Safety risk assessment of reservoir dam structure: an empirical study in China.

Reservoir dam structure is critical to protect public life and property and has always been attention worldwide. However, a systematic approach to assessing the safety risks of reservoir dam structure (RDS) is still required. This study presents a holistic framework for evaluating the safety risk of RDS and develops an evaluation index system. A risk assessment model is constructed based on the cloud and Dempster-Shafer (D-S) evidence theories. The model's validity is confirmed through an empirical analysis of the XY reservoir project. This study offers theoretical insights and practical solutions for managers to facilitate decision-making and supports the advancement of industry standards.

A detailed study on human height estimation model that combines video surveillance systems with 3D laser scanning.

The increase of video surveillance systems has highlighted the interest in forensic anthropometric analysis of subjects who commit crimes and are captured by cameras during their criminal activities. There are different methodologies for human height estimation. Forensic researchers developed a methodological approach that allows the height of a subject to be estimated through a combined model of 3D laser scanning acquisition and video images acquired by video surveillance systems. The proposed study had highlighted three limits: not assessments had been made for image correction to limit the distortion effect, the method had been tested by only one laboratory and probably height assessment was dependent on the ergonomics. To overcome these limitations, in this paper the analysis was repeated by correcting the images to compare the new results obtained with the previous ones. Furthermore, the same methodological approach was applied by estimating the height of a mannequin, to limit the ergonomic effects, and proposing the same study to three different forensic laboratories to compare the results. The presented study demonstrates the reliability and repeatability of the system, as the results obtained by the three laboratories are very similar. They have obtained the same trend and the maximum estimate distance is approximately 6 cm. Furthermore, it has showed that the accuracy of results is dependent on image correction, which has little impact (approximately 1 cm more accurate on the corrected frame than the normal frame) on the height evaluation and they are not dependent to the ergonomics of the subject captured.

Study on the risk assessment of Pedestrian-Vehicle conflicts in channelized Right-Turn lanes based on the Hierarchical-Grey Entropy-Cloud model.

Channelized right-turn lanes (CRTLs) in urban areas have been effective in improving the efficiency of right-turning vehicles but have also presented negative impacts on pedestrian movement. Pedestrians experience confusion regarding the allocation of road space when crossing crosswalks within these areas, leading to frequent conflicts between pedestrians and motor vehicles. In this paper, considering the characteristics of pedestrian-vehicle conflicts at channelized right-turn lanes as well as the ambiguity and uncertainty of the causes, a comprehensive assignment combined with a cloud model is proposed as a risk evaluation model for pedestrian-vehicle conflicts. The study established a risk indicator system based on three aspects of the transportation system: pedestrians, motor vehicles, and the road environment. Combining the analytic hierarchy process (AHP), grey relational analysis (GRA), and entropy weighting method (EWM) to get the weights of indicator combinations, and then using the cloud model to realize quantitative and qualitative language transformation to complete the risk evaluation. This study employs specific road segments in Qingdao as a validation case for model analysis. The results indicate that the model's evaluation outcomes exhibited a significant level of agreement with the findings from field investigations during both peak and off-peak periods. It is demonstrated that the model has good performance for the safety assessment of pedestrian-vehicle conflicts at CRTL, and it also reflects the ability of the model to assess fuzzy randomness problems. It provides participation value for urban pedestrian-vehicle safety problems as well as applications in other fields.

Analysis of vertical differentiation of vegetation in Taishan World Heritage site based on cloud model.

