# Analysis of Stadium Operation Risk Warning Model Based on Deep Confidence Neural Network Algorithm

Authors: Zijun Dang; Shunshun Liu; Tong Li; Liang Gao
Journal: Computational Intelligence and Neuroscience
 (2021)

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## Abstract

In this paper, a deep confidence neural network algorithm is used to design and deeply analyze the risk warning model for stadium operation. Many factors, such as video shooting angle, background brightness, diversity of features, and the relationship between human behaviors, make feature attribute-based behavior detection a focus of researchers’ attention. To address these factors, researchers have proposed a method to extract human behavior skeleton and optical flow feature information from videos. The key of the deep confidence neural network-based recognition method is the extraction of the human skeleton, which extracts the skeleton sequence of human behavior from a surveillance video, where each frame of the skeleton contains 18 joints of the human skeleton and the confidence value estimated for each frame of the skeleton, and builds a deep confidence neural network model to classify the dangerous behavior based on the obtained skeleton feature information combined with the time vector in the skeleton sequence and determine the danger level of the behavior by setting the corresponding threshold value. The deep confidence neural network uses different feature information compared with the spatiotemporal graph convolutional network. The deep confidence neural network establishes the deep confidence neural network model based on the human optical flow information, combined with the temporal relational inference of video frames. The key of the temporal relationship network-based recognition method is to extract some frames from the video in an orderly or random way into the temporal relationship network. In this paper, we use several methods for comparison experiments, and the results show that the recognition method based on skeleton and optical flow features is significantly better than the algorithm of manual feature extraction.


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## Body

## 1. Introduction

People’s fitness and health needs and the requirements of implementing the national fitness strategy for all are interdependent. The implementation of a health strategy requires a shift from being disease-centered to health-centered [1]. To achieve this goal, we must actively promote the in-depth integration of national fitness and national health, so that health knowledge and active participation in physical activity become the general quality and ability of the people and give full play to the unique advantages of sports in health promotion, disease prevention, and rehabilitation [2]. The rapid development of modern information technology is bringing new opportunities for the development of sports, using the Internet, Internet of Things, big data, cloud computing, artificial intelligence, and other modern information technologies combined with national fitness, to create an integrated online and offline public service system for national fitness and provide more convenient, efficient and accurate sports services for the community residents. The material and cultural aspects have greatly improved, which provides the necessary material basis and broad developmental space for the comprehensive development of national fitness [3]. The number of people who regularly participate in physical exercise, level of residents’ sports consumption, and healthy life expectancy of the population increase year by year substantially.Over the years, there are still many deep-rooted problems and contradictions in the development of grassroots national fitness. The problem of unbalanced and inadequate development between urban and rural areas is relatively prominent, and there is still a big gap between continuously improving the basic public sports service system and expanding the supply of quality sports resources and the expectations and requirements of the people [4]. The awareness on innovation in fitness for all is still not strong, with insufficient motivation and lack of service capacity. The total number of public sports facilities is insufficient and the utilization rate is not high, and the problem of “where to go for fitness” is still difficult to solve. However, in general, the construction of the community wisdom sports system is not fast, and the actual application is still in the exploration pilot stage [5]. For Anhui Province, although a large number of modern information technologies are combined in the community wisdom sports, there is still a big gap between the requirements of the wisdom city and wisdom community on the platform of the community and the information construction goal of coupling modern information technology in the community sports management services. Together with inherent problems such as imperfect institutional mechanism, lagging construction of venues and facilities, and shortage of talents, the overall level of community sports services is in urgent need of improvement [6].In surveillance systems in different settings, the detection and identification of dangerous behavior need to be done quickly to intervene and resolve. In addition to poor video quality, dangerous behaviors can be present in public places at any time of the day. Therefore, a fast and accurate recognition algorithm is needed to detect and identify dangerous behaviors in unused scenarios. Video data can be captured by video surveillance devices, including cameras inside and outside buildings, electronic filming devices for traffic violations, and police law enforcement recorders, among others. As one of the special categories of human behavior recognition, dangerous behavior detection requires special optimization and processing because it is applied to its characteristics. First, places where dangerous behavior detection is applied are usually schools, prisons, hospitals, and other places with strict public order. The high real-time nature of dangerous behavior detection requirements, therefore, requires special measures to be taken for the behavior in as short a time as possible without using the scenario, which places higher demands on the complexity of the algorithm. Second, dangerous behaviors are associated with multiple human objects, and their activity patterns are very complex because of the strong interactions between the human objects. It is usually difficult to identify, track, and recognize these behaviors one by one, which is the focus and difficulty of the research.


