGE

DESIGN AND CONSTRUCTION STAGE

Weiqi Zhu

Department of Economics and Management, Jiangyin Institute of Technology,

Jiangyin, Jiangsu, 214405, China

zhu20218882021@163.com

Reception: 28/02/2023 Acceptance: 19/04/2023 Publication: 06/05/2023

Suggested citation:

Zhu, W. (2023). Study on the application of deep learning technology and

BIM model in the quality management of bridge design and construction

stage. 3C TIC. Cuadernos de desarrollo aplicados a las TIC, 12(2), 137-154.

https://doi.org/10.17993/3ctic.2023.122.137-154

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137

ABSTRACT

The development of the transportation industry can effectively accelerate the speed of

economic development, in which bridges occupy an important position in

transportation. The safety of the bridge design and construction process is a key part

of bridge construction, and relying on human resources to investigate safety hazards

greatly affects efficiency. In this paper, we combine deep learning technology and BIM

model to explore the synergistic effect of both on the quality management of bridge

construction phase and analyze the measured data. The results show that the

application of BIM model can improve the efficiency by 35% compared with the

traditional 2D CAD drawings, and the accuracy of data analysis can be improved by

12.51% and 14.26% for DNN and DBN models based on deep learning, respectively.

The addition of the GSO algorithm leads to a further 19.19% improvement in the

training accuracy of the coupled model. Finally, the optimization model was used to

analyze the load factors and force majeure factors that affect the safety of the bridge,

and to find the structural factors that affect the safety of the bridge design, which

provides guidance to ensure the quality of the bridge during the construction process.

KEYWORDS

BIM model; CATIA modeling; deep learning; bridge construction; quality and safety

INDEX

ABSTRACT

KEYWORDS

1. INTRODUCTION

2. DESIGN OF THE MODEL

2.1. Parametric modeling of the bridge structure

2.2. Deep learning model

3. ENGINEERING APPLICATION RESEARCH

3.1. Specific application of the BIM model in the design and construction of bridge

engineering

3.2. Specific application of deep learning technology in bridge engineering quality

monitoring

4. RESULTS AND DISCUSSION

4.1. Analysis of test results

4.2. Data validity analysis

4.3. Structural safety analysis

5. CONCLUSION

REFERENCES

https://doi.org/10.17993/3ctic.2023.122.137-154

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ABSTRACT

The development of the transportation industry can effectively accelerate the speed of

economic development, in which bridges occupy an important position in

transportation. The safety of the bridge design and construction process is a key part

of bridge construction, and relying on human resources to investigate safety hazards

greatly affects efficiency. In this paper, we combine deep learning technology and BIM

model to explore the synergistic effect of both on the quality management of bridge

construction phase and analyze the measured data. The results show that the

application of BIM model can improve the efficiency by 35% compared with the

traditional 2D CAD drawings, and the accuracy of data analysis can be improved by

12.51% and 14.26% for DNN and DBN models based on deep learning, respectively.

The addition of the GSO algorithm leads to a further 19.19% improvement in the

training accuracy of the coupled model. Finally, the optimization model was used to

analyze the load factors and force majeure factors that affect the safety of the bridge,

and to find the structural factors that affect the safety of the bridge design, which

provides guidance to ensure the quality of the bridge during the construction process.

KEYWORDS

BIM model; CATIA modeling; deep learning; bridge construction; quality and safety

INDEX

ABSTRACT

KEYWORDS

1. INTRODUCTION

2. DESIGN OF THE MODEL

2.1. Parametric modeling of the bridge structure

2.2. Deep learning model

3. ENGINEERING APPLICATION RESEARCH

3.1. Specific application of the BIM model in the design and construction of bridge

engineering

3.2. Specific application of deep learning technology in bridge engineering quality

monitoring

4. RESULTS AND DISCUSSION

4.1. Analysis of test results

4.2. Data validity analysis

4.3. Structural safety analysis

5. CONCLUSION

REFERENCES

https://doi.org/10.17993/3ctic.2023.122.137-154

1. INTRODUCTION

With the continuous development of China's economy, transportation modes are

showing a trend of diversification. The establishment of a well-connected

transportation network is of great importance for the development of national

transportation, the promotion of inter-regional economic exchanges and the flow of

talents [1,2]. Among them, bridge construction is the core link in the transportation

network, which can effectively solve the problem of inconvenient traffic on both sides

of the river basin. The safety of bridges is a key consideration in the design and

construction process of bridges, which can cause very serious traffic accidents if the

quality is not up to par [3-5]. Safety problems not only cause national economic

losses, but also have a destructive effect on the ecological environment to some

extent [6-8]. The safety issues of bridges should have a preliminary prediction during

the construction phase and also throughout the construction phase of the project,

where data from all aspects of bridge construction should be collected, processed and

analyzed. Bridge quality and safety monitoring mainly include two aspects of data

acquisition as well as safety index evaluation [9,10]. BIM technology is a technology

that integrates the data in the construction process through a building information

model. By integrating a large amount of data and information, it can read the key

information and realize data interoperability. It enables information transfer and

resource sharing in the pre-project preparation, process implementation and quality

control stages at the end of the project. It plays an important role in quality inspection,

safety management, budgeting, and progress monitoring of the construction process

[11-14]. BIM technology can effectively improve efficiency, increase calculation

accuracy, shorten construction cycle time and scientifically maintain equipment.

