Dear editor：
Hello! The article " Construction Project Risk Prediction Model Based on EW-FAHP and
One Dimensional Convolution Neural Network" has been revised in strict accordance
with the opinions of the review experts. The specific changes are as follows, please
review.
Reviewer #1:
Comments on Revision1：
- Introduction should be clearly stated research questions and targets first. Then
answer several questions: Why is the topic important (or why do you study on it)?
What are the research questions? What are your contributions? Why is to propose this
particular methods? The last two questions are answered in some parts in the Introduction
section. But, the answer is not presented in a proper way. You should provide more
information in this regard.
- Need to highlight the novelty of study in the introduction.
- I suggest authors to clearly summarize what specific advantages brings your approach.
Enrich your Introduction section with more explanation: Why do you present this approach?
Why you use Entropy method for criteria weighting and not the other objective methods
like CRITIC or FANMA method? Why do we FAHP since can use only Entropy method for
determining criteria weights?
Modification instructions ：
Introduction
With the continuous development of science and technology, the complexity of construction
projects continues to increase, the construction period continues to grow, and there
are many uncertain factors[1], in order to reduce the probability of risk occurrence
and effectively avoid potential risks to the entire project, it is necessary to predict
the risks of the construction projects. In reference [2], the subway project construction
risk management method is based on Bayesian network. In reference[3], the Fault Tree
Analysis (FTA) method is combined with Bayesian network, and a Bayesian network-based
shale gas well blowout risk analysis method is proposed. However, Bayesian networks
are based on prior probabilities. In many cases, prior probabilities depend on assumptions,
which will largely lead to poor prediction results. Combine with AHP theory, use rough
set to analyze project risk group decision to realize attribute reduction, and combine
with analytic hierarchy process to realize quantitative research and analysis of project
risk. Literature [4] uses AHP to evaluate solid waste treatment methods in Libya,
but it is difficult for AHP to check and adjust the consistency of the judgment matrix.
Literature [5] proposed the use of fuzzy analytic hierarchy process and rough analytic
hierarchy process to evaluate traffic accessibility method. Literature[6] proposed
the use of IT2FS-DEMATEL to eliminate less important indicators, combines the IT2FS-AHP
method to sort the final indicators, and establishes a multi-index decision-making
model. But literature [5] and literature [6] involve risk prediction, rough set and
IT2FS-DEMATEL may have a greater impact on the final prediction results after removing
redundant attributes.
With the development of artificial intelligence and big data, neural network has attracted
more and more researchers’ attention. Because neural network has a strong nonlinear
fitting ability and has a good effect on mapping nonlinear relations, relevant scholars
have combined neural network with engineering project risk research in recent years
and achieved certain results [6][7].Literature [8] proposed a railway construction
risk assessment algorithm based on BP neural network. The expert scoring method was
used to establish initial sample data, and the BP neural network prediction model
was used to learn and predict the samples to get the risk score of each construction
project. However, BP neural network has some problems, such as not the best approximation
of continuous function and long training time. Literature [9] proposed a project risk
evaluation algorithm based on PCA (principal component analysis)-RBF neural network
on the basis of BP model, which improved the shortcomings of BP neural network that
it is difficult to obtain the optimal network, but the RBF neural network the center
of the hidden basis function is selected in the input sample set, which in many cases
can hardly reflect the real input-output relationship of the system. In order to solve
the problems of the above-mentioned neural network, literature [10] proposed a method
for predicting the risk of underground engineering rockburst based on ANN and ABC.
(the artificial neural network (ANN) and artificial bee colony (ABC), in order to
further improve the prediction accuracy, the paper uses the artificial bee colony
algorithm to optimize the artificial bee colony algorithm, but the artificial bee
colony algorithm has weak search ability and relatively slow convergence speed.