## 2. Related Work

Wang et al. sorted out the key contract terms and possible risks that need to be focused on according to many EPC contracts, as well as some problems that project managers often encounter during the project progress, to provide a basis for managers to perform contract risk management [7]. Martini et al. proposed a case-based evaluation method. This method makes the quantitative analysis of risk assessment more accurate and objective, and the model has achieved good results with five cases. The bidding cost of EPC projects is high, accounting for about 5% of the total contract cost, and good risk management in the bidding stage can help the general contractor save and reduce the pressure of risk management in the later contract negotiation and performance stages [8]. Zhang et al. pointed out that the study of organizational structure mainly refers to the aspects of the composition of the organization and the relationship between the members of the organization [9]. The study of clubs is mainly about studying the morphological types and club business models. Sports associations usually refer to community sports as life-circle sports, sports activities that are performed through people's interconnection in their daily lives within a common geographical area and the common use of living environment and facilities, thereby achieving the purpose of generating and consolidating community feelings [10]. In addition, there are two main types of sports organizations that exist in society: administrative and public service. From the experience of community sports construction in developed countries, the funding for community sports mainly comes from government input, but families, enterprises, lotteries, and other channels are also gradually becoming important sources [11–14].Human behavior recognition plays an important role in human-person and human-object interaction. Human behavior is related to a person’s identity, personality, and psychological state, so it is difficult to extract relevant information directly [15, 16]. Recognition of human behavior is one of the main topics of research in the field of computer vision and machine learning science. The recognition of human behavior and the applications of this research (including video surveillance systems, human-computer interaction, and robotics for human behavior recognition) require multiple systems to support each other. Although much of the current research recognizes human behavior based on frame sequences in the video, recognizing human behavior from still images remains a daunting task [17]. Currently, most of the research in human behavior recognition is related to facial expression recognition or pose estimation techniques. Dorie et al. summarized methods for recognizing human behavior from still images and classified them into two main categories based on the level of abstraction and the feature type approach used [18]. Huan et al. first introduced a spatiotemporal map representing human behavior. Then, a clustering algorithm is used to construct the input video as a spatiotemporal map [19]. Due to the nonrigidity of the constructed 3D spatiotemporal maps and the inherent differences between the temporal and spatial dimensions, traditional 3D feature analysis methods cannot be applied to spatiotemporal maps. Therefore, Poisson's equation is used to enhance the significance of the local spatiotemporal and derive its directional features. The input video is segmented into spatiotemporal maps using clustering [20–23]. These segmented regions are then comparatively classified using a classifier and compared with traditional contour classification methods [24–26]. Unlike previous contour-based methods, the proposed shape-based classification method does not require removing the background or specifying a particular scene.In the first layer, a clustering feature vector based on joints is introduced to form the initial class by clustering them according to the joints with the highest relevance. Since different sequences of the same action are grouped into different clusters, it facilitates the resolution of high intraclass variance. In the second layer, only the relevant joints in a specific cluster are used for feature extraction, thus enhancing the effectiveness of the features and reducing the computational effort. In this paper, we propose a target detection algorithm applied to high-resolution aerial images. We first describe the motivation of the algorithm, which is interested in using a segmentation-based rather than a regression-based approach to better predict the rotated rectangular boxes of targets in aerial images and using a training data enhancement method based on subimage block synthesis to deal with the need for cropping and data imbalance in aerial images. Then, the structure and flow of the network are described, and each part of the algorithm is described in detail. Finally, the algorithm is fully compared and analyzed in the experimental section to prove the effectiveness of the algorithm.