Neural network deep learning is a modern tool for data interpretation and result

prediction, which can quickly read information and extract data feature values, and

input calculation results after training with embedded algorithms. This learning

approach is currently combined with various fields, and the use of a deep learning

approach can help us to quickly make predictions about the results and greatly

improve efficiency [15-19]. If the deep learning approach is applied to the quality

inspection in the bridge construction process, not only the rate of problem solving can

be improved but also the accuracy of calculation results will be enhanced. Currently,

some scholars have conducted studies on the use of BIM technology and deep

learning methods in bridge construction and obtained desirable results [20-24].

Pan [25] et al. investigated a clustering-based log mining method for building

information modeling (BIM), while combining a novel clustering algorithm with an

efficient fuzzy Kohonen clustering network (EFKCN) to classify information with

different characteristics. The data were analyzed using regression prediction method

and the results showed that the model can be better for model building. D Forgues et

al [26] used the BIM model to reduce the cost of project completion and shorten the

project cycle by classifying the data through linear regression. The results showed

that the use of the BIM model can effectively discover the causes affecting the quality

of bridge construction and improve efficiency. R Edirisinghe [27] developed a safety

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life cycle BIM prediction model with high maturity and analyzed the factors affecting

the model by data from real cases. As a result, five main factors affecting the BIM

model were found, and the model was improved to propose a life cycle BIM maturity

model (LCBMM) supported by actual data. Liang et al [28] proposed a novel deep

learning model based on a three-stage image training strategy to analyze the bridge

design structure, and in order to train and analyze the data effectively focused on the

model's robustness of the model was focused on in order to train and analyze the

data effectively. The results show that the robustness of the model is very good and

the accuracy of the prediction is more than 90%. T Abbas [29] et al. used a neural

network (ANN) model to predict and analyze the aerodynamic phenomena around the

bridge design process. In the paper, the neural network and the structural model were

combined and trained on bridge data with different interfaces and different geometric

features. The results show that artificial neural networks can predict the bridge

construction process more accurately, and this method can provide ideas for the

design of bridges with larger spans. Qz A [30] proposed a vision-based method for the

detection of cracks in concrete bridge decks, which is a one-dimensional convolutional

neural network (1D-CNN) and long short-term memory (LSTM) method in the image

frequency domain. The method is trained using a large number of cracked or

uncracked bridge deck images with high efficiency and accuracy. The results show

that the developed model can reach 99.25% accuracy with respect to the test data.

Moreover, the 1D-CNN-LSTM model can effectively reduce the computation time

compared with other neural network deep learning approaches. Ma [31] et al.

proposed a data-driven method for strain data detection and differentiation of vehicles

for detecting vehicle operation in large-span bridges. A neural network deep learning

approach is used to track and identify vehicles to ensure traffic safety on bridge

pavements. The results show that this detection method is relatively robust and

accurate and is able to predict traffic conditions well despite the presence of noise.

Dinh K [32] et al. proposed a coupled algorithm combining traditional image

processing techniques and deep convolutional neural networks for the localization and

detection of steel reinforcement during the construction of bridge construction. The

images are first processed by offset and normalization methods for locating the pixels

containing potential rebar peaks. The results obtained in the first step were then

classified by a convolutional neural network (CNN) and a total of 26 bridge data were

analyzed. The results showed that the average accuracy of the model's calculation

results exceeded 97.75%, and the overall accuracy of the whole bridge test was about

99.60%. Kim [33] et al. proposed a vision sensor-based UAV bridge inspection

method to troubleshoot the deterioration of bridges over a long period of time to

ensure the quality and safety issues of bridges. The test first used a UAV to fly around

and obtain a point cloud-based background model. A regional convolutional neural

network (R-CNN) model was then used to detect the crack structure on the bridge

surface and calculate the thickness and length of the cracks. A new network is

generated from the pre-trained network and used to collect 384 crack images with 256

×

256 pixel resolution. The results show that the model is highly accurate in the

identification and detection of bridge quality. Yang [34] et al. proposed a deep learning

model to evaluate the stability and safety of bridge structures, and the model maps a

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network of large data into a very small volume of eigenspheres. Where the data in the

spheres are normal values and the abnormal data are outside the eigenspheres. The

results show that this model learning approach is effective and the computed practical

results are superior compared to other advanced methods. In summary, the

application of BIM technology and deep learning neural networks for monitoring the

management of safety issues in bridge construction has ideal results in terms of both

data resources and access integration and project outcome prediction. However, the

two technologies are separate in the research work so far, and the combined use is

relatively rare.