As construction projects become large-scale and risk factors continue to increase,
traditional risk predictions mostly use regular event analysis, correlation analysis
and other methods to analyze key indicators and detailed records, which rely heavily
on manual extraction by professional workers. The current risk assessment of construction
projects adopts a single expert scoring method, entropy weight method, analytic hierarchy
process or fuzzy analytic hierarchy process, which does not fully combine multiple
evaluation methods, resulting in incomplete de-tailed factors affecting project risks,
and lack of objectivity and accuracy of inspection and evaluation results. At present,
Analytic Hierarchy Process (AHP) and its derivative methods are still the most widely
used and most effective risk assessment in the complex large systems. Among them,
the Fuzzy Analytic Hierarchy Process (FAHP), which integrates fuzzy theories, improves
the weight determination problem of AHP, and its practicality and simplicity have
been applied more and more widely [11]. In order to improve the closeness between
the weight of evaluation index and reality, this paper adopts the entropy weight-fuzzy
analytic hierarchy process (EW-FAHP method) to determine the weight. The risk prediction
method based on traditional neural network risk prediction requires too many weights,
which reduces the accuracy of project prediction to a certain extent [12]. The current
risk assessment of construction projects uses a single CNN network with different
convolution kernels to perform convolution operations on the input data, thereby obtaining
global features of the data, and then down-sampling the extracted features through
pooling operations, reducing the amount of calculations. At the same time, it can
also suppress the overfitting of the model to a certain extent.
Therefore, this paper uses entropy weight method and fuzzy analytic hierarchy process
to evaluate the construction period and cost index system of the construction project,
proposes a construction project risk prediction model based on EW-FAHP and 1D-CNN,
identifies the existing risks of the construction project through reference analytical
method and constructs risk evaluation index system. Using Entropy Weight (EW) and
Fuzzy Analytic Hierarchy Process (FAHP), the risk weight of each risk evaluation index
is determined by combining subjective and objective evaluation methods. One dimensional
convolution neural network model is constructed to train and learn the risk weight
of construction project. The duration risk and cost risk of construction project are
selected as the output unit of convolution neural network. The aver-age absolute error
between the predicted value and the actual value of duration risk and cost risk is
analyzed to realize the risk prediction of construction project.
The prediction results of the risk prediction model proposed in this paper show that
the method has strong practicability in the early stage of the project and in the
construction process. Compared with other commonly used forecasting algorithms, the
forecasting accuracy has been significantly improved, which is of greater reference
value for project decision-makers.
Comments on Revision 2
- Literature review should be revised. Remove lumped references. All references cited
in the text should be explained and discussed in the text. Remove some old references
published before 2017-2018. Also, literature review should be presented in a better
way. You should discuss application of various MCDM tools different fields. You should
update your literature review with a papers published in last two-three years, and
remove old references. I suggest authors to read and cite below interesting references:
Petrovic, I., & Kankaras, M. (2020). A hybridized IT2FS-DEMATEL-AHP-TOPSIS multicriteria
decision making approach: Case study of selection and evaluation of criteria for determination
of air traffic control radar position. Decision Making: Applications in Management
and Engineering, 3(1), 146-164.;
Badi, I., Abdulshahed, A., Shetwan, A., & Eltayeb, W. (2019). Evaluation of solid
waste treatment methods in Libya by using the analytic hierarchy process. Decision
Making: Applications in Management and Engineering, 2(2), 19-35.
Stanković, M., Gladović, P., & Popović, V. (2019). Determining the importance of the
criteria of traffic accessibility using fuzzy AHP and rough AHP method. Decision Making:
Applications in Management and Engineering, 2(1), 86-104.
Modification instructions ：
Thanks for the comments of the review experts, I have put the relevant references
into the article for discussion, namely, reference [4][5][6]
In reference [2], the subway project construction risk management method is based
on Bayesian network. In reference [3], the fault tree analysis(FTA) method is combined
with Bayesian network, and the shale gas well blowout risk analysis method based on
Bayesian network is proposed. However, Bayesian network is based on prior probability,
which often depends on assumptions, which leads to poor prediction effect to a large
extent. Literature [4] uses AHP to evaluate solid waste treatment methods in Libya,
but it is difficult for AHP to check and adjust the consistency of the judgment matrix.
Literature [5] proposed the use of fuzzy analytic hierarchy process and rough analytic
hierarchy process to evaluate traffic accessibility method, and literature [6] proposed
the use of IT2FS-DEMATEL. The method eliminates less important indicators, combines
the IT2FS-AHP method to sort the final indicators, and establishes a multi-index decision-making
model. However, in terms of reference [5] and reference [6] concerning risk prediction,
rough set and IT2FS-DEMATEL may have a great influence on the final prediction results
after eliminating redundant attributes.
