Yafeng Gong

Temperature Effect on Mechanical Properties and Damage Identification of Concrete Structure

Static and dynamic mechanical properties of concrete are affected by temperature effect in practice. Therefore, it is necessary to investigate the corresponding influence law and mechanism. This paper demonstrates the variation of mechanical properties of concrete at temperatures from −20°C to 60°C. Temperature effects on cube compressive strength, splitting tensile strength, prism compressive strength, modulus of elasticity, and frequency are conducted and discussed. The results indicate that static mechanical properties such as compressive strength (cube and prism), splitting tensile strength, and modulus of elasticity have highly linear negative correlation with temperature; this law is also applied to the first order frequency of concrete slab. The coupling effect of temperature and damage on change rate of frequency reveals that temperature effect cannot be ignored in damage identification of structure. Mechanism analysis shows that variation of elastic modulus of concrete caused by temperature is the primary reason for the change of frequency.

Damage Identification of Urban Overpass Based on Hybrid Neurogenetic Algorithm Using Static and Dynamic Properties

Urban overpass is an important component of transportation system. Health condition of overpass is essential to guarantee the safe operation of urban traffic. Therefore, damage identification of urban overpass possesses important practical significance. In this paper, finite element model of left auxiliary bridge of Qianjin Overpass is constructed and vulnerable sections of structure are chosen as objects for damage recognition. Considering the asymmetry of Qianjin bridge, change rate of modal frequency and strain ratio are selected as input parameters for hybrid neurogenetic algorithm, respectively. Identification effects of damage location and severity are investigated and discussed. The results reveal that the proposed method can successfully identify locations and severities with single and multiple damage locations; its interpolation ability is better than extrapolation ability. Comparative analysis with BP neural network is conducted and reveals that the damage identification accuracy of hybrid neurogenetic algorithm is superior to BP. The effectiveness between dynamic and static properties as input variable is also analyzed. It indicates that the identification effect of strain ratios is more satisfactory than frequency ratio.

Damage Identification of Bridge Based on Chebyshev Polynomial Fitting and Fuzzy Logic without Considering Baseline Model Parameters

The paper presents an effective approach for damage identification of bridge based on Chebyshev polynomial fitting and fuzzy logic systems without considering baseline model data. The modal curvature of damaged bridge can be obtained through central difference approximation based on displacement modal shape. Depending on the modal curvature of damaged structure, Chebyshev polynomial fitting is applied to acquire the curvature of undamaged one without considering baseline parameters. Therefore, modal curvature difference can be derived and used for damage localizing. Subsequently, the normalized modal curvature difference is treated as input variable of fuzzy logic systems for damage condition assessment. Numerical simulation on a simply supported bridge was carried out to demonstrate the feasibility of the proposed method.

A fuzzy logic-based damage identification method for simply-supported bridge using modal shape ratios

Abstract A fuzzy logic system (FLS) is established for damage identification of simply supported bridge. A novel damage indicator is developed based on ratios of mode shape components between before and after damage. Numerical simulation of a simply-supported bridge is presented to demonstrate the memory, inference and anti-noise ability of the proposed method. The bridge is divided into eight elements and nine nodes, the damage indicator vector at characteristic nodes is used as the input measurement of FLS. Results reveal that FLS can detect damage of training patterns with an accuracy of 100%. Aiming at other test patterns, the FLS also possesses favorable inference ability, the identification accuracy for single damage location is up to 93.75%. Tests with noise simulated data show that the FLS possesses favorable anti-noise ability.

Reduction of Uncertainties for Damage Identification of Bridge Based on Fuzzy Nearness and Modal Data

To avoid the false results of deterministic identification methods induced by uncertainties, a fuzzy nearness-based method is proposed for the damage identification of bridge. An improved index based on ratios of modal shape components is used as identification measurements. The knowledge base for damage identification is established through corresponding relationship between fuzzified measurements and damage severities. The damage condition of test samples can be assessed based on approaching principle through fuzzy nearness with rules in knowledge base. A numerical analysis on a multigirder bridge considering uncertainty is presented to demonstrate the effectiveness of the proposed method. The results indicate that the fuzzy nearness-based method can achieve an accurate identification with success rate up to 93.75%. Antinoise analysis and the ability for dealing with incomplete information reveal its robustness.

Fuzzy Neural Network-Based Damage Assessment of Bridge under Temperature Effect

Vibration-based method has been widely applied for damage identification of bridge. Natural frequency, mode shape, and their derivatives are sensitive parameters to damage. However, these parameters can be affected not only by the health of structure, but also by the changing temperature. It is essential to eliminate the influence of temperature in practice. Therefore, a fuzzy neural network-based damage assessment method is proposed in this paper. Uniform load surface curvature is used as damage indicator. Elasticity modulus of concrete is assumed to be temperature dependent in the numerical simulation of bridge model. Through selecting temperature and uniform load surface curvature as input variables of fuzzy neural network, the algorithm can distinguish the damage from temperature effect. Comparative analysis between fuzzy neural network and BP network illustrates the superiority of the proposed method.

Comparative research on Superpave Gyratory Compaction method and Marshall Compaction method in the mix design of AC-16

Gradation designs of AC-16 dense-graded asphalt mixture are conducted separately using the Marshall Compaction method of China and Superpave Gyratory Compaction method of America. The indexes determined by these two design methods are compared and analyzed. The results show that the optimum asphalt content determined by Superpave Gyratory Compaction method is a little more than that determined by Marshall Compaction method, and Superpave Gyratory Compaction method is better than Marshall Compaction method in pavement performance such as dynamic stability, freeze-thaw split tensile strength ratio, residual stability and so on.

Low-Temperature, Anti-Cracking Performance of Superpave Asphalt Mixture

The mixture ratio design of asphalt mixture is based on the material design system of Superpave asphalt mixture. The purpose of using the MTS fatigue testing machine to do flexural test is to test its low-temperature, anti-cracking performance. At the same time, the performance indexes of asphalt mixture are compared with the performance indicators, which are determined by the Marshall compaction method. The results show that the optimum asphalt content designed by the material design system of Superpave asphalt mixture has reduced. The low-temperature, anti-cracking performance of asphalt mixture that is determined by Superpave asphalt mixture design system is better. The flexural strength R(B) and the maximum bending e(B) of the material design system of Superpave asphalt mixture is slightly less than that which is designed by Marshall Compaction method.

Damage identification of urban overpass based on modal frequency and genetic neural network

The finite element model of left auxiliary bridge of Qianjin Overpass is built and vulnerable sections of structure are chosen as research objects. In consideration of the asymmetry of the bridge, change rate of modal frequency is chosen as input parameter for genetic neural network, and identification ability of damage location and level is studied. The result shows that this method can successfully identify location of single damage and multi-damage; The error of damage level identification for test samples is less than 5% and the interpolation ability is better than the extrapolation ability. This indicates the method has good practice prospects.

Optimal Placement of Static Sensors Based on Damage Identification for Bridge Health Monitoring

Setting monitoring sensors in the bridge structure is a prerequisite for achieving health monitoring. It is necessary to optimize the layout of the sensors in order to obtain the most reliable and comprehensive information of health status of the bridge. Theoretical and practical experiment on optimal placement of static sensors for bridge health monitoring is carried out in the paper. An optimal placement method of static sensors that is based on damage identification is put forward and the corresponding algorithm is written for computing. It is proved that the method is efficient and ascendant through numerical examples. Then this optimal placement method is applied for the special-shaped bridge of a city overpass bridge. It shows that the method is simple and superior.