Application of uncertain type of AHP to condition assessment of cable-stayed bridges

In order to guarantee the safety service and life-span of long-span cable-stayed bridges, the uncertain type of analytic hierarchy process （AHP） method is adopted to access the bridge condition. The correlative theory and applied objects of uncertain type of AHP are introduced, and then the optimal transitive matrix method is chosen to calculate the interval number judgment matrix, which makes the weights of indices more reliable and accurate. Finally, with Harbin Songhua River Cable-Stayed Bridge as an example, an index system and an assessment model are proposed for the condition assessment of this bridge, and by using uncertain type of AHP, the weights of assessment indices are fixed and the final assessment results of the bridge are calculated, which proves the feasibility and practicability of this method. The application of this assessment method can provide the scientific basis for maintenance and management of long-span cable-stayed bridges.

Synthetical condition assessment of long span suspension bridge based on closeness degree and FAHP

Based on the theory of pattern recognition, the concept of closeness degree between fuzzy sets is brought into the condition assessment of long span bridges. Using the fuzzy analytic hierarchy process （FAHP）, a mathematical model of a multi-objective assessment of a long span suspension bridge is set up. An example is given to show the procedure in the synthetical condition assessment of the Runyang Suspension Bridge, which includes the hierarchical division, the definition of factor weights and fuzzy membership functions, and the calculation of closeness degrees, etc. The assessment combines both the data from the health monitoring system and the manual tests. The classification of evaluation items as well as the calculation of deterministic and nondeterministic items is presented. Compared with the traditional method of point rating, this method can better describe the discreteness of monitoring data and the fuzziness in the condition assessment.

Condition assessment of structures under unknown support excitation

A new method is proposed to assess the condition of structures under unknown support excitation by simultaneously detecting local damage and identifying the support excitation from several structural dynamic responses. The support excitation acting on a structure is modeled by orthogonal polynomial approximations, and the sensitivities of structural dynamic response with respect to its physical parameters and orthogonal coefficients are derived. The identification equation is based on Taylor＇s first order approximation, and is solved with the damped least-squares method in an iterative procedure. A fifteen-story shear building model and a five-story three-dimensional steel frame structure are studied to validate the proposed method. Numerical simulations with noisy measured accelerations show that the proposed method can accurately detect local damage and identify unknown support excitation from only several responses of the structure. This method provides a new approach for detecting structural damage and updating models with unknown input and incomplete measured output information.

Review Article on Condition Assessment of Structures Using Electro-Mechanical Impedance Technique

Structural health monitoring(SHM)is a process for determination of presence,location,severity of damages and remaining life of the infrastructures.SHM is widely applied in aerospace,mechanical and civil engineering sys-tems to assess the conditions of structures to improve the operation,safety,serviceability and reliability,respec-tively.There are various SHM techniques for monitoring the health of structures such as global response based and local techniques.Damages occur in the structures due to its inability to withstand intended design loadings,physical environment and chemical environment.Therefore,damage identification is necessary to improve the durability of the structures for protection against catastrophic failure.The research paper is focused on elec-tro-mechanical impedance(EMI)technique which is one of the techniques based on smart materials.The smart materials are utilized for monitoring the health of the structures.These are used for damage determination and its quantification for the interrogated structures.Variation in admittance or impedance signature shows the existence of damage in the structures.Furthermore,the different statistical methods viz.,root mean square deviation(RMSD),mean absolute percentage deviation(MAPD),covariance(Cov),and correlation coefficient(CC)are used for the quantification of damage.Smart material such as piezoelectric materials,its properties and applica-tions are also considered.In this paper,the implementation of EMI technique based on different recent advances in smart materials and their appropriateness have been described.Subsequently,the reviewed investigations are significant for the monitoring of real-life infrastructures.The presented paper is the compact state-of-the-art for EMI technique which is used for SHM.This examination will be valuable to infrastructural health monitoring and engineering applications in respect to innovative research directions.

