Hierarchical Fuzzy Model Research Articles

Hierarchical Takagi-Sugeno fuzzy hyperbolic tangent static model control for a circulating fluidized bed boiler thermal power unit

This paper proposed a novel two layer hierarchical Takagi-Sugeno fuzzy hyperbolic tangent static model control scheme for a circulating fluidized bed (CFB) boiler thermal power unit to cope with the complex nonlinearity and large inertia performance which make its existing PI type load-fuel control loop not functioning normally. In the upper layer, a reference governor with Takagi-Sugeno fuzzy hyperbolic tangent static model control algorithm was developed to yield a compensation for the external load command of the existing PI type load-fuel control loop in the lower layer. By constructing a new quadratic integration Lyapunov function, the stability condition of proposed control system was derived. While implemented in the decentralized control system (DCS) of CFB boiler, the reference governor was constructed and adopted the cubic variables hyperbolic tangent function algorithm which can oppose effectively the nonlinearity and large inertia performance. The experimental results shown that the proposed hierarchical fuzzy hyperbolic tangent static model control scheme can improve the control performance of the PI type load-fuel control system obviously, weaken the fluctuation in the main steam pressure and bed temperature apparently and make the controlled system more stable.

Underground Risk Index Assessment and Prediction Using a Simplified Hierarchical Fuzzy Logic Model and Kalman Filter

Normally, most of the accidents that occur in underground facilities are not instantaneous; rather, hazards build up gradually behind the scenes and are invisible due to the inherent structure of these facilities. An efficient inference system is highly desirable to monitor these facilities to avoid such accidents beforehand. A fuzzy inference system is a significant risk assessment method, but there are three critical challenges associated with fuzzy inference-based systems, i.e., rules determination, membership functions (MFs) distribution determination, and rules reduction to deal with the problem of dimensionality. In this paper, a simplified hierarchical fuzzy logic (SHFL) model has been suggested to assess underground risk while addressing the associated challenges. For rule determination, two new rule-designing and determination methods are introduced, namely average rules-based (ARB) and max rules-based (MRB). To determine efficient membership functions (MFs), a module named the heuristic-based membership functions allocation (HBMFA) module has been added to the conventional Mamdani fuzzy logic method. For rule reduction, a hierarchical fuzzy logic model with a distinct configuration has been proposed. In the simplified hierarchical fuzzy logic (SHFL) model, we have also tried to minimize rules as well as the number of levels of the hierarchical structure fuzzy logic model. After risk index assessment, the risk index prediction is carried out using a Kalman filter. The prediction of the risk index is significant because it could help caretakers to take preventive measures in time and prevent underground accidents. The results indicate that the suggested technique is an excellent choice for risk index assessment and prediction.

A Blended Risk Index Modeling and Visualization Based on Hierarchical Fuzzy Logic for Water Supply Pipelines Assessment and Management

Critical infrastructure such as power and water delivery is growing rapidly in the developing world and there are developed assets that must be maintained in developed nations. One underground component that is difficult to inspect is water supply pipelines. Most of the water line accidents occur in buildings is due to pipeline damage. To minimize accidental loss, a risk assessment method is needed to continuously assess risk and report any abnormality for preventative maintenance. In this work, a blended hierarchical fuzzy logic model for water supply pipeline risk index assessment is proposed. Four important parameters are inputs to the proposed blended hierarchical fuzzy logic model. The blended hierarchical fuzzy logic model dramatically reduces the number of conditions in the rule base. Rule reduction is important because the transparency and interpretation are compromised by an overly large set. Further, it is challenging to accurately design a large number of rules because rule design requires expert knowledge and uncertainty in predictions can lead to unforeseen incidents. A blended hierarchical fuzzy model is designed with a structure that takes fewer rules as compared to conventional fuzzy logic. For the four parameters, the proposed model takes 85 rules and for the same four parameters, the conventional fuzzy logic approach requires 900 rules. In this paper, four parameters are considered because these are available measurements. The proposed reduction method is also applicable for systems with more parameters. The numbers of rules increase exponentially in the conventional fuzzy logic as new parameters enter into the system. The blended hierarchical fuzzy logic is deployed on a water distribution network to compute the risk index of supply pipelines. The results indicate improved performance of the blended hierarchical fuzzy logic. This approach is usable in practical applications where the calculated risk index values of the water supply pipeline are plotted on a geographic information system (GIS) map to provide a graphical interface for the caretaker. The risk index visualization is necessary to trace the risk index location and to take preemptive measures to avoid failures.