While the forests on Mount Taishan are predominantly man-made, there is a notable vertical variation in vegetation. This study employs the method of cloud model, quantifying uncertainty (fuzziness and randomness) of things. Utilizing digital elevation model (DEM) and vegetation distribution data, we constructed elevation cloud models for Mount Taishan's deciduous broad-leaved, temperate coniferous, and mixed coniferous-broadleaved forests. Using three numerical features of the cloud model-Expectation (EX), Entropy (EN), and Hyper-entropy (HE)-we quantitatively analyzed the macro regularity and local heterogeneity of Mount Taishan's forests vertical distribution from the perspective of uncertainty theory. The results indicate: (1) The EX of the core zone elevation of deciduous broad-leaved forest is 716.65 m, temperate coniferous forest is 1053.51 m, and mixed coniferous-broadleaved forest is 1384.09 m. The variation range of the core zone distribution height is smaller in the mixed coniferous-broadleaved forest (EN: 53.74 m) compared to deciduous broad-leaved forest (EN: 99.63 m) and temperate coniferous forest (EN: 121.70 m). (2) The fuzziness and randomness of the distribution height of the lower extension zones of deciduous broad-leaved forest and temperate coniferous forest (EN: 75.15 m, 184.56 m; HE: 24.09 m, 63.54 m) are greater than those of the upper extension zones (EN: 44.75 m, 42.49 m; HE: 14.48 m, 13.23 m). (3) The distribution fuzziness and randomness within temperate coniferous forests exceed those of deciduous broad-leaved forests. Within the core zones, the uncertainty regarding the vertical distribution of vegetation across different aspects remains consistent, which retains the characteristic of man-made forests. However, in transition areas, there is significant disparity, reflecting the adaptive relationship between vegetation and its environment to some extent. In the upper and lower extension zones of deciduous broad-leaved forests, the EX values for the vertical distribution height of mixed coniferous and broad-leaved forests differ significantly from those of deciduous broad-leaved forests (the difference is 22.82-39.15 m), yet closely resemble those of temperate coniferous forests (the difference is 4.79-7.94 m). This suggests a trend wherein deciduous broad-leaved tree species exhibit a proclivity to encroach upon coniferous forest habitats. The elevation cloud model of vertical vegetation zones provides a novel perspective and method for the detailed analysis of Mount Taishan's vegetation vertical differentiation.

Telepresence augmentation for visual and haptic guided immersive teleoperation of industrial manipulator.

Teleoperation under human guidance has become an effective solution to extend human's reach in various environments. However, the teleoperation system still faces challenges of insufficient sense of both visual and haptic feedback from remote environments, which results in the inadequate guidance for the operator. In this paper, a visual/haptic integrated perception and reconstruction system (VHI-PRS) is developed to provide the operator with 3D visual information and effective haptic guidance. Specifically, a visual-based telepresence augmentation method is proposed to provide the operator with virtual-reality combined visual telepresence, where the real point cloud model is directly superimposed on virtual manipulator to avoid the time-consuming process of mesh model rendering. With the utilization of visual information, a haptic-based telepresence augmentation method is proposed to provide the operator with comprehensive force feedback, including the virtual guiding force, virtual repulsive force and real-time interactive force, which greatly helps reduce the workload of operator. Finally, a user study on grab-place task is carried out to verify the effectiveness of proposed system.

Evaluation of land resources carrying capacity based on entropy weight and cloud similarity.

Land is the foundation of human life and development, which is also the most important part of a country. The study of land carrying capacity is one of the important contents of land management, wherein the evaluation of land resource carrying capacity (LRCC) is an important reference for land resource planning. Aiming at the information fuzziness and uncertainty in the evaluation of LRCC, firstly, a comprehensive evaluation model based on entropy weight and normal cloud similarity was proposed, which is based on cloud model theory and combined with normal cloud similarity measurement method and entropy weight method. Secondly, taking the asphalt pavement experiment as an example for empirical analysis, the experimental results are consistent with the actual situation, which proves the feasibility and effectiveness of the proposed model. Finally, taking China's Chongqing city as the research area, the proposed evaluation model is used to study LRCC. The research results indicate that the comprehensive carrying capacity and average carrying capacity of various systems in China's Chongqing have been improved in the past decade. Among them, the comprehensive carrying capacity rose from the second level during the "12th Five-Year Plan" period to the third level during the "13th Five-Year Plan" period. In the future, it is necessary to focus on the improvement of soil and water resources system and economic and technological system. This conclusion reflects LRCC of Chongqing in China objectively and has a reference value for Chongqing's land planning.

An innovative method based on Gaussian cloud distribution and sample information richness for eutrophication assessment of Yangtze's lakes and reservoirs under uncertainty.