Comments on Revision 3
- Add flowchart of proposed methodology and follow that flowchart steps in case study.
Modification instructions ：
Fig. 5 is the flow chart of risk prediction model of construction engineering project
based on EW-FAHP and 1D-CNN. Firstly, the model identifies the project risk based
on the literature analysis method, and then constructs the project risk evaluation
index. The relative importance of each risk index is given by using 0.1~0.9 scale
method, and the initial matrix is established. Use the initial matrix data to calculate
the FAH weights and entropy weights of risk indicators at all levels, and then obtain
the comprehensive weight based on EW-FAHP method. The comprehensive weight is input
into the 1D-CNN model for learning, and the corresponding model parameters are changed
to make the output prediction result converge. Finally, the corresponding prediction
results are obtained.
Fig.5. The flow chart of risk prediction model of construction engineering project
based on EW-FAHP and 1D-CNN
Comments on Revision 4
- Case study should be better organized. The calculations should be deeply presented
and follow the methodology presented in methodology section. Add more deep calculations
in case study section.
Modification instructions：
This paper presents the process of determining the weight of each index of public
relations risk, and the weights of other risk factors can be determined sequentially.
The relevant data comes from the data of a Sichuan group's entire construction project
in a community in Chengdu. First, use the expert scoring method to fill in the proportional
scale table for the public relations risk factors of the construction project, and
the following matrix can be obtained:
According to formula (4), it can be concluded that:
From formula (8), we can get: FAHP calculation weight is: W={0.1341,0.2694,0.1648,0.2378,0.1939},
from formula (6), the weight of reciprocal matrix is : , from formula (7), the sum
row normalized weight initial vector of public risk is obtained: , from equation
(8), we can get: .
The entropy weight method (EW) calculates the weight as: W*={0.809,0.0394,0.0112,0.0501,0.0258},
based on the above derivation, the EW-FAHP weight of public management risk is G={0.0066,0.0394,0.0112,0.0501,0.0258}.
The FAHP weight and entropy weight (EW) of the remaining secondary indicators can
be obtained in turn.
The calculation method of the first-level index weight is the same as that of the
second-level index. The initial matrix of first-level indicators is：
The comprehensive weight of EW-FAHP is calculated as: G={0.32299, 0.1126, 0.2568,
0.1878, 0.0745, 0.0443}. Table 1 shows the construction period and cost information
of a Sichuan group in a community in Chengdu. According to equation (1) and equation
(2), the construction period risk and cost risk value are obtained. Table 2 shows
the weights of relevant indicators at all levels.
Comments on Revision 4
- Add sensitivity analysis and validation of the results.
Modification instructions：
Sensitivity analysis based on input variable disturbance
In order to find out the sensitive factors which have an important impact on the construction
period and cost indicators from many uncertain factors, and analyze and measure the
degree of influence and sensitivity on the construction period and cost indicators,
this article further analyzes the impact of six sensitive factors, including economy,
environment, technology, society, public relations, and natural risks, on construction
period and cost indicators. For the neural network model, the only things need to
know are the input variable data and output data, without the need of prior knowledge.
It can carry on training and learning to the training data set, with a lot of simple
artificial neuron nonlinear relationship between the simulated data, and can adaptively
adjust the connection weight between neurons, so as to establish a network structure
that can better reflect the true situation of the data. Therefore, this paper inputs
the single sensitive factor into the prediction model under the premise that the other
five sensitive factors remain unchanged by +5% and -5%. Sort the sensitivity according
to the change size of the output index. Table 7 shows the prediction results when
a single sensitive factor changes +5% and -5%.