Supporting decision-making in patient risk assessment using a hierarchical fuzzy model

In this paper, we present a hierarchical fuzzy model to support patient triage in primary health care. In developing countries like Brazil, public health must usually cover degraded territories; thus, allocating patients to health services is very hard due low availability, enormous demands, and the complexity of assessing patient conditions—which must account for more then physical aspects of patients, but their social conditions as well. This approach combines the fuzzy set theory under the AHP framework in order to illustrate the inherent imprecision in the evaluation of patient risk. Fieldwork was conducted in a primary healthcare facility in Brazil to demonstrate the applicability of the proposed approach. The proposed approach represents criterion in the formation of patients’ risk scores encompassing important aspects of primary care triage such as the structure of families, the conditions of residences, exposure to urban violence, and other aspects of patients’ lives, taking the risk assessment beyond the simple evaluation of symptoms and physiological conditions. Our approach focuses on enforcing decisions of public health workers by improving the awareness of patients’ conditions, which we believe will make the employment of triage criteria uniform and capable of showing tendencies on patients’ risks, as well as avoiding bias in patient triage.

Evaluation model of economic competitiveness based on multi-layer fuzzy neural network

According to related theories of regional economics, eight indexes closely related to development level county economy of certain city in Sichuan province were chosen. Evaluation model of county economy competitiveness in Sichuan province based on multi-layer fuzzy neural network was proposed in the Thesis. Firstly, evaluation for in county economic competitiveness was analyzed, and evaluation index system for its competitiveness was given. Model index was provided with pre-treatment by adopting analysis method for normalization and factor; secondly, one four-layer fuzzy neural network model was used, and hierarchical fuzzy neural network model for economic competitiveness evaluation of county region was proposed. It included input layer, fuzzy layer, fuzzy reasoning layer and output layer, and composition and computing method for fuzzy function block and neural network module were analyzed; finally, through empirical analysis, effectiveness for algorithm was verified.

Modeling of Natural Ventilation Using a Hierarchical Fuzzy Control System for a New Energy-Saving Solar Greenhouse

Abstract. Natural ventilation as part of a greenhouse environment control system can save energy, reduce pollution, and cut the costs of production. Many studies have used the energy balance method to model greenhouse environments and calculate the rates of ventilation needed to control them. However, the efficacy of this method is influenced by many factors. There are often many parameters in the energy balance model, related to the greenhouse structure, crop growth, and climate. Thus, the effectiveness and applicability of greenhouse ventilation systems based on energy balance can be compromised. Here, we study a hierarchical fuzzy control method and use simple fuzzy logic controllers to control the coordination and opening angles of a new energy-saving solar greenhouse roof and sidewall ventilation. This design reduces the complexity of the fuzzy rule base and the fuzzy subsystem related to the physical model, therefore making it easy to design and build. The system uses the fuzzy tool in the Matlab environment, enabling a quick design, and the fuzzy inference engine fis.c file (Fuzzy Inference System) to load the design results into the fuzzy control system, thus making the modification and maintenance of the system easier. The experimental data showed that the new hierarchical fuzzy control reduced temperature fluctuations and maintained temperature closer to desired temperature than a non-fuzzy control method. Moreover, this method can also be easily used to control other equipment in the greenhouse. Keywords: Hierarchical fuzzy model, Natural ventilation, Matlab, Solar greenhouse.

Automatic thoracic anatomy segmentation on CT images using hierarchical fuzzy models and registration.