## 3. Deep Confidence Neural Network Algorithm for Stadium Operation Risk Warning Model Design

### 3.1. Improved Deep Confidence Neural Network Algorithm

The non-maximum suppression (NMS) sorts all the results according to their confidence scores from the largest to smallest and takes out the result with the highest confidence. Then, the next detection result with the highest confidence level is taken, and the previously mentioned operation is continued until all detection results are processed. The positive and negative samples are judged based on the size of the IoU between the recommended boxes and the true value. If the EU is greater than 0.5, it is considered as a positive sample, and if it is less than 0.5, it is considered as a negative sample. However, an IoU greater than 0.5 only means that there will be many target frames of lower quality. If the IoU threshold is simply increased, it will lead to a significant reduction in the number of positive samples, which does not improve the detection performance. To improve the quality of target frames, Cascade Mask RCNN (region-based CNN) proposes a cascaded network based on the Mask RCNN algorithm to continuously improve the quality of target frames. As shown in Figure1, the network F for extracting features, the RPN (Faster RCNN) network, and the network of the first step (branch M1 for instance segmentation network, branch C1 for classification network, and branch B1 for target frame regression network) are the same as the Mask RCNN algorithm. Therefore, the Cascade Mask RCNN is equivalent to cascading two additional output networks (M2, C2, B2, M3, C3, and B3) on top of the Mask RCNN algorithm. The recommended frames in the latter step of the Cascade Mask RCNN use the results of the regression optimization of the previous step, so the quality of the target frames is gradually improved.Figure 1
Improved deep confidence neural network framework.Under a given sample size, the confidence level (same confidence level) and accuracy are mutually restricted. The higher the confidence level, the lower the accuracy; on the contrary, the higher the accuracy, the lower the confidence level. The confidence level determines the size of the confidence interval. If the confidence level is very high (e.g., close to 1), the confidence interval will be very wide. At this time, no matter how the sampling is done, the interval estimate obtained will almost always contain the true value to be estimated. However, because the range is too large, the estimated interval loses its meaning (the accuracy is too low).On an image, the convolution operation uses some fixed-size convolution kernels to scan the input image. As shown in Figure1, a weight matrix with the same size extracts a pixel matrix of centroids and neighborhood points of each pixel, and the coordinates of the convolution output values are obtained by combining the feature vector inner product space of the pixels with the parameter vectors of the convolution kernel. When extending the convolution operation of 2D images to a graph of arbitrary structure, the neighborhood and weight matrices of arbitrary nodes can be defined. In the process of constructing a deep confidence neural network, the temporal vector of the graph is composed of the same key points of two adjacent frames. Therefore, the confidence neural network can be extended to a deep confidence neural network. The average coordinates of all joints in the skeleton are taken as its center of gravity, as shown in (1). This strategy is inspired by the fact that the motion of human body parts can be broadly categorized as concentric and eccentric motions:(1)GriWti=3,ifgi<gj,1,ifgi=gj,5,ifgi>gj.The deep confidence neural network first builds a confidence neural network on a single frame of an image. On a single frame at timeT, there will exist N nodes tV, skeleton contours EST=Wtitti|t=T2,i,j∈H. According to the definition of the convolution operation on 2D natural images or feature maps, both the node set V and the edge set E can be considered as 2D meshes. The output feature map of the convolution operation is based on 2D. Based on the deep confidence neural network, a behavioral model is built for the spatial time in the skeleton sequence. By detecting the behavior of the input video, a spatiotemporal map is constructed on the skeleton sequence. Multilevel spatiotemporal graph convolution (ST-GCN) is performed on each frame to generate higher-level feature maps. Then, the standard SoftMax classifier classifies them into the corresponding hazard classes. The whole model is trained by backpropagation.The in vivo concatenation of picture nodes in a single frame is represented by the adjacency matrixA, and the concatenation between video frames is represented by the unit matrix I. In the single-frame case, the output of ST-GCN is shown in the following, according to the spatial configuration partitioning strategy:(2)fout=λ1/2−1A−I+λ−1/2+1finW.The input feature map is represented as aC,V,T dimensional tensor in the space-time condition. The convolution of the graph is achieved by performing a standard 2D convolution and λ1/2A−I multiplying the resulting tensor with a second-dimensional normalized adjacency matrix. However, this adjacency matrix has been decomposed into multiple matrices Aj, where A−I=∑jAj. Therefore, let A0=I and A1=A, and (2) can be transformed into(3)fout=limn⟶∞∑j=1nλj1/2−1Ajλj−1/2+1finWj.We train the network to predict the IoU between the target frame and the matching annotation to improve the confidence level to satisfy the property that high-quality target frames have higher confidence scores. Like other network branches in the detector, the IoU estimation network branch consists of three fully connected layers. The middle fully connected layer has a dimension of 1024, and the final output dimension of the IoU estimation network is 1. When we obtain the recommended frames from the RPN (or from the previous stage in the cascade structure), we extract the features from these frames by RoI Align. Then, the IoU between the recommendation frame and the labeled truth frame is calculated and considered as the truth value of the IoU estimation network. Then, we feed the features into the IoU estimation network branch and train it with the IoU truth values. However, as we can tell, the IoU between most of the recommendation frames and the true value frames is low. This uneven distribution of training data will lead to the IoU estimation network being dominated by a large number of low IoU samples. In this chapter, an IOU focal loss function is proposed for training the IoU estimation network. This loss function is specified as(4)lossiouApred,Bgt=12β1+βBgtτApred+Bgt2.Both the foreground/background classification network branch and the category classification network branch consist of three fully connected layers. The middle fully connected layer has a dimension of 1024. The final output dimension of the foreground/background classification branch is 1, and the output dimension of the category classification branch is 80, i.e., the same number of categories of interest as in the MS COCO dataset. To train foreground/background classification, we sample positive and negative samples of recommendations in the ratio of 1 : 3 based on the IoU of the recommendation frame and the truth frame. The features of the recommendation box region are then extracted using RoI Align and fed into the foreground/background classification network branch. We train it with BCELoss (binary cross-entropy loss function):(5)Hx,yloss=−ylnδx−1−yln1+δx.For category classification web training practice, we use only positive samples as training samples. Since the IoU thresholds of the training samples for category classification do not need to be as strict as those for foreground/background classification, we resample the positive samples with a slightly smaller IoU threshold. Although their IoU is slightly lower, they still contain enough features for classification. This results in more training samples with greater robustness. We use the cross-entropy loss function to train this network branch:(6)Hxloss=lnexpxclasslimn⟶∞∑j=1nexpxj2.The confidence level can be improved in three ways. Separating the classification task frees each category of interest from competing with a background region that is approximately 240 times larger than the training sample. The estimated IoU can help meet the desired confidence properties; i.e., higher quality target frames (higher IoU) should have higher confidence scores (Figure2). The cross-execution makes the confidence-related network branches feature the corresponding target frames rather than the target frames before regression. With these three improvement strategies, we can obtain three confidence scores, namely, the estimated IoU, the foreground/background confidence score, and the category confidence score. Then, we use the geometric mean of the three confidence scores as the final improved confidence score, which is defined as follows:(7)F=ffg⋅fcatmaxAprep,0.054,ffg=δMfg/bg,fcat=SMfg/cat,Figure 2
Weighted score.wheref (fg) is the confidence score of the foreground probability for each target frame, f (cat) is the confidence score of each category probability, Mfg/bg is the foreground/background classification branch, Mfg/cat is the prediction of the category classification network branch, δMfg/bg is the Sigmoid function, and SMfg/cat is the SoftMax function. The Aprep is the predicted IoU, whose range of values is not explicitly limited, so we set its minimum value to 0.01 by the maximum function [20]. By these methods, the improved confidence is more reliable than the original method and achieves better results in both target detection and instance segmentation.The reasonableness of the selection of the research object is directly related to the correctness of the research conclusions. The selection of the object should not only be feasible (data can be collected) but also consider the typicality and representativeness of the field in which it is located. The questionnaire was based on a 5-point Likert scale, and the respondents were asked to judge the degree of influence of the indexes on the risk of the runners during the race according to the given evaluation scores and criteria and select the corresponding scores.