Therefore, this paper explores the synergistic effect of coupled models in the

application of bridge construction stage quality management based on the BIM model,

combined with a deep learning approach. Firstly, the bridge structure is parametrically

modeled by CATIA software, and the potential problems in the bridge construction

process are identified through the BIM model. Then the information is input into the

neural network through the input layer, while comparing the deep learning approach

with the traditional calculation method, and evaluating the accuracy of the deep

learning approach according to the calculation results. Further, the GSO algorithm is

used to optimize the deep learning method, and the validity of the computational

results of the optimized algorithm is analyzed. Finally, the structural factors affecting

safety and stability, including load factors and force majeure factors, are analyzed by

this optimization algorithm. It provides an idea for the bridge construction process

monitoring and the bridge quality and safety prediction.

2. DESIGN OF THE MODEL

2.1. PARAMETRIC MODELING OF THE BRIDGE

STRUCTURE

At present, BIM models are more widely used in all stages of engineering design,

construction and maintenance, among which the application in the transportation

industry is the most. Various urban transportation fields in China have made

application requirements for the application of BIM technology, and strict standards

have been set for the accuracy of the results. The standardized regulations are

important to ensure the construction quality and safety of the project, and all aspects

of bridge design should be carried out in strict accordance with the standards. the key

aspect of BIM application lies in the construction of the initial model, and the efficiency

and quality of the modeling directly affect the project. CATIA software is a good

modeling tool, which is a software developed by Dassault Systèmes, France, mainly

used for the construction of mechanical structure models. the most important feature

of CATIA is that it can model according to parametric spatial points, spatial curves and

surface features, and has a good effect on the parametric modeling of large bridges

(arch bridges, T-bridges, etc.).

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CATIA is an integrated software with CAD/CAE and CAM, which can provide a set

of mature technical solutions from product design and final product landing. CATIA

has the functions of 3D parametric design, dimensional constraints, parametric

modification, etc. The specific connotations of parametric include the following.

1.

Custom parameters: Users can input specific parameters according to their

actual needs during the modeling process, and there are various types of

parameters to meet different types of customers.

2.

All-round dimensional constraints: This is the advantage of CATIA.

Dimensional constraints refer to the specific constraints on the dimensions of

each drawing that are mandatory for users in the modeling process. If the

markup is missed, the modeling process cannot proceed. Also, the user has to

give a global dimension so that the accuracy can be effectively controlled

during the modeling process.

3.

Dimensional parameter modification: In the process of parametric modeling,

there must be constraints on the overall dimensions. But the detail part may

exist with the subject management, the size can not be completely determined.

CATIA can give the drive size modification function to this part, to achieve size

modification and change.

4.

Structural logic: In the design process respect the top-to-bottom concept,

specifically expressed as a loop. In the setting or modification link soft solutions

are made to record, the user can check or modify again.

5. Standardized design: For the frequently used structure, the software provides a

parametric template. That is, the software can save this part of the design in

the inventory, and the same is defined in accordance with the standard size.

Users can transfer in and out from the library in the process of use, to facilitate

the work of users.

2.2. DEEP LEARNING MODEL

In recent years, deep learning (DL) and artificial intelligence have been very closely

integrated and have penetrated into many industrial fields. DL has unique advantages

in extracting feature values from big data and processing data, and therefore has a

wide range of applications in computer systems, speech idiosyncratic recognition and

language expression. The input is generally located at the lower level. The input

transmits the data further to a higher layer. The layers of transmission finally reach the

output layer, which is the highest layer. It is important to note that the data is mined

step by step during the transfer process, and finally, the data is obtained with some

distribution pattern. The bridge structure is composed of many different small parts,

and each part is related. Therefore, many details need to be considered when

evaluating construction safety and quality, and the amount of data is relatively large.

To address this problem this paper uses the DL model for analysis, and adds the deep

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neural network (DNN) model and the deep belief network (DBN) model to the DL

model, aiming to further improve the accuracy of the model prediction results. Which

can be transformed into an output matrix when extracting the feature values, as

shown in Equation 1.

(1)

Where denotes the fault information and denotes the fault parameters. By

constructing a mixture model about the features, the distribution expression of the

features of the faults can be obtained as follows.

(2)

Where denotes the frequency of the fault wave and the subscript expresses

the number of faults. According to the deep learning method, the dynamic

components of the faults can be obtained by the distribution probabilities of the visible

and implicit layers corresponding to the initial detection and model reconstruction of

the bridge as shown in the following equation.