In an attempt to overcome several hurdles that exist in organ segmentation approaches, the authors previously described a general automatic anatomy recognition (AAR) methodology for segmenting all major organs in multiple body regions body-wide [J. K. Udupa et al., "Body-wide hierarchical fuzzy modeling, recognition, and delineation of anatomy in medical images," Med. Image Anal. 18(5), 752-771 (2014)]. That approach utilized fuzzy modeling strategies, a hierarchical organization of organs, and divided the segmentation task into a recognition step to localize organs which was then followed by a delineation step to demarcate the boundary of organs. It achieved speed and accuracy without employing image/object registration which is commonly utilized in many reported methods, particularly atlas-based. In this paper, our aim is to study how registration may influence performance of the AAR approach. By tightly coupling the recognition and delineation steps, by performing registration in the hierarchical order of the organs, and through several object-specific refinements, the authors demonstrate that improved accuracy for recognition and delineation can be achieved by judicial use of image/object registration. The presented approach consists of three processes: model building, hierarchical recognition, and delineation. Labeled binary images for each organ are registered and aligned into a 3D fuzzy set representing the fuzzy shape model for the organ. The hierarchical relation and mean location relation between different organs are captured in the model. The gray intensity distributions of the corresponding regions of the organ in the original image are also recorded in the model. Following the hierarchical structure and location relation, the fuzzy shape model of different organs is registered to the given target image to achieve object recognition. A fuzzy connectedness delineation method is then employed to obtain the final segmentation result of organs with seed points provided by recognition. The authors assess the performance of this method for both nonsparse (compact blob-like) and sparse (thin tubular) objects in the thorax. The results of eight thoracic organs on 30 real images are presented. Overall, the delineation accuracy in terms of mean false positive and false negative volume fractions is 0.34% and 4.02%, respectively, for nonsparse objects, and 0.16% and 12.6%, respectively, for sparse objects. The two object groups achieve mean boundary distance relative to ground truth of 1.31 and 2.28 mm, respectively. The hierarchical structure and location relation integrated into the model provide the initial pose for registration and make the recognition process efficient and robust. The 3D fuzzy model combined with hierarchical affine registration ensures that accurate recognition can be obtained for both nonsparse and sparse organs. Tailoring the registration process for each organ by specialized similarity criteria and updating the organ intensity properties based on refined recognition improve the overall segmentation process.

Automatic anatomy recognition in whole-body PET/CT images.

Whole-body positron emission tomography/computed tomography (PET/CT) has become a standard method of imaging patients with various disease conditions, especially cancer. Body-wide accurate quantification of disease burden in PET/CT images is important for characterizing lesions, staging disease, prognosticating patient outcome, planning treatment, and evaluating disease response to therapeutic interventions. However, body-wide anatomy recognition in PET/CT is a critical first step for accurately and automatically quantifying disease body-wide, body-region-wise, and organwise. This latter process, however, has remained a challenge due to the lower quality of the anatomic information portrayed in the CT component of this imaging modality and the paucity of anatomic details in the PET component. In this paper, the authors demonstrate the adaptation of a recently developed automatic anatomy recognition (AAR) methodology [Udupa et al., "Body-wide hierarchical fuzzy modeling, recognition, and delineation of anatomy in medical images," Med. Image Anal. 18, 752-771 (2014)] to PET/CT images. Their goal was to test what level of object localization accuracy can be achieved on PET/CT compared to that achieved on diagnostic CT images. The authors advance the AAR approach in this work in three fronts: (i) from body-region-wise treatment in the work of Udupa et al. to whole body; (ii) from the use of image intensity in optimal object recognition in the work of Udupa et al. to intensity plus object-specific texture properties, and (iii) from the intramodality model-building-recognition strategy to the intermodality approach. The whole-body approach allows consideration of relationships among objects in different body regions, which was previously not possible. Consideration of object texture allows generalizing the previous optimal threshold-based fuzzy model recognition method from intensity images to any derived fuzzy membership image, and in the process, to bring performance to the level achieved on diagnostic CT and MR images in body-region-wise approaches. The intermodality approach fosters the use of already existing fuzzy models, previously created from diagnostic CT images, on PET/CT and other derived images, thus truly separating the modality-independent object assembly anatomy from modality-specific tissue property portrayal in the image. Key ways of combining the above three basic ideas lead them to 15 different strategies for recognizing objects in PET/CT images. Utilizing 50 diagnostic CT image data sets from the thoracic and abdominal body regions and 16 whole-body PET/CT image data sets, the authors compare the recognition performance among these 15 strategies on 18 objects from the thorax, abdomen, and pelvis in object localization error and size estimation error. Particularly on texture membership images, object localization is within three voxels on whole-body low-dose CT images and 2 voxels on body-region-wise low-dose images of known true locations. Surprisingly, even on direct body-region-wise PET images, localization error within 3 voxels seems possible. The previous body-region-wise approach can be extended to whole-body torso with similar object localization performance. Combined use of image texture and intensity property yields the best object localization accuracy. In both body-region-wise and whole-body approaches, recognition performance on low-dose CT images reaches levels previously achieved on diagnostic CT images. The best object recognition strategy varies among objects; the proposed framework however allows employing a strategy that is optimal for each object.