### 3.2. Risk Warning Design for Stadium Operation

The bidding cost of Engineering Procurement Construction (EPC) projects can generally reach about 5% of the total project cost, and the bidding cost of EPC projects for stadiums may reach 8%–10% of the total project cost. This is mainly determined by the design difficulty of the stadium project. The design difficulty of stadium projects is not only reflected in the high requirements of appearance design, but also the high requirements of safety and practicality. First, the number of spectators in stadiums is usually tens of thousands of people, so there are strict requirements for fire prevention and firefighting, admission of facilities, weight-bearing of stands, evacuation of personnel, etc. Second, the view of the audience should be considered when setting up spectator seats, and the requirements of the opening ceremony should be considered when setting up the bureau and torch stand. Finally, stadium projects not only need to meet the design standards of civil construction but also have their design standards for various competition venues and competition appliances and high precision requirements, which eventually need to be accepted by the project country's department or even the organizing committee of the upcoming competition. Therefore, if the general contractor fails to meet the design requirements for any reason, they will face large losses.Large stadiums are usually local landmarks, so the owner usually requires the general contractor's design to be both innovative and recognizable, and such a design usually leads to excessive construction difficulties and the need to choose new construction techniques, which will make the general contractor unable to effectively identify and respond to possible problems in construction due to lack of experience. In addition, at present, general contractors generally lack experience in the construction of overseas stadiums, and poorly considered design and construction organization plans occur, and owners are often slow to make decisions due to lack of experience in the construction of large-scale projects, as shown in Figure3.Figure 3
The main risk management process.Contract risk refers to the risk directly related to contract bidding, negotiation, conclusion, and performance, which mainly consists of bidding risk, contract condition risk, contract price risk, and contract management risk. The bidding risk mainly comes from the general contractor's inadequate preparation and lack of seriousness for the bidding work. Generally, the bidding cost of overseas EPC projects can reach about 5% of the total contract cost, and the bidding cost of sports stadium projects is higher due to their difficult design and high design requirements. If the general contractor is found to be unable to perform the contract, the general contractor will face huge losses and compensation. Although the owner and the general contractor share the same goal, they represent different interests. When the contract is not perfect and clear, both parties will inevitably interpret it in their favor, which will easily lead to disputes. The owner will use their advantageous position in drafting the contract to add a large number of clauses unfavorable to the contractor, which will destroy the balance of the contract and even affect the recovery of claims and retainage, bringing risks to the general contractor. Contract price risk is mainly generated by the EPC mode of fixed lump sum contract. The FIDIC Silver Book only provides for the adjustment of the contract price under force majeure, law change, the owner's exercise of the right to change, etc. If no additional price adjustment clause is added, the general contractor will bear greater risk. The contract management risk is mainly generated by the imperfect contract management system, the insufficient level of contract management personnel, and the lack of attention to contract management by project managers. The insufficient level of contract management will lead to confusion and loss of contract files, which will affect the normal conduct of the contract process and affect the claim work at a later stage.Legal risk is caused by the lack of a sound legal system in the host country, inconsistent law enforcement standards, and government interference in the judiciary, which can disrupt the policy performance of the contract. While some countries have strict labor access and environmental protection systems or have strict requirements for overtime and legal holidays, other labor systems may also increase the performance costs of the general contractor. Social risks are mainly caused by differences in culture, language, work habits, and local social security. Different cultural and religious backgrounds and work habits may easily lead to friction and conflicts between Chinese and foreign employees and may even cause local xenophobia or excessive reactions from local governments. An unstable social security will not only damage employees’ personal and property safety and reduce their sense of security but may also lead to theft of project equipment and materials damage. Natural risks are designed to cover the severe climate, geological factors, hydrological conditions, and sanitary conditions during the contract life cycle, as shown in Figure4.Figure 4
Construction model of the gymnasium service system.