Table 7 Evaluation data of construction project risk factors
Risk indicator Related data Construction period
risk Coefficient of sensitivity
Cost of risk Coefficient of sensitivity
Economy
+5% 0.3391 0.0576 0.1404 0.0238 0.0596
Economy
-5% 0.3068 0.0530 0.0192
Environment
+5% 0.1193 0.08094 0.0067 0.02844 0.0088
Environment -5% 0.1079 0.08086 0.02836
Techniques
+5% 0.2696 0.0689 0.1284 0.0282 0.0442
Techniques
-5% 0.2440 0.0657 0.0250
Social risks
+5% 0.1972 0.0735 0.0906 0.0227 0.0255
Social risks
-5% 0.1784 0.0753 0.245
Public relations
+5% 0.0782 0.0748 0.1274 0.0274 0.0433
Public relations
-5% 0.0708 0.0758 0.0284
Natural risk
+5% 0.0452 0.0660 0.0636 0.0243 0.0016
Natural risk
-5% 0.0409 0.0658 0.0241
As can be seen from Table 7, the sensitivity coefficients of the six sensitivity factors
are 0.1404, 0.0067, 0.1284, 0.0906, 0.1274 and 0.0636 respectively for the risk of
construction period, and 0.0596, 0.0088, 0.0442, 0.0255, 0.0433 and 0.0016 respectively
for the risk of cost. Based on the above analysis, it can be seen that for project
duration risk and cost risk, the order of sensitivity is economic risk, technical
risk, public relations risk, social risk, environmental risk and natural risk.
Therefore, comprehensive prediction results and analysis of sensitivity factors show
that, for a Sichuan group's construction project in a community in Chengdu, efforts
should be made to resolve economic risks, technical risks, and public relations risks
before the project starts, so as to avoid project delays and economic losses.
Comments on Revision 5
- The problem on which this present method is applied has significant social and managerial
implications. How the method can address those implications need to be included.
- Conclusion- Add future scope. Also, how the proposed method can be applicable to
other real life problems need to be mentioned. Add limitations of proposed model.
Do not use bullets or numerations in this section.
Modification instructions：
The prediction model proposed in this paper can be extended in the future research,
the prediction results of the risk prediction model proposed in this paper demonstrate
that the method has strong practicability in the early stage of the project and in
the construction process. Compared with other commonly used prediction algorithms,
the prediction accuracy is significantly improved, which has great reference value
for project decision-makers.
Conclusion
This paper proposes a project risk prediction model based on EW-FAHP and one-dimensional
convolutional neural network. By selecting the risk evaluation index of construction
project, the corresponding risk value is established by combining the EW-FAHP, and
then the risk value of construction project is input into the established one-dimensional
convolution neural network model for training and learning. The construction project
duration risk and cost risk are selected as the output units of the neural network
risk prediction. The experimental results show that:
(1) The EW-FAHP weight calculation method proposed in this paper realizes the combination
of subjective and objective weighting method and reduces the influence of human factors
on the weight. At the same time, the one-dimensional convolutional neural network
has strong reliability and high accuracy in the pre-diction of construction project
duration risk and cost risk, which can meet the engineering application conditions.
(2) In the case of a certain number of samples, during the neural network training
process, the risk Loss value continues to decrease with the number of iterations,
and the network converges. This verifies that the risk prediction model has high stability
and can provide a reasonable basis for project managers’ early decision-making and
can effectively reduce risks. It can provide a reasonable basis for project managers’
early decision-making and can effectively reduce risks. In addition, due to the difficulty
of obtaining sample data, if more relevant data of construction projects can be obtained
by combining relevant big data, the prediction accuracy of the 1D-CNN risk prediction
model will be further improved, and the prediction results will be more convincing.
In the practical engineering application, it aims at different types of risk prediction
requirements, such as investment risk, traffic risk, coal mine safety and disease
risks, etc. After sorting and collecting relevant data, EW-FAHP or other combination
of subjective and objective weight determination methods are applied to determine
the comprehensive weight of risks affecting the prediction results. Then, the comprehensive
weight data is input into the 1D-CNN prediction model for learning and prediction,
and the prediction results are also of great reference significance.
Reviewer #2:
Comments on Revision1：
- Indicate what is new that is presented in the paper.
Modification instructions：
Abstract
In order to solve the problem of low accuracy of traditional construction project
risk prediction, a project risk prediction model based on EW-FAHP and 1D-CNN(One Dimensional
Convolution Neural Network) is proposed. Firstly, the risk evaluation index value
of construction project is selected by literature analysis method, and the comprehensive
weight of risk index is obtained by combining entropy weight method (EW) and fuzzy
analytic hierarchy process (FAHP). The risk weight is input into the 1D-CNN model
for training and learning, and the pre-diction values of construction period risk
and cost risk are output to realize the risk prediction. The experimental results
show that the average absolute error of the construction period risk and cost risk
of the risk prediction model proposed in this paper is below 0.1%, which can meet
the risk prediction of construction projects with high accuracy.