Personal-Statistics-Based Heart Rate Evaluation in Anytime Risk Calculation Model

Nowadays, the changes in people’s habits and the development of technology result in the wide spreading of the model-based health monitoring systems. In this paper, an improvement of the measurement evaluation method is introduced, which is used during the sport activity in real-time. The basis of the novel approach is our previously reported anytime hierarchical fuzzy risk calculation model, which is able to handle some uncertainties, imprecision, and subjectivity in the data and in the evaluation process and can cope with the dynamically changing environment, available time, and resources. In the new model the input membership functions, which are tuned according to the patient characteristics, are modified based on the data recorded during previous measurements under the same conditions. By this, the person-dependent characteristics and the unavoidable changing of the dynamic reactions of the human organism can also be considered and the risk level can be more reliably predicted.

Variable coded hierarchical fuzzy classification model using DNA coding and evolutionary programming

In this study, we propose a new variable coded hierarchical fuzzy model (VCHFM) for handling classification problems. The proposed hierarchical framework classification model synergistically integrates the standard fuzzy inference system and DNA coding with supervised learning. The VCHFM automatically generates fuzzy rules from numerical data and membership functions using both the feature extraction unit and the inference unit. Furthermore, three modified algorithms are employed by the proposed VCHFM. The implementation of this model comprises four stages. First, a genetic algorithm procedure is used to determine the distribution of fuzzy sets for each feature variable in the feature extraction unit. Second, the membership functions are adjusted by DNA computing. Third, chaotic particle swarm optimization is used to regulate the weighting grade of the principal output node in the inference unit. Finally, a multi-objective optimum fitness function is used to ensure the best classification rate with the minimum number and length of rules. We validated the proposed VCHFM by classifying five benchmark datasets: the UCI Pima Indians Diabetes, Glass, Wisconsin Breast Cancer, Wine, and Iris datasets. The computer simulation results demonstrate that the proposed VCHFM can obtain a sufficiently high classification rate, unlike other models proposed in previous studies.

Approaches of fuzzy systems applied to an AGV dispatching system in a FMS

Excellence in manufacturing systems has been recognized as one of the main factors behind the success of industrial companies or production companies. New technology for manufacturing processes plays a significant role in this process. Achieving the potential of technological innovations in production, however, requires a wide range of management, as well as engineering issues related to the system. The handling capacity of advanced materials is essential because without this ability of providing the material needed for the proper workstation at the right time and in the right amount, the whole plant will become “bogged down.” This makes it less efficient and thus produces less profit, and/or it operates at higher costs. This paper proposes two approaches for the dispatching of AGV (automated guided vehicle) using systems fuzzy. The first use hierarchical fuzzy rule-based model building in which the main feature is to make the base of fuzzy rules is smaller and simpler but with high coverage and interpretability. The second use adaptive genetic fuzzy system with simple prediction in which the main feature is to increase the sensitivity of the system about the variables. Both approaches using multiple attributes and having the objective decrease the makespan in a FMS (flexible manufacturing system).

Body-wide hierarchical fuzzy modeling, recognition, and delineation of anatomy in medical images.