Color ImageBased Defect Detection Method for Bridge Coating Assessment

This paper presents a highly efficient method for recognizing the existence of bridge coating rust defects by using color image processing. The detection of defects on steel bridge surfaces during the operation and maintenance of bridge structures is important to ensure the safety and reliability of them. More advanced techniques such as digital image processing have been studied for better monitoring and detection as existing infrastructure systems are aged and deteriorated rapidly. Recently, image-based defect recognition and assessment methods have gained considerable attention in the civil engineering domain due to their accuracy, speed, and lower cost. The proposed method in this paper is a fast decision-making system by utilizing color image processing. It was developed by processing original bridge coating images to generate color values and calculating eigenvalues from each digitized image. The values from two different groups, a defective group and a nondefective group, are compared each other to figure out the feasibility of this approach. Finally, an automated defect recognition method is presented and tested with more images. This method can be used to make a decision whether a given digitized image contains defects.

WATERiD＇s Novel Methodology for Condition Assessment Cost Data Collection and Visualization

A profound understanding of the costs to perform condition assessment on buried drinking water pipeline infrastructure is required for enhanced asset management. Toward this end, an automated and uniform method of collecting cost data can provide water utilities a means for viewing, understanding, interpreting and visualizing complex geographically referenced cost information to reveal data relationships, patterns and trends. However, there has been no standard data model that allows automated data collection and interoperability across platforms. The primary objective of this research is to develop a standard cost data model for drinking water pipeline condition assessment projects and to conflate disparate datasets from differing utilities. The capabilities of this model will be further demonstrated through performing trend analyses. Field mapping files will be generated from the standard data model and demonstrated in an interactive web map created using Google Maps API （application programming interface） for JavaScript that allows the user to toggle project examples and to perform regional comparisons. The aggregation of standardized data and further use in mapping applications will help in providing timely access to condition assessment cost information and resources that will lead to enhanced asset management and resource allocation for drinking water utilities.

Assessment of Structural Damage Condition Based on Fuzzy Pattern Recognition

This paper presents a new method of damage condition assessment that allows accommodating other types of uncertainties due to ambiguity, vagueness, and fuzziness that are statistically nondescribable. In this method, healthy observations are used to construct a fury set representing sound performance characteristics. Additionally, the bounds on the similarities among the structural damage states are prescribed by using the state similarity matrix. Thus, an optimal group fuzzy sets representing damage states such as little, moderate, and severe damage can be inferred as an inverse problem from healthy observations only. The optimal group of damage fuzzy sets is used to classify a set of observations at any unknown state of damage using the principles of fitzzy pattern recognition based on an approximate principle . This method can be embedded into the system of Structural Health Monitoring （SHM） to give advice about structural maintenance and life predictio comes from Reference [ 9 ] for damage pattern recognition is presented n. Finally, a case and discussed. The study, which compared result illustrates our method is more effective and general, so it is very practical in engineering.

Fast prediction of the mechanical response for layered pavement under instantaneous large impact based on random forest regression

The layered pavements usually exhibit complicated mechanical properties with the effect of complex material properties under external environment.In some cases,such as launching missiles or rockets,layered pavements are required to bear large impulse load.However,traditional methods cannot non-destructively and quickly detect the internal structural of pavements.Thus,accurate and fast prediction of the mechanical properties of layered pavements is of great importance and necessity.In recent years,machine learning has shown great superiority in solving nonlinear problems.In this work,we present a method of predicting the maximum deflection and damage factor of layered pavements under instantaneous large impact based on random forest regression with the deflection basin parameters obtained from falling weight deflection testing.The regression coefficient R^(2)of testing datasets are above 0.94 in the process of predicting the elastic moduli of structural layers and mechanical responses,which indicates that the prediction results have great consistency with finite element simulation results.This paper provides a novel method for fast and accurate prediction of pavement mechanical responses under instantaneous large impact load using partial structural parameters of pavements,and has application potential in non-destructive evaluation of pavement structure.