The precise assessment of a water body's eutrophication status is essential for making informed decisions in water environment management. However, conventional approaches frequently fail to consider the randomness, fuzziness, and inherent hidden information of water quality indicators. These would result in an unreliable assessment. An enhanced method was proposed for the eutrophication assessment under uncertainty in this study. The multi-dimension gaussian cloud distribution was introduced to capture the randomness and fuzziness. The Shannon entropy based on various sample size and trophic levels was proposed to maximize valuable information hidden in the datasets. Twenty-seven significant lakes and reservoirs located in the Yangtze River Basin were selected to demonstrate the proposed method. The sensitivity and consistency were used to evaluate the accuracy of the proposed method. Results indicate that the proposed method has the capability to effectively assess the eutrophication status of lakes and reservoirs under uncertainty and that it has a better sensitivity since it can identify more than 33-50% trophic levels compared to the traditional methods. Further scenario experiments analysis revealed that the sample information richness, i.e., sample size and the number of trophic levels is of great significance to the accuracy/robustness of the method. Moreover, a sample size of 60 can offer the most favorable balance between accuracy/robustness and the monitoring expenses. These findings are crucial to optimizing the eutrophication assessment.

Research on safety evaluation of collapse risk in highway tunnel construction based on intelligent fusion.

To solve the problems of untimely and low accuracy of tunnel project collapse risk prediction, this study proposes a method of multi-source information fusion. The method uses the PSO-SVM model to predict the surrounding rock displacement. With the prediction index as the benchmark, the Cloud Model (CM) is used to calculate the basic probability assignment value. At the same time, the improved D-S theory is used to fuse the monitoring data, the advanced geological forecast, and the tripartite information indicators of site inspection patrol. This method is applied to the risk assessment of Jinzhupa Tunnel, and the decision-makers adjust the risk factors in time according to the prediction level. In the end, the tunnel did not collapse on a large scale.

Improving multidimensional normal cloud model to evaluate groundwater quality with grey wolf optimization algorithm and projection pursuit method.

Groundwater quality is related to several uncertain factors. Using multidimensional normal cloud model to reduce the randomness and ambiguity of the integrated groundwater quality evaluation is important in environmental research. Previous optimizations of multidimensional normal cloud models have focused on improving the affiliation criteria of the evaluation results, neglecting the weighting scheme of multiple indicators. In this study, a new multidimensional normal cloud model was constructed for the existing one-dimensional normal cloud model (ONCM) by combining the projection-pursuit (PP) method and the Grey Wolf Optimization (GWO) algorithm. The effectiveness and robustness of the model were analyzed. The results showed that compared with ONCM, the new multidimensional normal cloud model (GWOPPC model) integrated multiple evaluation parameters, simplified the modeling process, and reduced the number of calculations for the affiliation degree. Compared with other metaheuristic optimization algorithms, the GWO algorithms converged within 20 iterations during 20 simulations showing faster convergence speed, and the convergence results of all objective functions satisfy the iteration accuracy of 0.001, which indicates that the algorithm is more stable. Compared to the traditional entropy weights (0.27, 0.23, 0.47, 0.44, 0.29, 0.59, 0.12) or principal component weights (0.38, 0.33, 0.42, 0.34, 0.47, 0.29, 0.38), the weight allocation scheme provided by the GWOPP method (0.50, 0.48, 0.05, 0.38, 0.02. 0.51 and 0.32) considers the density of the distribution of all samples in the data set space. Among all 55 groundwater samples, the GWOPPC model has 21 samples with lower evaluation ratings than the fuzzy evaluation method, and 28 samples lower than the Random Forest method or the WQI method, indicating that the GWOPPC model is more conservative under the conditions of considering fuzziness and randomness. This method can be used to evaluate groundwater quality in other areas to provide a basis for the planning and management of groundwater resources.

Coal and gas protrusion risk evaluation based on cloud model and improved combination of assignment.