## 3.1. Improved Deep Confidence Neural Network Algorithm

The non-maximum suppression (NMS) sorts all the results according to their confidence scores from the largest to smallest and takes out the result with the highest confidence. Then, the next detection result with the highest confidence level is taken, and the previously mentioned operation is continued until all detection results are processed. The positive and negative samples are judged based on the size of the IoU between the recommended boxes and the true value. If the EU is greater than 0.5, it is considered as a positive sample, and if it is less than 0.5, it is considered as a negative sample. However, an IoU greater than 0.5 only means that there will be many target frames of lower quality. If the IoU threshold is simply increased, it will lead to a significant reduction in the number of positive samples, which does not improve the detection performance. To improve the quality of target frames, Cascade Mask RCNN (region-based CNN) proposes a cascaded network based on the Mask RCNN algorithm to continuously improve the quality of target frames. As shown in Figure1, the network F for extracting features, the RPN (Faster RCNN) network, and the network of the first step (branch M1 for instance segmentation network, branch C1 for classification network, and branch B1 for target frame regression network) are the same as the Mask RCNN algorithm. Therefore, the Cascade Mask RCNN is equivalent to cascading two additional output networks (M2, C2, B2, M3, C3, and B3) on top of the Mask RCNN algorithm. The recommended frames in the latter step of the Cascade Mask RCNN use the results of the regression optimization of the previous step, so the quality of the target frames is gradually improved.Figure 1
Improved deep confidence neural network framework.Under a given sample size, the confidence level (same confidence level) and accuracy are mutually restricted. The higher the confidence level, the lower the accuracy; on the contrary, the higher the accuracy, the lower the confidence level. The confidence level determines the size of the confidence interval. If the confidence level is very high (e.g., close to 1), the confidence interval will be very wide. At this time, no matter how the sampling is done, the interval estimate obtained will almost always contain the true value to be estimated. However, because the range is too large, the estimated interval loses its meaning (the accuracy is too low).On an image, the convolution operation uses some fixed-size convolution kernels to scan the input image. As shown in Figure1, a weight matrix with the same size extracts a pixel matrix of centroids and neighborhood points of each pixel, and the coordinates of the convolution output values are obtained by combining the feature vector inner product space of the pixels with the parameter vectors of the convolution kernel. When extending the convolution operation of 2D images to a graph of arbitrary structure, the neighborhood and weight matrices of arbitrary nodes can be defined. In the process of constructing a deep confidence neural network, the temporal vector of the graph is composed of the same key points of two adjacent frames. Therefore, the confidence neural network can be extended to a deep confidence neural network. The average coordinates of all joints in the skeleton are taken as its center of gravity, as shown in (1). This strategy is inspired by the fact that the motion of human body parts can be broadly categorized as concentric and eccentric motions:(1)GriWti=3,ifgi<gj,1,ifgi=gj,5,ifgi>gj.The deep confidence neural network first builds a confidence neural network on a single frame of an image. On a single frame at timeT, there will exist N nodes tV, skeleton contours EST=Wtitti|t=T2,i,j∈H. According to the definition of the convolution operation on 2D natural images or feature maps, both the node set V and the edge set E can be considered as 2D meshes. The output feature map of the convolution operation is based on 2D. Based on the deep confidence neural network, a behavioral model is built for the spatial time in the skeleton sequence. By detecting the behavior of the input video, a spatiotemporal map is constructed on the skeleton sequence. Multilevel spatiotemporal graph convolution (ST-GCN) is performed on each frame to generate higher-level feature maps. Then, the standard SoftMax classifier classifies them into the corresponding hazard classes. The whole model is trained by backpropagation.The in vivo concatenation of picture nodes in a single frame is represented by the adjacency matrixA, and the concatenation between video frames is represented by the unit matrix I. In the single-frame case, the output of ST-GCN is shown in the following, according to the spatial configuration partitioning strategy:(2)fout=λ1/2−1A−I+λ−1/2+1finW.The input feature map is represented as aC,V,T dimensional tensor in the space-time condition. The convolution of the graph is achieved by performing a standard 2D convolution and λ1/2A−I multiplying the resulting tensor with a second-dimensional normalized adjacency matrix. However, this adjacency matrix has been decomposed into multiple matrices Aj, where A−I=∑jAj. Therefore, let A0=I and A1=A, and (2) can be transformed into(3)fout=limn⟶∞∑j=1nλj1/2−1Ajλj−1/2+1finWj.We train the network to predict the IoU between the target frame and the matching annotation to improve the confidence level to satisfy the property that high-quality target frames have higher confidence scores. Like other network branches in the detector, the IoU estimation network branch consists of three fully connected layers. The middle fully connected layer has a dimension of 1024, and the final output dimension of the IoU estimation network is 1. When we obtain the recommended frames from the RPN (or from the previous stage in the cascade structure), we extract the features from these frames by RoI Align. Then, the IoU between the recommendation frame and the labeled truth frame is calculated and considered as the truth value of the IoU estimation network. Then, we feed the features into the IoU estimation network branch and train it with the IoU truth values. However, as we can tell, the IoU between most of the recommendation frames and the true value frames is low. This uneven distribution of training data will lead to the IoU estimation network being dominated by a large number of low IoU samples. In this chapter, an IOU focal loss function is proposed for training the IoU estimation network. This loss function is specified as(4)lossiouApred,Bgt=12β1+βBgtτApred+Bgt2.Both the foreground/background classification network branch and the category classification network branch consist of three fully connected layers. The middle fully connected layer has a dimension of 1024. The final output dimension of the foreground/background classification branch is 1, and the output dimension of the category classification branch is 80, i.e., the same number of categories of interest as in the MS COCO dataset. To train foreground/background classification, we sample positive and negative samples of recommendations in the ratio of 1 : 3 based on the IoU of the recommendation frame and the truth frame. The features of the recommendation box region are then extracted using RoI Align and fed into the foreground/background classification network branch. We train it with BCELoss (binary cross-entropy loss function):(5)Hx,yloss=−ylnδx−1−yln1+δx.For category classification web training practice, we use only positive samples as training samples. Since the IoU thresholds of the training samples for category classification do not need to be as strict as those for foreground/background classification, we resample the positive samples with a slightly smaller IoU threshold. Although their IoU is slightly lower, they still contain enough features for classification. This results in more training samples with greater robustness. We use the cross-entropy loss function to train this network branch:(6)Hxloss=lnexpxclasslimn⟶∞∑j=1nexpxj2.The confidence level can be improved in three ways. Separating the classification task frees each category of interest from competing with a background region that is approximately 240 times larger than the training sample. The estimated IoU can help meet the desired confidence properties; i.e., higher quality target frames (higher IoU) should have higher confidence scores (Figure2). The cross-execution makes the confidence-related network branches feature the corresponding target frames rather than the target frames before regression. With these three improvement strategies, we can obtain three confidence scores, namely, the estimated IoU, the foreground/background confidence score, and the category confidence score. Then, we use the geometric mean of the three confidence scores as the final improved confidence score, which is defined as follows:(7)F=ffg⋅fcatmaxAprep,0.054,ffg=δMfg/bg,fcat=SMfg/cat,Figure 2
Weighted score.wheref (fg) is the confidence score of the foreground probability for each target frame, f (cat) is the confidence score of each category probability, Mfg/bg is the foreground/background classification branch, Mfg/cat is the prediction of the category classification network branch, δMfg/bg is the Sigmoid function, and SMfg/cat is the SoftMax function. The Aprep is the predicted IoU, whose range of values is not explicitly limited, so we set its minimum value to 0.01 by the maximum function [20]. By these methods, the improved confidence is more reliable than the original method and achieves better results in both target detection and instance segmentation.The reasonableness of the selection of the research object is directly related to the correctness of the research conclusions. The selection of the object should not only be feasible (data can be collected) but also consider the typicality and representativeness of the field in which it is located. The questionnaire was based on a 5-point Likert scale, and the respondents were asked to judge the degree of influence of the indexes on the risk of the runners during the race according to the given evaluation scores and criteria and select the corresponding scores.