Introduction
With the continuous development of science and technology, the complexity of construction
projects continues to increase, the construction period continues to grow, and there
are many uncertain factors[1], in order to reduce the probability of risk occurrence
and effectively avoid potential risks to the entire project, it is necessary to predict
the risks of the construction projects.
In reference [2], the subway project construction risk management method is based
on Bayesian network. In reference[3], the Fault Tree Analysis (FTA) method is combined
with Bayesian network, and a Bayesian network-based shale gas well blowout risk analysis
method is proposed. However, Bayesian networks are based on prior probabilities. In
many cases, prior probabilities depend on assumptions, which will largely lead to
poor prediction results. Combine with AHP theory, use rough set to analyze project
risk group decision to realize attribute reduction, and combine with analytic hierarchy
process to realize quantitative research and analysis of project risk. Literature
[4] uses AHP to evaluate solid waste treatment methods in Libya, but it is difficult
for AHP to check and adjust the consistency of the judgment matrix. Literature [5]
proposed the use of fuzzy analytic hierarchy process and rough analytic hierarchy
process to evaluate traffic accessibility method. Literature[6] proposed the use of
IT2FS-DEMATEL to eliminate less important indicators, combines the IT2FS-AHP method
to sort the final indicators, and establishes a multi-index decision-making model.
But literature [5] and literature [6] involve risk prediction, rough set and IT2FS-DEMATEL
may have a greater impact on the final prediction results after removing redundant
attributes.
With the development of artificial intelligence and big data, neural network has attracted
more and more researchers’ attention. Because neural network has a strong nonlinear
fitting ability and has a good effect on mapping nonlinear relations, relevant scholars
have combined neural network with engineering project risk research in recent years
and achieved certain results [6][7]. Literature [8] proposed a railway construction
risk assessment algorithm based on BP neural network. The expert scoring method was
used to establish initial sample data, and the BP neural network prediction model
was used to learn and predict the samples to get the risk score of each construction
project. However, BP neural network has some problems, such as not the best approximation
of continuous function and long training time. Literature [9] proposed a project risk
evaluation algorithm based on PCA (principal component analysis)-RBF neural network
on the basis of BP model, which improved the shortcomings of BP neural network that
it is difficult to obtain the optimal network, but the RBF neural network the center
of the hidden basis function is selected in the input sample set, which in many cases
can hardly reflect the real input-output relationship of the system. In order to solve
the problems of the above-mentioned neural network, literature [10] proposed a method
for predicting the risk of underground engineering rockburst based on ANN and ABC.
(the artificial neural network (ANN) and artificial bee colony (ABC), in order to
further improve the prediction accuracy, the paper uses the artificial bee colony
algorithm to optimize the artificial bee colony algorithm, but the artificial bee
colony algorithm has weak search ability and relatively slow convergence speed.As
construction projects become large-scale and risk factors continue to increase, traditional
risk predictions mostly use regular event analysis, correlation analysis and other
methods to analyze key indicators and detailed records, which rely heavily on manual
extraction by professional workers. The current risk assessment of construction projects
adopts a single expert scoring method, entropy weight method, analytic hierarchy process
or fuzzy analytic hierarchy process, which does not fully combine multiple evaluation
methods, resulting in incomplete de-tailed factors affecting project risks, and lack
of objectivity and accuracy of inspection and evaluation results. At present, Analytic
Hierarchy Process (AHP) and its derivative methods are still the most widely used
and most effective risk assessment in the complex large systems. Among them, the Fuzzy
Analytic Hierarchy Process (FAHP), which integrates fuzzy theories, improves the weight
determination problem of AHP, and its practicality and simplicity have been applied
more and more widely [11]. In order to improve the closeness between the weight of
evaluation index and reality, this paper adopts the entropy weight-fuzzy analytic
hierarchy process (EW-FAHP method) to determine the weight. The risk prediction method
based on traditional neural network risk prediction requires too many weights, which
reduces the accuracy of project prediction to a certain extent [12]. The current risk
assessment of construction projects uses a single CNN network with different convolution
kernels to perform convolution operations on the input data, thereby obtaining global
features of the data, and then down-sampling the extracted features through pooling
operations, reducing the amount of calculations. At the same time, it can also suppress
the overfitting of the model to a certain extent.
