Hanbing Liu

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.

Experimental Investigation of the Mechanical and Durability Properties of Crumb Rubber Concrete

Recycling waste tire rubber by incorporating it into concrete has become the preferred solution to dispose of waste tires. In this study, the effect of the volume content of crumb rubber and pretreatment methods on the performances of concrete was evaluated. Firstly, the fine aggregate and mixture were partly replaced by crumb rubber to produce crumb rubber concrete. Secondly, the mechanical and durability properties of crumb rubber concrete with different replacement forms and volume contents had been investigated. Finally, the crumb rubber after pretreatment by six modifiers was introduced into the concrete mixture. Corresponding tests were conducted to verify the effectiveness of pretreatment methods as compared to the concrete containing untreated crumb rubber. It was observed that the mechanical strength of crumb rubber concrete was reduced, while durability was improved with the increasing of crumb rubber content. 20% replacement of fine aggregate and 5% replacement of the total mixture exhibited acceptable properties for practical applications. In addition, the results indicated that the modifiers had a positive impact on the mechanical and durability properties of crumb rubber concrete. It avoided the disadvantage of crumb rubber concrete having lower strength and provides a reference for the production of modified crumb rubber concrete.

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.

Unsupervised Performance Evaluation Strategy for Bridge Superstructure Based on Fuzzy Clustering and Field Data

Performance evaluation of a bridge is critical for determining the optimal maintenance strategy. An unsupervised bridge superstructure state assessment method is proposed in this paper based on fuzzy clustering and bridge field measured data. Firstly, the evaluation index system of bridge is constructed. Secondly, a certain number of bridge health monitoring data are selected as clustering samples to obtain the fuzzy similarity matrix and fuzzy equivalent matrix. Finally, different thresholds are selected to form dynamic clustering maps and determine the best classification based on statistic analysis. The clustering result is regarded as a sample base, and the bridge state can be evaluated by calculating the fuzzy nearness between the unknown bridge state data and the sample base. Nanping Bridge in Jilin Province is selected as the engineering project to verify the effectiveness 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.

Effect of Temperature Variation on Modal Frequency of Reinforced Concrete Slab and Beam in Cold Regions

Changes of modal frequencies induced by temperature variation can be more obvious than those caused by structural damage, which will lead to the false damage identification results. Therefore, quantifying the temperature effect on modal frequencies is a critical step to eliminate its interference in damage detection. Due to the nonuniform and time-dependent characteristics of temperature distribution, it is insufficient to obtain the reliable relationships between temperatures and modal frequencies using temperatures in air or at surface. In this paper, correlations between measured temperatures (air temperature, surface temperature, mean temperature, etc.) and modal frequencies for the slab and beam are comparatively analyzed. And the quantitative models are constructed considering nonuniform temperature distribution. Firstly, the reinforced concrete slab and beam were constructed and placed outside the laboratory to be monitored. Secondly, the correlation coefficients between modal frequencies and three kinds of temperatures are calculated, respectively. Thirdly, simple linear regression models between mean temperature and modal frequencies are established for the slab and beam. Finally, five temperature variables are selected to construct the multiple linear regression models. Prediction results reveal that the proposed multiple linear regression models possess favorable accuracy to quantify the temperature effect on modal frequencies considering nonuniform temperature distribution.

Damage Identification of Bridge Based on Modal Flexibility and Neural Network Improved by Particle Swarm Optimization

An approach to identify damage of bridge utilizing modal flexibility and neural network optimized by particle swarm optimization (PSO) is presented. The method consists of two stages; modal flexibility indices are applied to damage localizing and neural network optimized by PSO is used to identify the damage severity. Numerical simulation of simply supported bridge is presented to demonstrate feasibility of the proposed method, while comparative analysis with traditional BP network is for its superiority. The results indicate that curvature of flexibility changes can identify damages with both single and multiple locations. The optimization of bias and weight for neural network by fitness function of PSO algorithm can realize favorable damage severity identification and possesses more satisfactory accuracy than traditional BP network.

Condition evaluation for existing reinforced concrete bridge superstructure using fuzzy clustering improved by particle swarm optimisation

Abstract Condition evaluations of old bridges are necessary for determining their health states and providing priority levels of maintenance. In this paper, a novel condition evaluation approach for reinforced concrete (RC) bridge superstructure is presented based on fuzzy c-mean clustering optimised by particle swarm optimisation (FCM-PSO) algorithm. It is equipped with the advantages of PSO algorithm in global optimisation and FCM algorithm in convergence acceleration, which greatly improves the effectiveness of clustering. Using this methodology, a reliable evaluation index system and a number of training samples from field-measured data of existing old bridges are prerequisites. In addition, the optimal cluster number for training samples can be determined by Xie–Beni validity evaluation index. Subsequently, condition grades and corresponding cluster centres can be determined based on the calculation of cluster centres and membership matrix for training samples. On the above basis, bridge conditions of testing samples can be evaluated based on the fuzzy membership to the cluster centres of condition grades. A case study was carried out to verify the feasibility and effectiveness of the proposed FCM-PSO method. Evaluation results reveal that the proposed method can effectively reduce the influence of subjective factors and will be favourable for condition evaluation of existing RC bridges.