Techno Press
Tp_Editing System.E (TES.E)
Login Search


sss
 
CONTENTS
Volume 17, Number 6, June 2016
 

Abstract
.

Key Words
.

Address
.

Abstract
This study aims to establish an effective methodology for the detection of instant damages occurred in cable-stayed bridges with the measurements of cable vibration and structural temperatures. A transfer coefficient for the daily temperature variation and another for the long-term temperature variation are firstly determined to eliminate the environmental temperature effects from the cable force variation. Several thresholds corresponding to different levels of exceedance probability are then obtained to decide four upper criteria and four lower criteria for damage detection. With these criteria, the monitoring data for three stay cables of Ai-Lan Bridge are analyzed and compared to verify the proposed damage detection methodology. The simulated results to consider various damage scenarios unambiguously indicate that the damages with cable force changes larger than +-1.5% can be detected within 12 hours. Even though not exhaustively reflecting the environmental temperature effects on the cable force variation, both the effective temperature and the air temperature can be considered as valid indices to eliminate these effects at high and low monitoring costs.

Key Words
cable-stayed bridge; cable force; effective temperature; daily variation; long-term variation; transfer coefficient; damage detection; air temperature

Address
Chien-Chou Chen, Wen-Hwa Wu and Gwolong Lai: Department of Construction Engineering, National Yunlin University of Science and Technology, 123 University Road, Touliu, Yunlin 640, Taiwan
Chun-Yan Liu: Graduate School of Engineering Science and Technology, National Yunlin University of Science and Technology, 123 University Road, Touliu, Yunlin 640, Taiwan


Abstract
In this study, a method to compensate the effect of temperature variation on impedance responses which are used for prestress-loss monitoring in prestressed concrete (PSC) girders is presented. Firstly, an impedance-based technique using a mountable lead-zirconate-titanate (PZT) interface is presented for prestress-loss monitoring in the local tendon-anchorage member. Secondly, a cross-correlation-based algorithm to compensate the effect of temperature variation in the impedance signatures is outlined. Thirdly, lab-scale experiments are performed on a PSC girder instrumented with a mountable PZT interface at the tendon-anchorage. A series of temperature variation and prestress-loss events are simulated for the lab-scale PSC girder. Finally, the feasibility of the proposed method is experimentally verified for prestress-loss monitoring in the PSC girder under temperature-varying conditions and prestress-loss events.

Key Words
impedance monitoring, mountable PZT interface; prestress force monitoring; tendon-anchorage; PSC girders; temperature effect; temperature compensation

Address
Thanh-Canh Huynh and Jeong-Tae Kim: Department of Ocean Engineering, Pukyong National University, 599-1 Daeyeon 3-dong, Nam-gu, Busan 608-737, Republic of Korea


Abstract
The classical Kalman filter (KF) provides a practical and efficient state estimation approach for structural identification and vibration control. However, the classical KF approach is applicable only when external inputs are assumed known. Over the years, some approaches based on Kalman filter with unknown inputs (KF-UI) have been presented. However, these approaches based solely on acceleration measurements are inherently unstable which leads poor tracking and so-called drifts in the estimated unknown inputs and structural displacement in the presence of measurement noises. Either on-line regularization schemes or post signal processing is required to treat the drifts in the identification results, which prohibits the real-time identification of joint structural state and unknown inputs. In this paper, it is aimed to extend the classical KF approach to circumvent the above limitation for real time joint estimation of structural states and the unknown inputs. Based on the scheme of the classical KF, analytical recursive solutions of an improved Kalman filter with unknown excitations (KF-UI) are derived and presented. Moreover, data fusion of partially measured displacement and acceleration responses is used to prevent in real time the so-called drifts in the estimated structural state vector and unknown external inputs. The effectiveness and performance of the proposed approach are demonstrated by some numerical examples.

Key Words
Kalman filter; unknown inputs; input estimation; response prediction; data fusion

Address
Lijun Liu, Jiajia Zhu, Ying Su and Ying Lei:Department of Civil Engineering, Xiamen University, Xiamen 361005, China