(3)

Where denotes the fusion transfer parameter containing the fault features, and

based on the above-extracted eigenvalues, combined with the spectral analysis, the

density component of the monitoring fault phase coupling is obtained as shown in the

following equation.

(4)

Where represents the peak of the bridge fault state. The joint analysis method is

applied to it, and the expression of energy distribution can be obtained as follows:

(5)

Where denotes the dimensionless parameter for equipment failure in the bridge

monitoring process, and analysis of the data yields the output of automatic fault

tolerance for bridge equipment failure monitoring as:

R

=

r

1

r

2

…r

1,n

r1,1 r2,2 …r2,n

⋮ ⋮ ⋱ ⋮

rn,1 rn,2 …rk,n

k

rnk

R

i=

∑

i=1

R+r

n,k

mi

mi

i

T

=

R

i

(n+1)+R

mi

n

R

x=

vx

T+Ri

×∑

i=1

R

vx

M

=

N

x

Ri+Rx

+∑

i=1

R

Nx

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(6)

From the above analysis, it can be seen that when a bridge fails, the extraction of

fault feature values can help engineers quickly determine the cause of the failure and

find the location of the failure. The bridge fault tolerance model constructed can

improve the fault tolerance of the device.

DNN is an artificial neural network composed of the input layer, implicit layer and

output layer, DNN is mainly through the learning behavior to solve the mapping

relationship between the input layer and output layer. DNNs can achieve more

satisfactory results for both feature value extraction and computational result

prediction.

DBN is a generative model with multiple hidden layers because it is composed of

multiple restricted Boltzmann machines (RBMs) cumulatively, which is a model with

random probability distribution consisting of a set of visible units as well as hidden

units. DBN is able to retain data features and on this basis, the computational process

can be simplified by reducing the dimensionality as much as possible. Therefore, the

effective learning method of DBN can be thought of as reducing the complex model to

a combination of many simple models, and learning the input parameters by passing

them in layers. The DBN is more accommodating and can input different kinds of

data. And the data transfer is iterative, i.e., the previous data calculation result will be

used as the next input data, so DBN has an efficient learning method as well as

scientific classification performance.

3. ENGINEERING APPLICATION RESEARCH

Deep learning technology and BIM models have a role in the quality management

of bridge design and construction phases that should not be underestimated. Among

them, BIM technology and the CATIA software module available in recent years have

a guiding role in the design of the bridge in the early stage and the subsequent

construction problem ranking, which can reduce the manual input. Deep learning

technology, on the other hand, can be used to troubleshoot bridge quality problems

that occur during the construction phase and to monitor the bridge quality monitoring

system, which can provide statistical analysis efficiency by excluding invalid data. This

section will detail the specific application of the BIM model and deep learning

approach in bridge construction.

3.1. SPECIFIC APPLICATION OF THE BIM MODEL IN THE

DESIGN AND CONSTRUCTION OF BRIDGE

ENGINEERING

G

= 2T+

N

g

+N

x

mi

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For the bridge project, the bridge design and construction and bridge engineering

feasibility links, through the application of BIM technology, can more effectively

accelerate the progress of the bridge project design. the advantages of BIM

technology applied to bridge design are mainly the following two points, one is that

through the use of the technology, you can check out in advance whether there are

defects and deficiencies in the project, timely improvement and processing, for the

later bridge engineering project The first is that by using this technology, the defects

and shortcomings of the project can be identified in advance, and timely

improvements can be made to bring more convenience to the later bridge projects.

The second is that the application of BIM technology helps designers and constructors

to have a deeper understanding of the project, and can accurately analyze and judge

the different cost situations involved in the project to facilitate further reduction of

bridge project costs. the main contents of the four main steps of BIM technology

application are:

1.

In the design and construction of bridge projects, the first step of BIM

technology application requires that the designer should continuously improve

the model according to the requirements of different periods and the actual

needs of the project so as to ensure the accuracy of the model. The

construction process mainly has the following three steps: the first is to

establish the relevant parameter library, the second is to build a scientific and

accurate model, and the third is to reasonably set the corresponding

reinforcement module. Among them, the establishment of the relevant

parameter library and the next step of building the model can use Dassault's

CATIA software to achieve parametric modeling. CATIA 3D model can pass

management object data, index data, etc. to the construction stage, which can

make the modeling process more convenient with higher accuracy.

2.

After constructing an accurate parametric model of the bridge project in the

early stage, to develop a perfect construction strategy, advanced BIM

technology needs to be further applied. By means of simulation, the link is

clearly presented, and the construction differences are compared, so that the

simulated construction process is visualized and dynamic. This step will enable

the designer to make a more scientific and accurate judgment on the safety

and economy of the bridge structure.