Therefore, this paper uses entropy weight method and fuzzy analytic hierarchy process
to evaluate the construction period and cost index system of the construction project,
proposes a construction project risk prediction model based on EW-FAHP and 1D-CNN,
identifies the existing risks of the construction project through reference analytical
method and constructs risk evaluation index system. Using Entropy Weight (EW) and
Fuzzy Analytic Hierarchy Process (FAHP), the risk weight of each risk evaluation index
is determined by combining subjective and objective evaluation methods. One dimensional
convolution neural network model is constructed to train and learn the risk weight
of construction project. The duration risk and cost risk of construction project are
selected as the output unit of convolution neural network. The aver-age absolute error
between the predicted value and the actual value of duration risk and cost risk is
analyzed to realize the risk prediction of construction project.
The prediction results of the risk prediction model proposed in this paper show that
the method has strong practicability in the early stage of the project and in the
construction process. Compared with other commonly used forecasting algorithms, the
forecasting accuracy has been significantly improved, which is of greater reference
value for project decision-makers.
Comments on Revision2：
- Show the entire model in phases and steps in one figure.
Modification instructions：
Fig. 5 is the flow chart of risk prediction model of construction engineering project
based on EW-FAHP and 1D-CNN. Firstly, the model identifies the project risk based
on the literature analysis method, and then constructs the project risk evaluation
index. The relative importance of each risk index is given by using 0.1~0.9 scale
method, and the initial matrix is established. Use the initial matrix data to calculate
the FAH weights and entropy weights of risk indicators at all levels, and then obtain
the comprehensive weight based on EW-FAHP method. The comprehensive weight is input
into the 1D-CNN model for learning, and the corresponding model parameters are changed
to make the output prediction result converge. Finally, the corresponding prediction
results are obtained.
Fig.5. The flow chart of risk prediction model of construction engineering project
based on EW-FAHP and 1D-CNN
Comments on Revision3：
- One unit should be a description of the FAHP method. Analyze different approaches
in fuzzyfication of AHP methods (standard fuzzy numbers, interval fuzzy numbers, Z
numbers ...)
Modification instructions：
Through the research and analysis of interval fuzzy numbers, it is found that the
existing processing method is to directly model and predict the two boundary points.
Doing so often leads to a failure to describe the overall development trend of the
sequence and the results predicted by the model are prone to be confused, etc., which
results in the failure of predictions. Z-number is a more anthropomorphic way of representing
un-certain information. The existing references on Z-number research, especially theoretical
research, is still in its infancy. A prominent feature of mainstream research in existing
theoretical aspects is that the amount of calculation is relatively large, not easy
to be understood, and is not conducive to actual engineering applications, particularly
inconvenient to handle emergency management that requires high time complexity.
Although the fuzzy analytic hierarchy process overcomes the defects of analytic hierarchy
process in the process of calculation, its evaluation results are still calculated
based on the experts' scores, which makes the evaluation results inevitably mixed
with some experts' personal views. The entropy weight method, by contrast, is mainly
based on actual data, without combining some special cases, and the evaluation results
are relatively objective. Therefore, I want to obtain the subjective weight and objective
weight of each factor through fuzzy analytic hierarchy process and entropy weight
method respectively, and then combine the two to obtain their comprehensive weight.
In practical application, the combination of subjective and objective methods are
not the same, mainly including mean value method, product method, gray correlation
method, etc. However, these combination methods only use the subjective and objective
weights of the lowest-level indicators for a relatively simple combination, ignoring
the effective integration of the intermediate steps of the two methods. This will
cause the calculated weight to be different from the true component in the evaluation
process, which deviates from the actual situation. Therefore, a new combination method
is adopted, which not only considers the combination of the underlying index weights,
but also considers the organic integration of the intermediate processes.
Comments on Revision4：
- The paper lacks sensitivity analysis.
Modification instructions：
Sensitivity analysis based on input variable disturbance
In order to find out the sensitive factors which have an important impact on the construction
period and cost indicators from many uncertain factors, and analyze and measure the
degree of influence and sensitivity on the construction period and cost indicators,
this article further analyzes the impact of six sensitive factors, including economy,
environment, technology, society, public relations, and natural risks, on construction
period and cost indicators. For the neural network model, the only things need to
know are the input variable data and output data, without the need of prior knowledge.