Abstract
In a prestressed concrete bridge, the magnitude of the prestress force (PF) decreases with time. This unexpected loss can cause failure of a bridge which makes prestress force identification (PFI) critical to evaluate bridge safety. However, it has been difficult to identify the PF non-destructively. Although some research has shown the feasibility of vibration based methods in PFI, the requirement of having a determinate exciting force in these methods hinders applications onto in-service bridges. Ideally, it will be efficient if the normal traffic could be treated as an excitation, but the load caused by vehicles is difficult to measure. Hence it prompts the need to investigate whether PF and moving load could be identified together. This paper presents a synergic identification method to determine PF and moving load applied on a simply supported prestressed concrete beam via the dynamic responses caused by this unknown moving load. This method consists of three parts: (i) the PF is transformed into an external pseudo-load localized in each beam element via virtual distortion method (VDM); (ii) then these pseudo-loads are identified simultaneously with the moving load via Duhamel Integral; (iii) the time consuming problem during the inversion of Duhamel Integral is overcome by the load-shape function (LSF). The method is examined against different cases of PFs, vehicle speeds and noise levels by means of simulations. Results show that this method attains a good degree of accuracy and efficiency, as well as robustness to noise.

Key Words
prestress force identification (PFI); moving load identification; synergic identification; virtual distortion method (VDM)

Address
Ziru Xiang, Tommy H.T. Chan, David P. Thambiratnam and Theanh Nguyen: School of Civil Engineering and Built Environment, Queensland University of Technology (QUT),
2 George St, Brisbane, Queensland 4000, Australia


Abstract
With the help of advanced image acquisition and processing technology, the vision-based measurement methods have been broadly applied to implement the structural monitoring and condition identification of civil engineering structures. Many noncontact approaches enabled by different digital image processing algorithms are developed to overcome the problems in conventional structural dynamic displacement measurement. This paper presents three kinds of image processing algorithms for structural dynamic displacement measurement, i.e., the grayscale pattern matching (GPM) algorithm, the color pattern matching (CPM) algorithm, and the mean shift tracking (MST) algorithm. A vision-based system programmed with the three image processing algorithms is developed for multi-point structural dynamic displacement measurement. The dynamic displacement time histories of multiple vision points are simultaneously measured by the vision-based system and the magnetostrictive displacement sensor (MDS) during the laboratory shaking table tests of a three-story steel frame model. The comparative analysis results indicate that the developed vision-based system exhibits excellent performance in structural dynamic displacement measurement by use of the three different image processing algorithms. The field application experiments are also carried out on an arch bridge for the measurement of displacement influence lines during the loading tests to validate the effectiveness of the vision-based system.

Key Words
structural health monitoring; displacement measurement; vision-based system; digital image processing; pattern matching algorithm; mean shift tracking algorithm

Address
X.W. Ye, C.Z. Dong and T. Liu: Department of Civil Engineering, Zhejiang University, Hangzhou 310058, China

Abstract
Structural damage detection (SDD) is a challenging task in the flied of structural health monitoring (SHM). As an exploring attempt to the SDD problem, a hybrid self-adaptive Firefly-Nelder-Mead (SA-FNM) algorithm is proposed for the SDD problem in this study. First of all, the basic principle of firefly algorithm (FA) is introduced. The Nelder-Mead (NM) algorithm is incorporated into FA for improving the local searching ability. A new strategy for exchanging the information in the firefly group is introduced into SA-FNM for reducing the computation cost. A random walk strategy for the best firefly and a self-adaptive control strategy of three key parameters,such as light absorption, randomization parameter and critical distance, are proposed for preferably balancing the exploitation and exploration ability of the SA-FNM. The computing performance of the SA-FNM is evaluated and compared with the basic FA by three benchmark functions. Secondly, the SDD problem is mathematically converted into a constrained optimization problem, which is then hopefully solved by the SA-FNM algorithm. A multi-step method is proposed for finding the minimum fitness with a big probability. In order to assess the accuracy and the feasibility of the proposed method, a two-storey rigid frame structure without considering the finite element model (FEM) error and a steel beam with considering the model error are taken examples for numerical simulations. Finally, a series of experimental studies on damage detection of a steel beam with four damage patterns are performed in laboratory. The illustrated results show that the proposed method can accurately identify the structural damage. Some valuable conclusions are made and related issues are discussed as well.