The proposed study presents an enhanced combination weighting cloud model for accurate assessment of coal and gas outburst risks. Firstly, a comprehensive evaluation index system for coal and gas outburst risks is established, consisting of primary indicators such as coal rock properties and secondary indicators including 13 factors. Secondly, the improved Analytic Hierarchy Process (IAHP) based on the 3-scale method and the improved CRITIC based on indicator correlation weight determination method are employed to determine subjective and objective weights of evaluation indicators respectively. Additionally, the Lagrange multiplier method is introduced to fuse these weights in order to obtain optimal weights. Subsequently, a prominent danger assessment model is developed based on cloud theory. Finally, using a mine in Hebei Province as an example, the results obtained from IAHP combined with improved CRITIC weighting method are compared with those from traditional AHP method and AHP-CRITIC combination weighting method. The findings demonstrate that among all methods considered, IAHP combined with improved CRITIC exhibits superior performance in terms of distribution expectation Ex, entropy value En, and super entropy He within cloud digital features; thus indicating that the risk level of coal and gas outbursts in this particular mine can be classified as general risk. These evaluation results align well with actual observations thereby validating the effectiveness of this approach. Consequently, this constructed model enables rapid yet accurate determination of coal and gas outburst risks within mines.

Comprehensive risk management of health, safety and environment for social emergency rescue organization.

During the rescue and relief work of social emergency rescue organizations, the rescue team members face significant responsibilities and risks. Social rescue organizations need to improve funding, available equipment and other aspects compared with professional emergency rescue organizations. Moreover, the development of rescue levels among emergency rescue organizations is unbalanced, and rescue teams'' comprehensive quality and skills are uneven. To understand the safety risks of these organizations before the implementation of rescue and relief tasks, the task situation and its characteristics must be assessed timely, and safety must be ensured under the premise of efficient completion of the rescue missions. Based on the theory of safety system engineering and health, safety, and environment risk management, a risk management model is established to achieve a closed-loop risk management. The risk factors in rescue and relief tasks of social rescue organizations were identified, and a health, safety and environment risk assessment index system and grading standard were established. A gray cloud model was applied for the evaluation method, the problems of information randomness, risk-level boundary fuzziness and randomness of the evaluation index data were effectively solved. Subsequently, a risk hierarchical early warning and control strategies were proposed to allocate emergency resources rationally. The proposed method was verified and found to have universal applicability and strong practicability.

An innovative method for human height estimation combining video images and 3D laser scanning.

Digitalization has increased the number of video surveillance systems that sometimes capture crime images. Traditional methods of human height estimation use projective geometry. However, sometimes they cannot be used because the video camera surveillance system is not available or has been moved and there are no reference lines on the frame. Scientific studies have developed a new method for human height estimation using 3D laser scanning. This model necessarily requires a series of approximations, which increase the final measurement error. To overcome this problem, in the present study, images of a subject are projected directly on the 3D model, estimating the height of the subject. This article describes the methodological approach adopted through the analysis of a real case study in a controlled environment executed by Carabinieri Forensic Investigation Department (Italy). The aim is to obtain a human anthropometric measure derived from frames extracted from the videos associated with the digital survey of the framed area obtained with 3D laser scanning and point cloud analysis. The result is the height estimation of five subjects filmed by a camera obtained through the combination of 2D images extracted by a DVR/surveillance systems with 3D laser scanning. Results show that most estimated measurements are less than the real measurement of the subject; it also depends on the posture of the subject while walking. Furthermore, results shows the differences between the real height and the estimated height with a statistical approach.

Research on linguistic multi-attribute decision making method for normal cloud similarity.

Aiming at the uncertainty linguistic transformation problem in multi-attribute decision making, a decision-making method based on normal cloud similarity was proposed. Firstly, starting from the normal cloud characteristic curves, a normal cloud similarity measurement method based on Wasserstein distance is proposed by combing with the normal cloud entropy-containing expectation curve, which is using the Wasserstein distance to characterize the similarity characteristics of probability distribution. The properties of the proposed similarity measure are discussed in the paper. Secondly, the performance of the proposed method is compared and analyzed with the existed methods by numerical simulation experiment and time series data classification experiment. The experimental results show that the proposed method has good similarity discrimination ability, high classification accuracy and low CPU time cost. Finally, the method was successfully applied into linguistic multi-attribute decision making, and TOPSIS thought is used to compare and rank the schemes, so as to realize the final decision.