## 3.2. Risk Warning Design for Stadium Operation

The bidding cost of Engineering Procurement Construction (EPC) projects can generally reach about 5% of the total project cost, and the bidding cost of EPC projects for stadiums may reach 8%–10% of the total project cost. This is mainly determined by the design difficulty of the stadium project. The design difficulty of stadium projects is not only reflected in the high requirements of appearance design, but also the high requirements of safety and practicality. First, the number of spectators in stadiums is usually tens of thousands of people, so there are strict requirements for fire prevention and firefighting, admission of facilities, weight-bearing of stands, evacuation of personnel, etc. Second, the view of the audience should be considered when setting up spectator seats, and the requirements of the opening ceremony should be considered when setting up the bureau and torch stand. Finally, stadium projects not only need to meet the design standards of civil construction but also have their design standards for various competition venues and competition appliances and high precision requirements, which eventually need to be accepted by the project country's department or even the organizing committee of the upcoming competition. Therefore, if the general contractor fails to meet the design requirements for any reason, they will face large losses.Large stadiums are usually local landmarks, so the owner usually requires the general contractor's design to be both innovative and recognizable, and such a design usually leads to excessive construction difficulties and the need to choose new construction techniques, which will make the general contractor unable to effectively identify and respond to possible problems in construction due to lack of experience. In addition, at present, general contractors generally lack experience in the construction of overseas stadiums, and poorly considered design and construction organization plans occur, and owners are often slow to make decisions due to lack of experience in the construction of large-scale projects, as shown in Figure3.Figure 3
The main risk management process.Contract risk refers to the risk directly related to contract bidding, negotiation, conclusion, and performance, which mainly consists of bidding risk, contract condition risk, contract price risk, and contract management risk. The bidding risk mainly comes from the general contractor's inadequate preparation and lack of seriousness for the bidding work. Generally, the bidding cost of overseas EPC projects can reach about 5% of the total contract cost, and the bidding cost of sports stadium projects is higher due to their difficult design and high design requirements. If the general contractor is found to be unable to perform the contract, the general contractor will face huge losses and compensation. Although the owner and the general contractor share the same goal, they represent different interests. When the contract is not perfect and clear, both parties will inevitably interpret it in their favor, which will easily lead to disputes. The owner will use their advantageous position in drafting the contract to add a large number of clauses unfavorable to the contractor, which will destroy the balance of the contract and even affect the recovery of claims and retainage, bringing risks to the general contractor. Contract price risk is mainly generated by the EPC mode of fixed lump sum contract. The FIDIC Silver Book only provides for the adjustment of the contract price under force majeure, law change, the owner's exercise of the right to change, etc. If no additional price adjustment clause is added, the general contractor will bear greater risk. The contract management risk is mainly generated by the imperfect contract management system, the insufficient level of contract management personnel, and the lack of attention to contract management by project managers. The insufficient level of contract management will lead to confusion and loss of contract files, which will affect the normal conduct of the contract process and affect the claim work at a later stage.Legal risk is caused by the lack of a sound legal system in the host country, inconsistent law enforcement standards, and government interference in the judiciary, which can disrupt the policy performance of the contract. While some countries have strict labor access and environmental protection systems or have strict requirements for overtime and legal holidays, other labor systems may also increase the performance costs of the general contractor. Social risks are mainly caused by differences in culture, language, work habits, and local social security. Different cultural and religious backgrounds and work habits may easily lead to friction and conflicts between Chinese and foreign employees and may even cause local xenophobia or excessive reactions from local governments. An unstable social security will not only damage employees’ personal and property safety and reduce their sense of security but may also lead to theft of project equipment and materials damage. Natural risks are designed to cover the severe climate, geological factors, hydrological conditions, and sanitary conditions during the contract life cycle, as shown in Figure4.Figure 4
Construction model of the gymnasium service system.