To make Quantitative Radiology (QR) a reality in radiological practice, computerized body-wide Automatic Anatomy Recognition (AAR) becomes essential. With the goal of building a general AAR system that is not tied to any specific organ system, body region, or image modality, this paper presents an AAR methodology for localizing and delineating all major organs in different body regions based on fuzzy modeling ideas and a tight integration of fuzzy models with an Iterative Relative Fuzzy Connectedness (IRFC) delineation algorithm. The methodology consists of five main steps: (a) gathering image data for both building models and testing the AAR algorithms from patient image sets existing in our health system; (b) formulating precise definitions of each body region and organ and delineating them following these definitions; (c) building hierarchical fuzzy anatomy models of organs for each body region; (d) recognizing and locating organs in given images by employing the hierarchical models; and (e) delineating the organs following the hierarchy. In Step (c), we explicitly encode object size and positional relationships into the hierarchy and subsequently exploit this information in object recognition in Step (d) and delineation in Step (e). Modality-independent and dependent aspects are carefully separated in model encoding. At the model building stage, a learning process is carried out for rehearsing an optimal threshold-based object recognition method. The recognition process in Step (d) starts from large, well-defined objects and proceeds down the hierarchy in a global to local manner. A fuzzy model-based version of the IRFC algorithm is created by naturally integrating the fuzzy model constraints into the delineation algorithm. The AAR system is tested on three body regions - thorax (on CT), abdomen (on CT and MRI), and neck (on MRI and CT) - involving a total of over 35 organs and 130 data sets (the total used for model building and testing). The training and testing data sets are divided into equal size in all cases except for the neck. Overall the AAR method achieves a mean accuracy of about 2 voxels in localizing non-sparse blob-like objects and most sparse tubular objects. The delineation accuracy in terms of mean false positive and negative volume fractions is 2% and 8%, respectively, for non-sparse objects, and 5% and 15%, respectively, for sparse objects. The two object groups achieve mean boundary distance relative to ground truth of 0.9 and 1.5 voxels, respectively. Some sparse objects - venous system (in the thorax on CT), inferior vena cava (in the abdomen on CT), and mandible and naso-pharynx (in neck on MRI, but not on CT) - pose challenges at all levels, leading to poor recognition and/or delineation results. The AAR method fares quite favorably when compared with methods from the recent literature for liver, kidneys, and spleen on CT images. We conclude that separation of modality-independent from dependent aspects, organization of objects in a hierarchy, encoding of object relationship information explicitly into the hierarchy, optimal threshold-based recognition learning, and fuzzy model-based IRFC are effective concepts which allowed us to demonstrate the feasibility of a general AAR system that works in different body regions on a variety of organs and on different modalities.

Development of hierarchical fuzzy model for groundwater vulnerability to pollution assessment

There is a need to develop new modelling techniques that assess groundwater vulnerability with less extensive data and which are robust when data are uncertain and incomplete. In this present study, a hierarchical fuzzy system (HFS) model was developed to systematically quantify uncertainties associated with hydro-geological conditions of the site in groundwater vulnerability assessment. The need for hierarchical fuzzy expert systems can occur frequently for higher number of input variables. The HFS model was designed for seven input variables viz. depth to water table (D), net recharge (R), aquifer media (A), soil media (S), topography (T), impact of vadose zone (I), and hydraulic conductivity (C). The model was validated by the observed nitrate concentrations in groundwater. The HFS model was applied to groundwater vulnerability assessment in Ranchi district, Jharkhand, India. Sensitivity analysis of model was also demonstrated and compared the results with that of the existing method (DRASTIC model). It was observed that HFS model are better sensitive to the input parameters in comparison to that of the DRASTIC model. The present study offers a unique tool to policy makers for decision makings in groundwater management.

Adapt-Traf: An adaptive multiagent road traffic management system based on hybrid ant-hierarchical fuzzy model

Usually, road networks are characterized by their great dynamics including different entities in interactions. This leads to more complex road traffic management. This paper proposes an adaptive multiagent system based on the ant colony behavior and the hierarchical fuzzy model. This system allows adjusting efficiently the road traffic according to the real-time changes in road networks by the integration of an adaptive vehicle route guidance system. The proposed system is implemented and simulated under a multiagent platform in order to discuss the improvement of the global road traffic quality in terms of time, fluidity and adaptivity.

Fuzzy Prediction of Molten Iron Silicon Content in BF Based on Hierarchical System

A hierarchical fuzzy system model is presented based on data driving, and then, the model is used to predict the molten iron silicon content in BF. As input variables this model uses the control parameters of a current BF such as moisture, pulverized coal injection, oxygen addition, coke ratio etc. And variables employed to develop the model have been obtained from data collected online from Blast Furnace of Baotou Steel plant. This paper utilizes the fuzzy clustering algorithm combined nearest neighbor clustering and fuzzy c-means clustering to classify the input space. The simulation and error results show that the prediction based on hierarchical fuzzy model and data-driven method has good approximation and fit the output characteristics of the system. The most important point is that the number of fuzzy rules is greatly reduced.