A novel similarity measurement for triangular cloud models based on dual consideration of shape and distance.

It is important to be able to measure the similarity between two uncertain concepts for many real-life AI applications, such as image retrieval, collaborative filtering, risk assessment, and data clustering. Cloud models are important cognitive computing models that show promise in measuring the similarity of uncertain concepts. Here, we aim to address the shortcomings of existing cloud model similarity measurement algorithms, such as poor discrimination ability and unstable measurement results. We propose an EPTCM algorithm based on the triangular fuzzy number EW-type closeness and cloud drop variance, considering the shape and distance similarities of existing cloud models. The experimental results show that the EPTCM algorithm has good recognition and classification accuracy and is more accurate than the existing Likeness comparing method (LICM), overlap-based expectation curve (OECM), fuzzy distance-based similarity (FDCM) and multidimensional similarity cloud model (MSCM) methods. The experimental results also demonstrate that the EPTCM algorithm has successfully overcome the shortcomings of existing algorithms. In summary, the EPTCM method proposed here is effective and feasible to implement.

Predicting examinee performance based on a fuzzy cloud cognitive diagnosis framework in e-learning environment.

The score profiles could be used to measure learners' skills proficiency via cognitive diagnosis models (CDMs) for predicting their performance in the future examination. The prediction results could provide important decision-making supports for personalized e-learning instruction. However, facing the possible complexity of skills, the uncertainty of learners' skill proficiency and the large-scale volume of score profiles, the existing CDMs have limitations in the measurement mechanisms and diagnostic efficiency. In this paper, we proposed an approach based on a fuzzy cloud cognitive diagnosis framework (FC-CDF) to predicting examinees' performance in e-learning environment. In this approach, the normal cloud models (NCMs) are utilized innovatively to measure the expectation, degree of variation and variation frequency of learners' skill proficiency, and each NCM is transformed into an interval fuzzy number to characterize the uncertainty of the skill proficiency for every learner. Combining the educational psychology hypothesis with the parameter estimation method, we could obtain the learners' skill proficiency level and the slip and guess factors relevant to each test item, based on which the learners' scores could be predicted in a future test. Finally, the experiments demonstrate that the proposed approach provides good accuracy and significantly reduces execution time for predicting examinee performance, compared with the existing methods.

The comprehensive risk assessment of the Tangjiashan landslide dam incident, China.

Risk assessment for landslide dams is very important to avoid unanticipated landslide failure and calamity. Recognition of the risk of landslide dams associated with changing influencing factors is to identify the risk grade and provide early warning of oncoming failure, while quantitative risk analysis of landslide dams due to many influencing factors changing in spatiotemporal domain is currently lacking. We applied the model to analyze the risk level of the Tangjiashan landslide dam caused by the Wenchuan Ms 8.0 earthquake. The risk evaluation, obtained according to the analysis of the influencing factors located in the risk assessment grade criteria, clearly shows that the risk reaches a higher level at that moment. Our analysis shows that the risk level of landslide dams can be quantitatively analyzed with our assessment method. Our results suggest that the risk assessment system can be an effective measure to dynamically predict the risk level and provide a sufficient early warning of the oncoming hazard by analyzing the variables of influencing factors at different times.

Implementation of pollution source assessment and treatment strategy for plateau railway construction in China: an AHP-cloud model approach.