## 4. Analysis of Results

### 4.1. Deep Confidence Neural Network Algorithm Model Performance Results

Training directly with the positive and negative sample sets described previously will result in severe data imbalance, as the targets are very sparse and sparsely distributed in the aerial images. Performing data resampling can slightly mitigate this problem but is slightly less efficient. This chapter, therefore, proposes a data enhancement method using synthetic images as an alternative solution that not only achieves a balance between positive and negative samples but also increases the diversity of the training data, resulting in more efficient network training. We randomly place a block of subimages from the positive sample set on top of a random block of subimages from a larger negative sample set at any position to obtain an enhanced new training sample. In more detail, for a positive sample subimage block of a training image whose side length is less than 800 pixels (i.e., 400 or 200 pixels), we randomly select a negative sample subimage block of larger size from that image and then place that positive sample randomly on top of the negative sample subimage block at a random position, thus synthesizing a new training sample with a different background, as shown in Figure5. In this paper, we determine whether the previously mentioned image synthesis data enhancement operation is performed on each positive sample subimage block with random probability p. The positive and negative sample ratios can be further adjusted. In the experiment, the deep confidence neural network algorithm in this paper can get better performance.Figure 5
Comparison of algorithms.As shown in Figure6, the MAP of the deep confidence neural network algorithm without AC and BBC is 73.95, the MAP of the deep confidence neural network algorithm with AC is 74.08, and the MAP of the deep confidence neural network algorithm with BBC is 74.14. When AC and BBC are combined, the MAP of the detection algorithm is 74.15. Thus, the regional connectivity can be improved by 0.13%, the target frame consistency can be improved by 0.19%, and the combination of both can be improved by 0.2%. Both methods improve performance in almost all categories, but this improvement is not very significant. This is because both methods can improve performance by reducing the confidence scores of unreliable rotating target frames, but most rotating target frames are reliable and the confidence scores of these frames do not change, so there is some but not significant performance improvement. Since the impact of both methods is the same, that is, reducing the confidence level of unreliable detection results, the improvement of combining the two methods will be smaller than the sum of the individual improvements of both.Figure 6
Ablation experiments comparing regional connectivity and target frame consistency.To evaluate the effectiveness of the proposed mechanism of ignoring incomplete targets (IPIO) and image synthesis-based training data enhancement (IS), we performed ablation experiments. These results are tested on the DOTA validation set only at a doubled single-scale scaling. It is worth noting that we only trained the network on the DOTA training set. The baseline detection algorithm without IPIO and IS has a MAP of 70.56. When we train with IPIO, the detection algorithm has a MAP of 72.5, which is a 1.94% improvement. We believe that this significant improvement is mainly because the network is not confused by incomplete targets with unclear semantic information during the training process, and therefore, the network can be trained better. When we also use IPIO in the test, MAP improves slightly, from 72.5 to 72.63. This is because the number of incomplete targets in the test is much smaller than the number of complete targets, so the impact is not significant and only a small improvement is observed. When we add the IS mechanism, the MAP can be further improved significantly by 1.52% to 74.15. The IS mechanism is effective because the enhancement of the training data based on image synthesis improves the diversity of the training data and results in fewer false positives in the background region.


### 4.2. Risk Warning Results for Stadium Operations

The risk response system usually consists of an expert consultation system and a general case base. The risk response system is connected to the expert consultation, which enables the efficient and convenient use of the case base and expert consultation, and provides timely risk response strategies for amateur runners or race managers, etc. The system stores routine cases related to marathon risks in the computer system to form a general expert database, and when a risk problem arises, relevant cases can be called up according to the nature, category, and relevance of the event to provide coping strategies and ideas for solving the risk. In addition, when an unconventional risk arises, the experts in the system are called up and timely consultation is completed through the Internet to find risk response strategies and solve risk problems, while the consultation results are saved in the expert database for future use, forming a risk response cycle system.Information openness, timeliness, and reliability are prerequisites for the prevention and disposal of risk events and play a key role in the process of solving and resolving risks. The information release system is mainly to report the nature, scale, and degree of impact of risks to the management promptly, so that the relevant departments can address them promptly and make preliminary judgments based on the factual situation of the risks to take timely and effective countermeasures. In addition, the risk issues and risk treatment results that the marathon may face should be announced to the participants and related personnel promptly to reduce unnecessary panic and public opinion, as shown in Figure7.Figure 7
Risk factor evaluation.Because of the high pressure and high risk of the logistics and transportation of this project, the project team made the following responses: first, purchase all insurance for all goods and vehicles and transfer the risks during transportation to the insurance company; second, plan near the construction site. In the material storage area, some materials in high demand are stored on-site to prevent material shortages due to poor logistics; third, to avoid the risks of low customs clearance efficiency and high taxes and fees in Laos, the project team has strengthened the cooperation with the Lao Prime Minister’s Office. The connection with the government has obtained administrative support from the government, accelerated the efficiency of entry and exit of people and goods, opened a green channel for logistics, and exempted some taxes and fees. The logistics risk results of the stadium project are shown in Figure8.Figure 8
Logistics risk results of stadium projects.The project site lacked municipal facilities, and a new power supply and water supply and drainage facilities were required, which took a long time to provide due to the level of municipal construction in Laos and might affect the start of the project on schedule. The project team communicated with the owner after the inspection and finally reached an agreement that the owner would provide the materials for the facilities and the approval of all procedures, and Y Group would be responsible for the construction. Although this move caused some losses to the general contractor, it ensured the normal start of the project and gained the trust of the owner.