3.

Volume statistics, refers to the application of BIM technology application

statistics to obtain accurate volume calculation values in the bridge engineering

design period. Compared with the traditional method of calculating the points,

lines and surfaces of the two-dimensional plane, the calculation task is

completed with Excel tables. Using BIM technology, in building an accurate

three-dimensional model of the bridge, it is possible to scientifically select the

design components and suitable materials, and use the automated

measurement function to achieve the purpose of determining the bridge

volume. This method, in addition to reducing the calculation time, improves the

accuracy of calculation by about 35% on average and saves the energy input

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of design staff. In addition, for the modeling of complex curves under linear

engineering, it is also possible to accurately determine the twisted part and

accurately measure the length, area and volume of the bridge by using the

CATIA software in BIM technology.

4.

In the design and construction of bridge projects, after completing the above

work, Navisworks and other related software can be further used. For the

construction of the BIM model for collision analysis, to complete the preliminary

check, proofreading and audit and other different work. At the same time, it

helps to find out whether there are defects and deficiencies in the construction

drawings, and further correct them. In addition, the use of BIM technology can

also achieve the task of comparing procurement lists and determining what

needs to be procured.

3.2. SPECIFIC APPLICATION OF DEEP LEARNING

TECHNOLOGY IN BRIDGE ENGINEERING QUALITY

MONITORING

It is necessary to monitor the health system data in the design and construction of

bridges. Compared to most traditional test and analysis methods that rely on statistical

theory and require extensive domain knowledge, monitoring systems based on deep

learning techniques are more suitable for large-scale data sets. The main work of the

deep learning technique monitoring system is to perform health monitoring and data

characterization of bridge structures. Its main purpose is to study the different

distribution characteristics of the data for subsequent processing of the data. One of

the bridge quality monitoring devices for monitoring bridge structure data is an

important device for monitoring faults and status, and the reliability of bridge quality

monitoring devices is to be ensured in performing bridge quality monitoring. However,

the reliability of the bridge quality monitoring device cannot be guaranteed due to the

bridge's own structural factors that easily affect the bridge quality monitoring device. In

order to guarantee the reliability of monitoring data, this paper proposes an intelligent

bridge quality monitoring device fault tolerance system automatically based on deep

learning technology, and carries out hardware design and application testing.

Establish the communication module of bridge quality monitoring equipment in the

fault-tolerant system of the upper computer, and carry out the interface interaction

design of bridge quality monitoring equipment fault judgment through reset control and

Internet networking control technology. Set the dual ports as RAM, apply the Internet

of Things networking technology to obtain the PCI protocol of the dual ports, and

obtain the bus control parameter analysis model for fault-tolerant judgment of bridge

quality monitoring equipment according to the PCI protocol control signal. Set the

operating main frequency of the system to 180MHz/MIPS, and carry out the joint

multi-channel control of bridge quality monitoring equipment fault judgment by PCI

protocol. The bridge quality monitoring sensor module with data bus set to LD[16:0]

signal is used for VXI transmission of bridge quality monitoring equipment fault

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tolerance system automatically to build a network monitoring model for bridge quality

monitoring equipment fault tolerance judgment. To reduce the error of fault

information, the register initialization process is carried out, and finally, the monitoring

information of bridge quality monitoring equipment fault automatic fault tolerance

system sends data to the main control computer through the bus.

4. RESULTS AND DISCUSSION

4.1. ANALYSIS OF TEST RESULTS

A part of the data from the China Construction Project Database was used as the

experimental data set for the experimental testing of the bridge quality monitoring

device fault automatic fault tolerance system designed based on deep learning. The

output curve of fault feature monitoring of the bridge quality monitoring device is

shown in Figure 1.

Figure 1. Monitoring output amplitude change curve

As can be seen from Figure 1, the deep learning technique-based test method for

bridge quality monitoring equipment fault monitoring has a high level of sample fusion.

In addition, compared with the traditional test method without troubleshooting, the

highest output value of the deep learning technique test is 28.64% higher in the first

125S and 69.97% higher in the second 250S, which greatly improves the fault

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tolerance level of the bridge quality monitoring equipment and the reliability of the

measured data. The fault tolerance level of the bridge quality monitoring equipment is

greatly improved and the reliability of the measured data is also greatly enhanced. On

this basis, the bridge quality monitoring equipment fault judgment was implemented,

and the fault tolerance convergence level evaluation results were obtained as shown

in Figure 2.