Key Words
structural damage detection (SDD); firefly algorithm; Nelder-Mead algorithm; self-adaptive; finite element model (FEM)

Address
Chu-Dong Pan, Ze-Peng Chen, Wen-Feng Luo and Huan-Lin Liu: Department of Mechanics and Civil Engineering, Jinan University, Guangzhou 510632, China;
Ling Yu: Department of Mechanics and Civil Engineering, Jinan University, Guangzhou 510632, China;
MOE Key Lab of Disaster Forecast and Control in Engineering, Jinan University, Guangzhou 510632, China


Abstract
Two field research stations based upon atmospheric corrosivity monitoring combined with reinforced concrete corrosion rate sensors have been established in Kuwait. This was established for the purpose of remote monitoring of building materials performance for concrete under Kuwait atmospheric environment. The two field research sites for concrete have been based upon an outcome from a research investigation intended for monitoring the atmospheric corrosivity from weathering station distributed in eight areas, and in different regions in Kuwait. Data on corrosivity measurements are essential for the development of specification of an optimized corrosion resistance system for reinforced concrete manufactured products. This study aims to optimize, characterize, and utilize long-term concrete structural health monitoring through on line corrosion measurement and to determine the feasibility and viability of the integrated anode ladder corrosion sensors embedded in concrete. The atmospheric corrosivity categories supported with GSM remote data acquisition system from eight corrosion monitoring stations at different regions in Kuwait are being classified according to standard ISO 9223. The two nominated field sites where based upon time of wetness and bimetallic corrosion rate from atmospheric data where metals and rebar\'s concrete are likely to be used. Eight concrete blocks with embeddable anodic ladder corrosion sensors were placed in the atmospheric zone adjacent to the sea shore at KISR site. The anodic ladder corrosion rate sensors for concrete were installed to provide an early warning system on prediction of the corrosion propagation and on developing new insights on the long‐term durability performance and repair of concrete structures to lower labor cost. The results show the atmospheric corrosivity data of the environment and the feasibility of data retrieval of the corrosion potential of concrete from the embeddable sets of anodic ladder corrosion sensors.

Key Words
field research station; atmospheric corrosivity sensors; corrosion sensors for concrete

Address
A. Husain, Suad Kh. Al-Bahar and Safaa A. Abdul Salam: Department of Kuwait Institute for Scientific Research (KISR),Construction and Building Materials (CBM),
Energy and Building Center (EBRC), PO BOX 24885, 13109 SAFAT, Kuwait

Abstract
In the design and condition assessment of bridges, it is usually necessary to take into consideration the extreme conditions which are not expected to occur within a short time period and thus require an extrapolation from observations of limited duration. Long-term structural health monitoring (SHM) provides a rich database to evaluate the extreme conditions. This paper focuses on the extrapolation of extreme traffic load effects on bridges using long-term monitoring data of structural strain. The suspension Tsing Ma Bridge (TMB), which carries both highway and railway traffic and is instrumented with a long-term SHM system, is taken as a testbed for the present study. Two popular extreme value extrapolation methods: the block maxima approach and the peaks-over-threshold approach, are employed to extrapolate the extreme stresses induced by highway traffic and railway traffic, respectively. Characteristic values of the extreme stresses with a return period of 120 years (the design life of the bridge) obtained by the two methods are compared. It is found that the extrapolated extreme stresses are robust to the extrapolation technique. It may owe to the richness and good quality of the long-term strain data acquired. These characteristic extremes are also compared with the design values and found to be much smaller than the design values, indicating conservative design values of traffic loading and a safe traffic-loading condition of the bridge. The results of this study can be used as a reference for the design and condition assessment of similar bridges carrying heavy traffic, analogous to the TMB.

Key Words
traffic load effects; extreme value; structural health monitoring; design validation; condition assessment

Address
Y.X. Xia and Y.Q. Ni: Department of Civil and Environmental Engineering, The Hong Kong Polytechnic University,
Hung Hom, Kowloon, Hong Kong


Abstract
This study focuses on the system identification of reinforced concrete bridges using vector autoregressive model (VAR). First, the time series output response from a bridge establishes the autoregressive (AR) models. AR models are one of the most accurate methods for stationary time series. Burg\'s algorithm estimates the autoregressive coefficients (ARCs) at p-lag by reducing the sum of the forward and the backward errors. The computed ARCs are assembled in the state system matrix and the eigen-system realization algorithm (ERA) computes: the eigenvector matrix that contains the vectors of the mode shapes, and the eigenvalue matrix that contains the associated natural frequencies. By taking advantage of the characteristic of the AR model with ERA (ARMERA), civil engineering can address problems related to damage detection. Operational modal analysis using ARMERA is applied to three experiments. One experiment is coupled with an artificial neural network algorithm and it can detect damage locations and extension. The neural network uses a specific number of ARCs as input and multiple submatrix scaling factors of the structural stiffness matrix as output to represent the damage.