## 4.1. Deep Confidence Neural Network Algorithm Model Performance Results

Training directly with the positive and negative sample sets described previously will result in severe data imbalance, as the targets are very sparse and sparsely distributed in the aerial images. Performing data resampling can slightly mitigate this problem but is slightly less efficient. This chapter, therefore, proposes a data enhancement method using synthetic images as an alternative solution that not only achieves a balance between positive and negative samples but also increases the diversity of the training data, resulting in more efficient network training. We randomly place a block of subimages from the positive sample set on top of a random block of subimages from a larger negative sample set at any position to obtain an enhanced new training sample. In more detail, for a positive sample subimage block of a training image whose side length is less than 800 pixels (i.e., 400 or 200 pixels), we randomly select a negative sample subimage block of larger size from that image and then place that positive sample randomly on top of the negative sample subimage block at a random position, thus synthesizing a new training sample with a different background, as shown in Figure5. In this paper, we determine whether the previously mentioned image synthesis data enhancement operation is performed on each positive sample subimage block with random probability p. The positive and negative sample ratios can be further adjusted. In the experiment, the deep confidence neural network algorithm in this paper can get better performance.Figure 5
Comparison of algorithms.As shown in Figure6, the MAP of the deep confidence neural network algorithm without AC and BBC is 73.95, the MAP of the deep confidence neural network algorithm with AC is 74.08, and the MAP of the deep confidence neural network algorithm with BBC is 74.14. When AC and BBC are combined, the MAP of the detection algorithm is 74.15. Thus, the regional connectivity can be improved by 0.13%, the target frame consistency can be improved by 0.19%, and the combination of both can be improved by 0.2%. Both methods improve performance in almost all categories, but this improvement is not very significant. This is because both methods can improve performance by reducing the confidence scores of unreliable rotating target frames, but most rotating target frames are reliable and the confidence scores of these frames do not change, so there is some but not significant performance improvement. Since the impact of both methods is the same, that is, reducing the confidence level of unreliable detection results, the improvement of combining the two methods will be smaller than the sum of the individual improvements of both.Figure 6
Ablation experiments comparing regional connectivity and target frame consistency.To evaluate the effectiveness of the proposed mechanism of ignoring incomplete targets (IPIO) and image synthesis-based training data enhancement (IS), we performed ablation experiments. These results are tested on the DOTA validation set only at a doubled single-scale scaling. It is worth noting that we only trained the network on the DOTA training set. The baseline detection algorithm without IPIO and IS has a MAP of 70.56. When we train with IPIO, the detection algorithm has a MAP of 72.5, which is a 1.94% improvement. We believe that this significant improvement is mainly because the network is not confused by incomplete targets with unclear semantic information during the training process, and therefore, the network can be trained better. When we also use IPIO in the test, MAP improves slightly, from 72.5 to 72.63. This is because the number of incomplete targets in the test is much smaller than the number of complete targets, so the impact is not significant and only a small improvement is observed. When we add the IS mechanism, the MAP can be further improved significantly by 1.52% to 74.15. The IS mechanism is effective because the enhancement of the training data based on image synthesis improves the diversity of the training data and results in fewer false positives in the background region.


## 4.2. Risk Warning Results for Stadium Operations

The risk response system usually consists of an expert consultation system and a general case base. The risk response system is connected to the expert consultation, which enables the efficient and convenient use of the case base and expert consultation, and provides timely risk response strategies for amateur runners or race managers, etc. The system stores routine cases related to marathon risks in the computer system to form a general expert database, and when a risk problem arises, relevant cases can be called up according to the nature, category, and relevance of the event to provide coping strategies and ideas for solving the risk. In addition, when an unconventional risk arises, the experts in the system are called up and timely consultation is completed through the Internet to find risk response strategies and solve risk problems, while the consultation results are saved in the expert database for future use, forming a risk response cycle system.Information openness, timeliness, and reliability are prerequisites for the prevention and disposal of risk events and play a key role in the process of solving and resolving risks. The information release system is mainly to report the nature, scale, and degree of impact of risks to the management promptly, so that the relevant departments can address them promptly and make preliminary judgments based on the factual situation of the risks to take timely and effective countermeasures. In addition, the risk issues and risk treatment results that the marathon may face should be announced to the participants and related personnel promptly to reduce unnecessary panic and public opinion, as shown in Figure7.Figure 7
Risk factor evaluation.Because of the high pressure and high risk of the logistics and transportation of this project, the project team made the following responses: first, purchase all insurance for all goods and vehicles and transfer the risks during transportation to the insurance company; second, plan near the construction site. In the material storage area, some materials in high demand are stored on-site to prevent material shortages due to poor logistics; third, to avoid the risks of low customs clearance efficiency and high taxes and fees in Laos, the project team has strengthened the cooperation with the Lao Prime Minister’s Office. The connection with the government has obtained administrative support from the government, accelerated the efficiency of entry and exit of people and goods, opened a green channel for logistics, and exempted some taxes and fees. The logistics risk results of the stadium project are shown in Figure8.Figure 8
Logistics risk results of stadium projects.The project site lacked municipal facilities, and a new power supply and water supply and drainage facilities were required, which took a long time to provide due to the level of municipal construction in Laos and might affect the start of the project on schedule. The project team communicated with the owner after the inspection and finally reached an agreement that the owner would provide the materials for the facilities and the approval of all procedures, and Y Group would be responsible for the construction. Although this move caused some losses to the general contractor, it ensured the normal start of the project and gained the trust of the owner.


## 5. Conclusion

This paper identifies the contract risks of overseas stadium EPC projects more comprehensively by conducting in-depth interviews with relevant experts and constructing a risk factor model using root theory, to assist the general contractors involved in overseas projects. Combining the constructed risk factor model and risk evaluation model to identify and evaluate the case contract risks, first, the overall contract risks were evaluated using AHP-fuzzy comprehensive evaluation method, second, eleven key risk factors were screened out using hierarchical total ranking, and the key risk factors were analyzed using ISM method, and finally, the successful experience of the project in dealing with risks was summarized. The training efficiency was improved by including all targets with as few subimage blocks as possible. Synthetic data enhancement using foreground-background subimage blocks solves the problem of many regions as background and is less likely to produce false positives for complex backgrounds. The detection effect is also further improved by ignoring the effect of incomplete targets with unclear semantic information. Simultaneously, considering that previous approaches to predict the target frame orientation in aerial images using regression are plagued by angular periodicity, i.e., small target rotations may lead to large changes in the network output, this paper uses a segmentation-based approach to predict the target frame angle to avoid this problem to obtain a better-rotated target frame, which makes the network easier to train and the segmentation-based results more accurate. Extensive experiments show that the algorithm achieves better detection performance in the DOTA database, which proves the effectiveness of the deep confidence neural network algorithm.



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*Source: 3715116-2021-07-05.xml*