Figure 2. Monitoring convergence level curve

From the convergence curve comparison, it is known that the convergence curve of

the method designed in this paper for bridge quality monitoring equipment fault

monitoring is smooth and of high quality, with small overall changes and reliable data

measured by side feedback. In contrast, with the data obtained from the traditional

method test, the data reliability cannot be guaranteed due to the failure of the

monitoring equipment and automatic error reporting is not excluded. Although the

highest convergence level and the lowest convergence level of the test method based

on deep learning technology are smaller than the traditional test method by about

0.01-0.02, the overall level change rate is only 2.1% per 25 iterative steps, the fault

reliability is large, the judgment ability is high, and the overall fault tolerance

convergence is high.

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4.2. DATA VALIDITY ANALYSIS

After excluding the bridge quality monitoring equipment fault monitoring data, the

validity analysis of the measured monitoring data is performed. The prediction results

of decision-tree, random-forest, SVM, DNN, DBN and other machine learning

classification methods were compared. For the same dataset collected, the DBN

model can improve the accuracy by 14.27% compared with the decision tree. The

experiments prove that the deep learning model has stronger data analysis capability

and is more suitable for validity analysis of bridge quality monitoring data obtained

from monitoring.

Although deep learning models such as DNN and DBN have a great improvement

for data analysis accuracy, but because there are more parameters in the two models

and the initial values of the parameters are set randomly, it is easy to lead to local

optimum during the training process of deep learning, which affects the training results

of network models and reduces the testing accuracy. For this reason, a firefly swarm

optimization algorithm, combined with artificial GSO, is used to further optimize the

DBN model. Since GSO has a strong ability to solve local optimization problems, its

objective function can be a loss function, and the initial parameters of the model are

optimized through GSO to improve the model's applicability and test accuracy. the

DBN-GSO model represents a random initialization of parameters to the original DBN-

R model, and the results show that the data accuracy obtained from testing through

the DBN-GSO optimized model is higher than that of the initial model by The accuracy

is improved by 19.19% compared with the initial model, indicating that the GSO

optimization algorithm can further optimize the DBN model and improve the training

test accuracy.

4.3. STRUCTURAL SAFETY ANALYSIS

Based on the above analysis, it is clear that the optimized DBN algorithm has

improved the model prediction accuracy, so this subsection further analyzes the

structural parameters affecting bridge safety by the optimized DBN algorithm. The

bridge construction process is fixed by the connection between the piers, the bridge

body and the bridge deck, and its structural integrity is an important factor affecting

the stability of the bridge. This paper focuses on the structural analysis of bridges,

including the bearing of bridges under overload conditions of use and force majeure

factors. These factors include mainly pedestrian and vehicle loads (lateral and

longitudinal), ambient temperature, typhoons and seismic natural disasters, and the

input layer data of the influencing factors under certain criteria are imported into DL,

as shown in Table 1.

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Table 1. Input layer parameter characteristics

For the input parameters, the DL model is calculated iteratively to obtain the output

results as shown in Table 4. The output layer results represent the structural

influences on the safety and stability of the bridge, highlighting the most jointed areas.

This is because of the large and complex structure of the bridge and the high degree

of correlation between the internal structures. Relying solely on the engineer's

experience to make judgments about safety issues at design time would slow down

the project schedule on the one hand, and there is no guarantee that the experience

is accurate and that any safety-threatening issues in the bridge design must be

eliminated. The structure obtained from the DL model can help engineers find the key

aspects of the construction problem and provide guidance for the bridge design. Table

2 lists the force problems during the use of the bridge. The main sources of force are

load and wind, and the parts that are subject to the greatest axial force, bending

moment, and shear can be obtained from the input results. The offset is then the

stability problem of the bridge in the presence of stresses. The same analysis was

carried out for different parts to get the sensitive areas affecting the main girders, piers

and towers.

No. Load Type

Load design limit value

Base value setting

basis Minimal value Maximum value

1Vehicle load /

（kN·m-2）

Consider the

overload situation 0 1.3

2Crowd Load /

（kN·m-2）

Number of people in

large events 0 2.8

3Temperature /

（℃）

Meteorological

statistics temperature

maximum value

-15 40

4Windward load

（m·s-1）Category 10 typhoon 0 27.6

5Upwind load

（m·s-1）Category 10 typhoon 0 27.9

6Transverse vehicle

load /.kN

Consider the

overload situation 0 45000

7Longitudinal vehicle

load /kN

Consider the

overload situation 0 18000

8 Seismic load /g 8 magnitude

earthquake 0 0.24

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Table 2. Output layer parameters and characteristics

5. CONCLUSION

This paper is based on the definition and role of both deep learning technology and

the BIM model, and discusses the significance of both in the quality management of

bridge design and construction stage, systematically analyzes the guidance

significance of the BIM model in bridge design and construction, describes the

application process of deep learning technology and its role in monitoring

experimental test in bridge quality monitoring equipment failure, and obtains the

following conclusions.

1.