Application of Serum Apelin and MMP-9 in Evaluation of Silicosis and Their Correlation with Lung Function

[Objectives]This study was conducted to explore the combination of serum Apelin and MMP-9 in the assessment of silicosis and their correlation with lung function. [Methods] From January 2020 to January 2021, eight patients with silicosis(including 3, 2 and 3 patients with silicosis in stages I, II and III respectively) were selected as the observation group, and eight persons frees of silicosis were selected as the control group during the same period. All patients were detected for serum APJ endogenous ligand(Apelin) of G protein-coupled receptor, matrix metalloproteinase-9(MMP-9) and pulmonary function indexes. The levels of serum Apelin and MMP-9 and lung function in the two groups were compared, and the correlation between serum Apelin and MMP-9 indexes and lung function was analyzed. [Results] The serum Apelin and lung function in the observation group were lower than those in the control group, and the level of MMP-9 in the observation group was higher than that in the control group, and the differences were statistically significant(P<0.05). The MMP-9 index in patients with silicosis in stage III was higher than those in patients with silicosis in stage II and stage I, and the MMP-9 index in patients with silicosis in stage II was higher than that in patients with silicosis in stage I, and the differences were statistically significant(P<0.05). The serum Apelin and lung function indexes of patients with silicosis in stage III were lower than those of patients with silicosis in stage II and stage I, and the serum Apelin and lung function indexes of patients with silicosis in stage II were lower than those of patients with silicosis in stage I, and the differences were statistically significant(P<0.05). The correlation analysis showed a positive correlation between serum Apelin and lung function indexes, and a negative correlation between MMP-9 and lung function indexes. [Conclusions] A lower serum Apelin and a higher MMP-9 value in patients with silicosis indicate severer lung function impairment, and the combined detection of serum Apelin and MMP-9 is conducive to effective evaluation of the disease of silicosis. The serum Apelin index was negatively correlated with silicosis, and the level of MMP-9 was positively correlated with silicosis, which could provide a scientific basis for clinical diagnosis and treatment.

Monte Carlo numerical simulation and its application in probability analysis of long span bridges

To get the probability of long span bridges under the influence of external random factors, the Monte Carlo method using Latin hypercube sampling is applied. Combined with the condition assessment system on Runyang Suspension Bridge, which is the longest suspension bridge in China, the structural probabilities in normal and damaged situations are calculated with the external random factors considered including environmental temperature, wind load, load of vehicles, etc. The main assessment items contain the maximal vertical displacement of girder, the maximal stress of cables, the maximal horizontal displacement of towers etc. Finally, the probabilities and their cumulative distribution functions are provided. The analysis results can be plotted on line in a clear and vivid way, so the efficiency of assessment is increased and the decision-making of maintenance is more objective and accurate.

Designand development of highway tunnel maintenance and management system

In order to solve the urgent problem of how to manage and sustain highway tunnels with advanced information technology with the background of the rapid development in the modem traffic, and achieve the cost- effectiveness optimal principle objectives under the premise of guaranteeing a smooth flow of traffic; a highway tunnel maintenance and management system framework and the key modules were proposed. First, the determined highway tunnel condition assessment index system was established according to the result of expert consulting forms. Secondly, the tunnel diseases, the corresponding maintenance measurements, and many-to-many relationship between diseases and maintenance measurements were introduced. Then, three kinds of 0-1 integer programming models were built according to different tunnel operators＇ needs in the optimization decision module. Finally, the further development and implementation of the system was prospected. The research results can provide references to tunnel researchers and managers.

Finite element model updating of existing steel bridge based on structural health monitoring

Based on the physical meaning of sensitivity,a new finite element(FE) model updating method was proposed. In this method,a three-dimensional FE model of the Nanjing Yangtze River Bridge(NYRB) with ANSYS program was established and updated by modifying some design parameters. To further validate the updated FE model,the analytical stress-time histories responses of main members induced by a moving train were compared with the measured ones. The results show that the relative error of maximum stress is 2.49% and the minimum relative coefficient of analytical stress-time histories responses is 0.793. The updated model has a good agreement between the calculated data and the tested data,and provides a current baseline FE model for long-term health monitoring and condition assessment of the NYRB. At the same time,the model is validated by stress-time histories responses to be feasible and practical for railway steel bridge model updating.