It can carry on training and learning to the training data set, with a lot of simple
artificial neuron nonlinear relationship between the simulated data, and can adaptively
adjust the connection weight between neurons, so as to establish a network structure
that can better reflect the true situation of the data. Therefore, this paper inputs
the single sensitive factor into the prediction model under the premise that the other
five sensitive factors remain unchanged by +5% and -5%. Sort the sensitivity according
to the change size of the output index. Table 7 shows the prediction results when
a single sensitive factor changes +5% and -5%.
Table 7 Evaluation data of construction project risk factors
Risk indicator Related data Construction period
risk Coefficient of sensitivity
Cost of risk Coefficient of sensitivity
Economy
+5% 0.3391 0.0576 0.1404 0.0238 0.0596
Economy
-5% 0.3068 0.0530 0.0192
Environment
+5% 0.1193 0.08094 0.0067 0.02844 0.0088
Environment -5% 0.1079 0.08086 0.02836
Techniques
+5% 0.2696 0.0689 0.1284 0.0282 0.0442
Techniques
-5% 0.2440 0.0657 0.0250
Social risks
+5% 0.1972 0.0735 0.0906 0.0227 0.0255
Social risks
-5% 0.1784 0.0753 0.245
Public relations
+5% 0.0782 0.0748 0.1274 0.0274 0.0433
Public relations
-5% 0.0708 0.0758 0.0284
Natural risk
+5% 0.0452 0.0660 0.0636 0.0243 0.0016
Natural risk
-5% 0.0409 0.0658 0.0241
As can be seen from Table 7, the sensitivity coefficients of the six sensitivity factors
are 0.1404, 0.0067, 0.1284, 0.0906, 0.1274 and 0.0636 respectively for the risk of
construction period, and 0.0596, 0.0088, 0.0442, 0.0255, 0.0433 and 0.0016 respectively
for the risk of cost. Based on the above analysis, it can be seen that for project
duration risk and cost risk, the order of sensitivity is economic risk, technical
risk, public relations risk, social risk, environmental risk and natural risk.
Therefore, comprehensive prediction results and analysis of sensitivity factors show
that, for a Sichuan group's construction project in a community in Chengdu, efforts
should be made to resolve economic risks, technical risks, and public relations risks
before the project starts, so as to avoid project delays and economic losses.
Comments on Revision4：
- Reference analysis is not at a satisfactory level. Most of the reference are of
older date.
Modification instructions：
The references in this paper have been updated.
References
1. Wang QK, Wang YH. ANP- based Research on the Strategic Risk Assessment for Multi
Project Management in Prefabricated Buildings [J]. JOURNAL OF WUHAN UNIVERSITY OF
TECHNOLOGY, 2018,40(4):76-79
2. Xiao QD, Zhao ZN, Liu LC. Research on Construction Risk Management of Subway Project
Based on Bayesian Network [J]. Journal of Xinyang Normal University(Natural Science
Edition) https://kns.cnki.net/kcms/detail/41.1107.N.20201207.1007.002.html.
3. Chen K, Chen X，Wei X et al. Bayesian network-based risk analysis on the b low out
of the shale gas wells [J]. Journal of Safety and Environment，2019,19(6):226-241.
4. Stanković, M., Gladović, P., & Popović, V. Determining the importance of the criteria
of traffic accessibility using fuzzy AHP and rough AHP method[J]. Decision Making:
Applications in Management and Engineering, 2019,2(1): 86-104
5. Badi, I., Abdulshahed, A., Shetwan, A., & Eltayeb, W. Evaluation of solid waste
treatment methods in Libya by using the analytic hierarchy process[J]. Decision Making:
Applications in Management and Engineering, 2019,2(2):19-35.
6. Petrovic, I., & Kankaras, M. A hybridized IT2FS-DEMATEL-AHP-TOPSIS multicriteria
decision making approach: Case study of selection and evaluation of criteria for determination
of air traffic control radar position[J]. Decision Making: Applications in Management
and Engineering,2020, 3(1), 146-164.