The application of the BIM model in bridge engineering design and

construction mainly through the construction of models, the development of

construction strategies, statistical engineering volume and fault monitoring and

material statistics during construction, compared to the traditional two-

dimensional CAD drawings BIM model has more aspects of bridge engineering

design and construction, improving the efficiency of about 35%.

2.

Deep learning techniques applied in bridge quality management monitoring

can improve the reliability of measurement data and analysis efficiency. The

bridge quality monitoring equipment fault monitoring and troubleshooting

system designed in this paper can improve up to 69.97% in 250S iteration time

compared with the data obtained from the traditional method testing, while the

No.

Type

Major Categories Minor Categories position

1

Inner Strength

Axial force

Tower main beam

2 Tower Root Section

3

Bending moment

Bridge pier main girders

4Tower Headquarters

(Yokohama direction)

5

Shear force

Bridge pier main girders

6Tower root (longitudinal

bridge direction)

7

Offset degree

Main beam Middle of main beam

8 Bridge pier Left side of the bridge pier

9Bridge Tower Tower top (cross-bridge

direction)

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level value of the convergence curve is high, with a change rate of only 2.1%

per 25 iterative steps.

3.

The validity analysis of the measured data after the bridge quality monitoring

equipment fault monitoring and elimination, the DNN model and DBN model in

the deep learning technique can improve the accuracy of data analysis by

12.51% and 14.26%, respectively, and the DBN-GSO model combined with the

GSO optimization algorithm can also avoid the local optimization results and

improve the training accuracy by 19.19% compared with the original model.

The optimized model is further analyzed for load-stress analysis and safety

issues of force majeure factors for bridge structures. Based on the imported

parameters, the specific structural parameters affecting the bridge quality and

safety were obtained by iterative learning.

REFERENCES

(1)

Hu, Y., & Chen, Y. (2022). Coupling of Urban Economic Development and

Transportation System: An Urban Agglomeration Case. Sustainability, 14.

(2)

Li, B., Gao, S., Liang, Y., et al. (2020). Estimation of Regional Economic

Development Indicator from Transportation Network Analytics. Scientific Reports,

10(1).

(3) Wang, X., Wang, X., Dong, Y., et al. (2020). A Novel Construction Technology for

Self-Anchored Suspension Bridge Considering Safety and Sustainability

Performance. Sustainability, 12.

(4)

Hu, J., Carvel, R., & Usmani, A. (2021). Bridge fires in the 21st century: A

literature review. Fire Safety Journal, 126, 103487.

(5)

Guo, X., Zhang, C., & Chen, Z. Q. (2020). Dynamic performance and damage

evaluation of a scoured double-pylon cable-stayed bridge under ship impact.

Engineering Structures, 216(3), 110772.

(6) Wang, X., Wang, H., Sun, Y., Liu, Y., Liu, Y., & Liang, P., et al. (2022). Fault-

tolerant interval inversion for accelerated bridge construction based on

geometric nonlinear redundancy of cable system. Automation in Construction.

(7)

Wu, Y., & Lu, P. (2022). Comparative analysis and evaluation of bridge

construction risk with multiple intelligent algorithms. Mathematical Problems in

Engineering, 2022.

(8)

Farre-Checa, J., Komarizadehasl, S., Ma, H., Lozano-Galant, J. A., & Turmo, J.

(2022). Direct simulation of the tensioning process of cable-stayed bridge

cantilever construction. Automation in Construction.

(9)

Yu, S., Xu, Z., Su, Z., et al. (2021). Two Flexible Vision-based Methods for

Remote Deflection Monitoring of a Long-span Bridge. Measurement, 2021(1),

109658.

(10)

Zhou, J., Li, T., Ye, X., et al. (2020). Safety assessment of widened bridges

considering uneven multi-lane traffic load modeling: a case study in China.

Journal of Bridge Engineering, 25(9), 1-12.

https://doi.org/10.17993/3ctic.2023.122.137-154

3C TIC. Cuadernos de desarrollo aplicados a las TIC. ISSN: 2254-6529

Ed.43 | Iss.12 | N.2 April - June 2023

152

(11) Yang, Y., Ng, S. T., Dao, J., et al. (2021). BIM-GIS-DCEs Enabled Vulnerability

Assessment of Interdependent Infrastructures – A Case of Stormwater Drainage-

Building-Road Transport Nexus in Urban Flooding. Automation in Construction,

125(4).

(12) Montiel-Santiago, F. J., Hermoso-Orzáez, M. J., Terrados-Cepeda, J., et al.

(2020). Sustainability and Energy Efficiency: BIM 6D. Study of the BIM

Methodology Applied to Hospital Buildings. Value of Interior Lighting and

Daylight in Energy Simulation. Sustainability.

(13) Zhang, J., Zhu, W., Wu, X., et al. (2021). Traffic State Detection Based on

Multidimensional Data Fusion System of Internet of Things. Wireless

Communications and Mobile Computing, 2021(1), 1-12.