裂纹和污秽瓷绝缘子的电磁场辐射信号特征(英文)

Porcelain cap and pin insulators are by far the most popular suspension insulators in high voltage(HV) distribution networks all around the world.Inspection and condition monitoring of HV insulators is also very important to the utility companies because of the critical and vital role that they play in distribution systems.In terms of safety, practicality and ease of use,remote detection methods are more popular among the line technicians.A new remote condition assessment method based on electromagnetic radiations from porcelain insulators is presented in this paper. In a lab environment,a string of two porcelain insulators is energized by a 45 kV transformer.Electromagnetic radiations due to the partial discharge activities inside the porcelain insulator are captured by passive sensors and antennas. Two cases of defective insulators on a two insulator string are studied here.The first case focuses on the effect of contaminated porcelain shells on radiation signature of partial discharges.A polluted porcelain shell with ESDD level of 0.03 mg/cm^2 was first tested.The second case studies the effect of cracks of an intentionally-cracked porcelain shell. The cracked insulator is also tested on a two insulator string.To compare the partial discharge radiation signature of different faulty insulators,phase resolved graphs were developed.The electromagnetic radiated signature of a polluted insulator and a cracked insulator was calculated and compared using phase resolved graphs.

Digital Image Processing Methods for Bridge Coating Management and Their Limitations

Accurate and objective rust defect assessment is required to maintain good quality steel bridge coating surfaces and make a decision whether a bridge shall completely or partially be repainted. For more objective rust defect recognition, digital image recognition methods have been developed for the past few years and they are expected to replace or complement conventional painting inspection methods. Efficient image processing methods are also essential for the successful implementation of steel bridge coating warranty contracting where the owner, usually a state agency, and the contractor inspect steel bridge coating conditions regularly and decide whether additional maintenance actions are needed based on the processed data. There are two approaches to develop automated rust defect recognition methods： applying a statistical method or an artificial intelligence technique. This paper presents the application of previously developed image processing methods for defect evaluations on a bridge coating surface and discusses their limitations under three environmental conditions which are often encountered while acquiring digital images.

A comparison of several indices for assessing body condition of Mongolian gazelle

Riney kidney fat index (RKFI), whole kidney fat index (WKFI), femur marrow fat index (FMFI), and tibia marrow fat index (TMFI) of 51 Mongolian gazelles (Procapra gutturosa), collected in Hulunbeier grassland, Inner Mongolian, China, were measured during spring, autumn and winter in 1997-98. These different indexes were compared for using them in assessing the body condition. There was a linear relationship (y=0.9444x-20.139; r=0.9454; p<0.01) between RKFI and KMFI. A linear relationship (y=0.9348x+1.1843; r=0.9875; P<0.01) between TMFI and FMFI also occurred for gazelles. There was a curvilinear relationship (y=31.44Ln(x) -44.403; r=0.8643; P<0.01) between FMFI and RKFI. FMFI remained high, while RKFI decreased to a certain extent. After most of the kidney fat was used, the femur marrow fat abruptly decreased. The results showed that the kidney fat index is more adequate for evaluating the population nutrition in good condition, but marrow fat index was more useful for assessing in poorer nutritional condition.

A Methodology for Bridge Condition Evaluation

Due to the substantial role of bridges in transportation networks and in accordance with the limited funding for bridge management, remediation strategies have to be prioritised. A conservative bridge assessment will result in unnecessary actions, such as costly bridge strengthening or repairs. On the other hand, any bridge maintenance negligence and delayed actions may lead to heavy future costs or degraded assets. The accuracy of decisions developed by any manager or bridge engineer relies on the accuracy of the bridge condition assessment which emanates from visual inspection. Many bridge rating systems are based on a very subjective procedure and are associated with uncertainty and personal bias. The developing condition rating method described herein is an important step in adding more holism and objectivity to the current approaches. Structural importance and material vulnerability are the two main factors that should be considered in the evaluation of element structural index and the causal factor as the representative of age, environment, road class and inspection is implemented as a coefficient to the OSCI （overall structural condition index）. The AHP （analytical hierarchy process） has been applied to evaluate the priority vector of the causal parameters.

GEOCHEMISTRY STUDY OF GOLD SILVER DEPOSITS IN THE MIDDLE UPPER PROTEROZOIC SUBERATHEM IN SOUTHERN QINLING METALLOGENETIC ZONE

According to Ihe combinations of mctallogcnetic elements and minerals assemblages,the Au-Ag deposits in the Middle-Upper Proterozoic Suberathcm of the middle of northern margin of Yangtze Platform could be classified into four types,(l)Au-Ag-Pb-Zn type,(2)Au-Ag-Te type,(3)Au-quartz vein type,(4)Au-Ag-Pb-Zn-Ba type.The Yangpin formation and the upper Dangyuhe subformation,which belong to Wudangshan group,are regarded as favorable strata for Au-Ag mineralization by systematic assessments for Au-Ag bearing ability of the strata,as well as the felsic rocks of Bikou group.The mctallogcnetic physicochemical conditions and the stable isotopic compositions(S.Pb,H.O,C)have been studied in this paper.The sources of metallogenetic materials,origins of fluids and genesis of various deposits have also been studied.