Evaluating stakeholder satisfaction during public participation in major infrastructure and construction projects: A fuzzy approach

Numerous different and sometimes discrepant interests can be affected, both positively and negatively, throughout the course of a major infrastructure and construction (MIC) project. Failing to address and meet the concerns and expectations of the stakeholders involved has resulted in many project failures. One way to address this issue is through a participatory approach to project decision making. Whether the participation mechanism is effective or not depends largely on the client/owner. This paper provides a means of systematically evaluating the effectiveness of the public participation exercise, or even the whole project, through the measurement of stakeholder satisfaction. Since the process of satisfaction measurement is complicated and uncertain, requiring approximate reasoning involving human intuition, a fuzzy approach is adopted. From this, a multi-factor hierarchical fuzzy comprehensive evaluation model is established to facilitate the evaluation of satisfaction in both single stakeholder group and overall MIC project stakeholders.

Hinging hyperplane based regression tree identified by fuzzy clustering and its application

Hierarchical fuzzy modeling techniques have great advantage since model accuracy and complexity can be easily controlled thanks to the transparent model structures. A novel tool for regression tree identification is proposed based on the synergistic combination of fuzzy c-regression clustering and the concept of hierarchical modeling. In a special case (c=2), fuzzy c-regression clustering can be used for identification of hinging hyperplane models. The proposed method recursively identifies a hinging hyperplane model that contains two linear submodels by partitioning operating region of one local linear model resulting a binary regression tree. Novel measures of model performance and complexity are developed to support the analysis and building of the proposed special model structure. Effectiveness of proposed model is demonstrated by benchmark regression datasets. Examples also demonstrate that the proposed model can effectively represent nonlinear dynamical systems. Thanks to the piecewise linear model structure the resulted regression tree can be easily utilized in model predictive control. A detailed application example related to the model predictive control of a water heater demonstrate that the proposed framework can be effectively used in modeling and control of dynamical systems.

Evaluating and selecting supplier in textile industry using hierarchical fuzzy TOPSIS

In textile industries, the costs of raw material or cotton make the main part of the prime cost. Reduction of purchasing costs is one the most important issues to bring down the selling price. In this paper, the Mazandaran textile factory, one of the biggest textile industrial units, in Iran is considered for the cotton supplier selection problems. The effective criteria for ranking the suitable suppliers are evaluated using hierarchical fuzzy TOPSIS model. Based on the results, the quality of cotton is considered as the most important criterion in evaluation of cotton suppliers. Also, among different provinces, cotton produced in Golestan is considered as the best quality in the region. Ali-abad cotton factory located in Golestan ranked first which matched with the quality-oriented strategy of Mazandaran textile factory. Supplier's enough storage to meet the customers' regular and emergency needs is another important criterion. In addition, the flexibility, financial stability and strength and pricing and payment policies play critical roles in selection of the suppliers.

Improved hierarchical fuzzy TOPSIS for road safety performance evaluation

With the ever increasing public awareness of complicated road safety phenomenon, much more detailed aspects of crash and injury causation rather than only crash data are extensively investigated in the current road safety research. Safety performance indicators (SPIs), which are causally related to the number of crashes or to the injury consequences of a crash, are rapidly developed and increasingly used. To measure the multi-dimensional concept of road safety which cannot be captured by a single indicator, the exploration of a composite road safety performance index is vital for rational decision-making about road safety. In doing so, a proper decision support system is required. In this study, we propose an improved hierarchical fuzzy TOPSIS model to combine the multilayer SPIs into one overall index by incorporating experts’ knowledge. Using the number of road fatalities per million inhabitants as a relevant reference, the proposed model provides with a promising intelligent decision support system to evaluate the road safety performance for a case study of a given set of European countries. It effectively handles experts’ linguistic expressions and takes the layered hierarchy of the indicators into account. The comparison results with those from the original hierarchical fuzzy TOPSIS model further verify the robustness of the proposed model, and imply the feasibility of applying this model to a great number of performance evaluation and decision making activities in other wide ranging fields as well.

Damage-Relevant Evaluation in Appraising Dangerous Buildings

Due to the relevance of item damages on a building, a power transmission mechanism that can reflect the damage relevance is introduced into the dangerous houses hierarchical fuzzy evaluation model (JGJ125-99). The proposed item description model uses a force-delivery tree to describe the relevant relationship among items. A new risk rating formula and a method to calculate the model parameters are addressed. The practical result by applying the approach to Ezhou city primary school in Hubei province shows that the item description model can reduce error judgments in appraising dangerous buildings.