7. Ehsan E, Nima K, Ezutah U O, el at. Applying fuzzy multi-objective linear programming
to a project management decision with nonlinear fuzzy membership functions[J]. Neural
Computing and Applications,2017,(28)8:2193-2206.
8. Jin J, Li ZH , Zhu L, et al. Application of BP Neural Network in Risk Evaluation
of Railway Construction[J]. JOURNAL OF RAILWAY ENGINEERING SOCIETY 2019,3:103-109
9. Lu XQ, Huang YJ, Wang X. Intelligent Evaluation Model Based on PCA-RBF Neural Network
Applied to Risk Assessment of PPP Projects[J].2017,14:59-63.
10. Zhou, J., Koopialipoor, M., Li, E. et al. Prediction of rockburst risk in underground
projects developing a neuro-bee intelligent system[J]. Bull Eng Geol Environ 2020,79,
4265–4279.
11. Gao, Cl., Li, Sc., Wang, J. et al. The Risk Assessment of Tunnels Based on Grey
Correlation and Entropy Weight Method[J]. Geotech Geol Eng, 2018,36, 1621–1631.
12. Khorram, S. Correction to: A novel approach for ports’ container terminals’ risk
management based on formal safety assessment: FAHP-entropy measure—VIKOR model[J].
Nat Hazards 103, 1709 (2020).
13. Guo YH, Shi YC, Xu YJ. Evaluation of Bridge Construction Quality based on Improved
FAHP Evaluation Method[J]. Journal of Civil Engineering and Management, 2017,34(1):44-48.
14. Zhou FY, Jin LP, Dong Jun. A Review of Convolutional Neural Networks [J].Journal
of Computers，2017,40(6):1229-1251.
15. Morgunova E.P. Investment Project Risk Identification and Evaluation[C]. In: Solovev
D. (eds) Smart Technologies and Innovations in Design for Control of Technological
Processes and Objects: Economy and Production. FarEastСon 2018. Smart Innovation,
Systems and Technologies, vol 138. Springer, Cham.
16. Sanghera P. Project Risk Management. In: CAPM® in Depth[M]. 2019, Apress, Berkeley,
CA.
17. Schatteman D, Herroelen W, STIJN V D V, et al. Methodology for integrated risk
management and proactive scheduling of construction projects[J]. Faculty of Economics
and Applied Economics, doi:10.2139/ssrn.950903
18. Mulgan, G. Artificial intelligence and collective intelligence: the emergence
of a new field [J]. AI & Soc 2018,33, 631–632.
19. Anysz, H., Buczkowski, B. The association analysis for risk evaluation of significant
delay occurrence in the completion date of construction project [J]. Int. J. Environ.
Sci. Technol. 2019,16, 5369–5374.
20. Yu XJ, Peng YY. The Application and Challenges of Artificial Intelligence in the
Field of Financial Risk Management[J]. Southern Finance, 2017,9:70-74.
21. Wu Q, Gao SH, Zhou T. Comprehensive Evaluation of Construction Project Schedule
Control [J]. Journal of Xi'an University of Science and Technology, 2011, 4(31):412-419.
22. Li, S.C., Wu, J. A multi-factor comprehensive risk assessment method of karst
tunnels and its engineering application [J]. Bull Eng Geol Environ 2019,78, 1761–1776.
23. Zhong YW. Study on Schedule Risk of Project Group based on Rough Set Theory [D].
Chengdu: Xihua University，2018.
24. Li L, Li SY, He WJ, et al. Emergency Capability Evaluation of Construction Projects
based on EM and FAHP [J]. Journal of Xi'an University of Science and Technology, 2020,4(40):572-579.
25. Yu C, Luo B, Wang DG, et al. Evaluation of Cultivated Land Consolidation Potential
Based on Improved FAHP-Entropy Weighting Method[J]. China Agricultural Resources and
Regional Planning, 2020,41(6):15-24.
26. Liu W, Dong WQ. Research on Risk Assessment Method of Drainage Pipe Network Based
on AHP-Entropy Method Combination Weighting[J].Journal of Safety and Environment,
https://doi.org/10.13637/j.issn.1009-6094.2019.1400
27. Niu XX, Suen C Y. A Novel Hybrid CNN–SVM Classifier for Recognizing Handwritten
Digits[J]. Pattern Recognition, 2012,45(4):1318–1325.
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