Analysis and assessment of bridge health monitoring mass data——progress in research/development of “Structural Health Monitoring”

The "Structural Health Monitoring" is a project supported by National Natural Science Foundation for Distinguished Young Scholars of China(Grant No.50725828).To meet the urgent requirements of analysis and assessment of mass monitoring data of bridge environmental actions and structural responses,the monitoring of environmental actions and action effect modeling methods,dynamic performance monitoring and early warning methods,condition assessment and operation maintenance methods of key members are systematically studied in close combination with structural characteristics of long-span cable-stayed bridges and suspension bridges.The paper reports the progress of the project as follows.(1) The environmental action modeling methods of long-span bridges are established based on monitoring data of temperature,sustained wind and typhoon.The action effect modeling methods are further developed in combination with the multi-scale baseline finite element modeling method for long-span bridges.(2) The identification methods of global dynamic characteristics and internal forces of cables and hangers for long-span cable-stayed bridges and suspension bridges are proposed using the vibration monitoring data,on the basis of which the condition monitoring and early warning methods of bridges are developed using the environmental-condition-normalization technique.(3) The analysis methods for fatigue loading effect of welded details of steel box girder,temperature and traffic loading effect of expansion joint are presented based on long-term monitoring data of strain and beam-end displacement,on the basis of which the service performance assessment and remaining life prediction methods are developed.

A structural health assessment method for shield tunnels based on torsional wave speed

Following recent rapid developments in tunnel engineering in China,the heavy structural maintenance work of the future is likely to pose a great challenge.Newly developed vibration-based health assessment and monitoring methods offer good prospects for large-scale structural monitoring,hidden surface detection and disease pre-judgment.However,because the dynamic properties of tunnels are sensitive to the coupling and damping effects of the surrounding soil,there is little relevant research on tunnel structures.Using the PiP(pipe in pipe)model,the intrinsic tunnel modes and their response characteristics are investigated in this paper,and the degree to which the identification of these characteristics is influenced by mode superposition and the soil coupling effect are also considered.The response features of these flexible wave modes are found to be barely recognizable in a tunnel-soil coupled system,while the phase velocity of the torsional wave can be determined by combining phase spectrum analysis and the HHT(Hilbert-Huang transformation)method.A new structural health assessment method based on the torsional wave speed is therefore proposed.In this method,the torsional wave speed is used to determine the tunnel structure’s global stiffness based on a newly developed dispersion algorithm.The calculated stiffness is then used to evaluate the tunnel’s structural service status.A field test was also carried out at a newly built tunnel to validate the proposed method;the tunnel structure’s Young’s modulus was obtained and was very close to the designed value.This indicates that this method is an effective way to assess tunnel service conditions,and also provides a theoretical basis for future applications to health assessment of shield tunnels.

Power transmission risk assessment considering component condition

This paper proposes a new method for power transmission risk assessment considering historical failure statistics of transmission systems and operation failure risks of system components.Component failure risks are integrated into the new method based on operational condition assessment of components using the support vector data description(SVDD)approach.The traditional outage probability model of transmission lines has been modified to build a new framework for power transmission system risk assessment.The proposed SVDD approach can provide a suitable mechanism to map component assessment grades to failure risks based on probabilistic behaviors of power system failures.Under the new method,both up-todate component failure risks and traditional system risk indices can be processed with the proposed outage model.As a result,component failure probabilities are not only related to historical statistic data but also operational data of components,and derived risk indices can reflect current operational conditions of components.In simulation studies,the SVDD approach is employed to evaluate component conditions and link such conditions to failure rates using up-to-date component operational data,including both on-line and off-line data of components.The IEEE 24-bus RTS-1979 system is used to demonstrate that component operational conditions can greatly affect the overall transmission system failure risks.