(14) Zhang, Hongzhi. (2021). Regression function model in risk management of bank

resource allocation. Applied Mathematics and Nonlinear Sciences, doi:10.2478/

AMNS.2021.1.00093.

(15) Gao, Y., Li, H., Xiong, G., & Song, H. (2023). AIoT-informed digital twin

communication for bridge maintenance. Automation in Construction.

(16) Phares, B., Lee, Y. S., Hosteng, T. K., & Nelson, J. (2021). Investigation of

grouted coupler connection details for accelerated bridge construction.

Transportation Research Record, 2675(8), 562-569.

(17) Ugural, M. N., & Burgan, H. I. (2021). Project performance evaluation using EVA

technique: Kotay Bridge construction project on Kayto River in Afghanistan.

Tehnicki Vjesnik, (1).

(18) Clarke-Sather, A. R., McConnell, J. R., & Masoud, E. (2021). Application of lean

engineering to bridge inspection. Journal of Bridge Engineering, 26(2),

04020120.

(19) Wang, H., Ranade, R., & Okumus, P. (2023). Calibrating a physics-based

corrosion model with field-based bridge condition data. Journal of Bridge

Engineering.

(20) Chen, B., Liu, Q., Chen, H., et al. (2021). Multiobjective optimization of building

energy consumption based on BIM-DB and LSSVM-NSGA-II. Journal of Cleaner

Production, 294(5-6), 126153.

(21) Utkucu, D., & Szer, H. (2020). Interoperability and data exchange within BIM

platform to evaluate building energy performance and indoor comfort.

Automation in Construction, 116, 103225.

(22) Caterino, N., Nuzzo, I., Ianniello, A., et al. (2021). A BIM-based decision-making

framework for optimal seismic retrofit of existing buildings. Engineering

Structures, 242(3), 112544.

(23) Xu, B., & Yuan, X. (2022). A Novel Method of BP Neural Network Based Green

Building Design—The Case of Hotel Buildings in Hot Summer and Cold Winter

Region of China. Sustainability, 14.

(24) Wang, S., Zeng, X., Zhou, T., et al. (2020). Image super resolution via nonlocal

and second-order feature fusion network. Journal of Electronic Imaging, 29(2), 1.

(25) Pan, Y., & Zhang, L. (2020). BIM log mining: Exploring design productivity

characteristics. Automation in Construction, 109(Jan.), 102997.1-102997.14.

https://doi.org/10.17993/3ctic.2023.122.137-154

3C TIC. Cuadernos de desarrollo aplicados a las TIC. ISSN: 2254-6529

Ed.43 | Iss.12 | N.2 April - June 2023

153

(26)

Forgues, D. (2021). Bim Machine Learning and Design Rules to Improve the

Assembly Time in Steel Construction Projects. Sustainability, 14.

(27) Edirisinghe, R., Pablo, Z., Anumba, C., et al. (2021). An Actor–Network Approach

to Developing a Life Cycle BIM Maturity Model (LCBMM). Sustainability, 13.

(28) Liang, X. (2019). Image-based postヾ

isaster inspection of reinforced concrete

bridge systems using deep learning with Bayesian optimization. Computer-Aided

Civil and Infrastructure Engineering, 34(5), 415-430.

(29)

Abbas, T., Kavrakov, I., Morgenthal, G., et al. (2020). Prediction of aeroelastic

response of bridge decks using artificial neural networks. Computers &

Structures, 231, 106198.

(30) Oz, A., Kb, A., Skb, B., et al. (2020). Real-Time Detection of Cracks on Concrete

Bridge Decks Using Deep Learning in the Frequency Domain. Engineering.

(31)

Ma, R., Zhang, Z., Dong, Y., et al. (2020). Deep Learning Based Vehicle

Detection and Classification Methodology Using Strain Sensors under Bridge

Deck. Sensors, 20(18).

(32)

Dinh, K., Gucunski, N., Duong, T. H. (2018). An algorithm for automatic

localization and detection of rebars from GPR data of concrete bridge decks.

Automation in Construction, 89(MAY), 292–298.

(33)

In-Ho, K., Haemin, J., Seung-Chan, B., et al. (2018). Application of Crack

Identification Techniques for an Aging Concrete Bridge Inspection Using an

Unmanned Aerial Vehicle. Sensors, 18(6), 1881.

(34)

Yang, J. X., Yang, F., Zhang, L., et al. (2021). Bridge health anomaly detection

using deep support vector data description. Neurocomputing, 444.

https://doi.org/10.17993/3ctic.2023.122.137-154

3C TIC. Cuadernos de desarrollo aplicados a las TIC. ISSN: 2254-6529

Ed.43 | Iss.12 | N.2 April - June 2023

154