During the construction process of railways in the plateau region, various types of pollution sources can have serious or even irreversible impacts on the plateau ecology. To address pollution source treatment during the construction process, protect the ecological environment along the railway, and maintain the ecological balance, we collected geological and environmental data and analyzed the influencing factors of pollution sources. Taking sewage as the main research subject, we propose a new method based on the Analytic Hierarchy Process (AHP)-cloud model to classify the pollution source treatment level, establish an index system, and select the ecological environment level, sewage rate, and pollutant characteristics as the three main influencing factors. Finally, we divide the pollution source treatment level into I, II, and III, corresponding to V1 = {I-level} = {high impact}, V2 = {II-level} = {moderate impact}, and V3 = {III-level} = {low impact}. Based on the comprehensive factor weight analysis and the field engineering conditions of the studied railway in the western plateau of China, we classify the pollution source treatment level of six tunnels and propose treatment suggestions for each level. To advance the efficient implementation of environmental protection during the construction of the plateau railway, we propose three policy recommendations that can positively contribute to environmental protection and green development. This work provides theoretical and technical guidance for the treatment of pollution sources in the construction of the plateau railway, which also serves as a significant reference for other similar projects.

A Rice Hazards Risk Assessment Method for a Rice Processing Chain Based on a Multidimensional Trapezoidal Cloud Model.

Rice is common in the human diet, making rice safety issues important. Moreover, rice processing safety is key for rice security, so rice processing chain risk assessment is critical. However, methods proposed to assess the rice processing chain risk have issues, such as the use of unreasonable thresholds for the rice processing chain and fixed weight. To solve these problems, we propose a risk assessment method for the rice processing chain based on a multidimensional trapezoidal cloud model. First, an evaluation model based on a multidimensional trapezoidal cloud model was established. Based on the historical evaluation results, Atanassov's interval-valued intuition language numbers (AIVILNs) were introduced to determine the cloud model's parameters. Second, the concept of dynamic weight was introduced to integrate the static and dynamic weights. An exponential function was used to construct dynamic weighting mechanisms, and the analytic hierarchy stage (AHP) was used to construct a static weight. The proposed method was validated by 104 sets of rice processing chain data, and the results show that the method could evaluate the risk level of the rice processing chain more accurately and reasonably than other methods, indicating that it can provide a sound decision-making basis for food safety supervision authorities.

Resilience assessment of metro stations against rainstorm disaster based on cloud model: a case study in Chongqing, China.

Extremely heavy rainfall has posed a significant hazard to urban growth as the most common and disaster-prone natural calamity. Due to its unique geographical location, the metro system is more vulnerable to waterlogging caused by rainstorm disaster. Research on resilience to natural disasters has attracted extensive attention in recent years. However, few studies have focused on the resilience of the metro system against rainstorms. Therefore, this paper aims to develop an assessment model for evaluating metro stations' resilience levels. Twenty factors are carried out from dimensions of resistance, recovery and adaptation. The methods of ordered binary comparison, entropy weight and cloud model are proposed to build the assessment model. Then, taking Chongqing metro system in china as a case study, the resilience level of 13 metro stations is calculated. Radar charts from dimensions of resistance, recovery, and adaptation are created to propose recommendations for improving metro stations' resilience against rainstorms, providing a reference for the sustainable development of the metro system. The case study of the Chongqing metro system in china demonstrates that the assessment model can effectively evaluate the resilience level of metro stations and can be used in other infrastructures under natural disasters for resilience assessment.

A new method for subsea tunnel site selection based on environmental bearing capacity.

Engineering site selection is an essential and systematic work in the early engineering construction stage. At present, the subsea tunnel site selection mainly depends on manual experience. There is still a lack of subsea tunnel site selection systems based on environmental impact. This study develops a comprehensive site selection evaluation system based on the analytic hierarchy process (AHP) and fuzzy evaluation method for the subsea tunnel site selection. It is a multi-indicator mathematical model evaluation system. On this basis, the ecological site selection method of the subsea tunnel is further studied, an indicator system for evaluating the environmental carrying capacity of the island is established, and the site selection results of the subsea tunnel based on the environmental indicators are obtained. We compared the site selection results of the two methods. The results show that the conventional method and the ecological site selection method based on environmental indicators can well carry out the site selection of subsea tunnels. The two methods take into account both the overall and local optimum of the subsea tunnel route and organically combine the overall and local objectives. This way provides a reference for the design and construction of the subsea tunnel in the future and points out the direction for the site selection of other large-scale projects with significant environmental impact.