Key Words
system identification; autoregressive model; Burg

Address
Kyeongtaek Park and Marco Torbol: School of Urban and Environmental Engineering, Ulsan National Institute of Science and Technology, 50 UNIST-gil, Eonyang-eup, Ulju-gun, Ulsan 44919, Republic of Korea
Sehwan Kim: Department of Biomedical Engineering, College of Medicine, Dankook University, 119, Dandae-ro, Dongnam-gu, Cheonan-si, Chungnam 31116, Republic of Korea



Abstract
The health conditions of in-service civil infrastructures can be evaluated by employing structural health monitoring technology. A reliable health evaluation result depends heavily on the quality of the data collected from the structural monitoring sensor network. Hence, the problem of sensor fault diagnosis has gained considerable attention in recent years. In this paper, an innovative sensor fault diagnosis method that focuses on fault detection and isolation stages has been proposed. The dynamic or auto-regressive characteristic is firstly utilized to build a multivariable statistical model that measures the correlations of the currently collected structural responses and the future possible ones in combination with the canonical correlation analysis. Two different fault detection statistics are then defined based on the above multivariable statistical model for deciding whether a fault or failure occurred in the sensor network. After that, two corresponding fault isolation indices are deduced through the contribution analysis methodology to identify the faulty sensor. Case studies, using a benchmark structure developed for bridge health monitoring, are considered in the research and demonstrate the superiority of the new proposed sensor fault diagnosis method over the traditional principal component analysis-based and the dynamic principal component analysis-based methods.

Key Words
structural health monitoring; sensor fault diagnosis; canonical correlation analysis; dynamic or auto-regressive characteristic; contribution analysis

Address
Hai-Bin Huang and Ting-Hua Yi:School of Civil Engineering, Dalian University of Technology, Dalian 116023, China
Hong-Nan Li: School of Civil Engineering, Dalian University of Technology, Dalian 116023, China;
School of Civil Engineering, Shenyang Jianzhu University, Shenyang 110168, China


Abstract
An anisotropic ultrasonic transducer is proposed for Lamb wave applications, such as passive damage or impact localization based on ultrasonic guided wave theory. This transducer is made from a PMNPT single crystal, and has different piezoelectric coefficients d31 and d32, which are the same for the conventional piezoelectric materials, such as Lead zirconate titanate (PZT). Different piezoelectric coefficients result in directionality of guided wave generated by this transducer, in other words, it is an anisotropic ultrasonic transducer. And thus, it has different sensitivity in comparison with conventional ultrasonic transducer. The anisotropic one can provide more information related to the direction when it is used as sensors. This paper first shows its detailed properties, including analytical formulae and finite elements simulations. Then, its application is described.

Key Words
ultrasonic transducer; anisotropic; sensing property; ultrasonic applications

Address
Wensong Zhou and Hui Li: Key Lab of Structures Dynamic Behavior and Control of the Ministry of Education, Harbin Institute of Technology, 73 Huanghe Road, Harbin 150090, China;
School of Civil Engineering, Harbin Institute of Technology, 73 Huanghe Road, Harbin 150090, China
Fuh-Gwo Yuan: Department of Mechanical and Aerospace Engineering, North Carolina State University, Raleigh 27695, USA


Abstract
Nonstationary features existing in tropical storms have been frequently captured in recent field measurements, and the applicability of the stationary theory to the analysis of wind characteristics needs to be discussed. In this study, a tropical storm called Nakri measured at Taizhou Bridge site based on structural health monitoring (SHM) system in 2014 is analyzed to give a comparison of the stationary and nonstationary characteristics. The stationarity of the wind records in the view of mean and variance is first evaluated with the run test method. Then the wind data are respectively analyzed with the traditional stationary model and the wavelet-based nonstationary model. The obtained wind characteristics such as the mean wind velocity, turbulence intensity, turbulence integral scale and power spectral density (PSD) are compared accordingly. Also, the stationary and nonstationary PSDs are fitted to present the turbulence energy distribution in frequency domain, among which a modulating function is included in the nonstationary PSD to revise the non-monotonicity. The modulated nonstationary PSD can be utilized to unconditionally simulate the turbulence presented by the nonstationary wind model. The results of this study recommend a transition from stationarity to nonstationarity in the analysis of wind characteristics, and further in the accurate prediction of wind-induced vibrations for engineering structures.

Key Words
tropical storm; wind characteristics; stationarity; nonstationarity; structural health monitoring

Address
Tianyou Tao, Hao Wangand Aiqun Li: Key Laboratory of Concrete and Prestressed Concrete Structures of Ministry of Education, Southeast University, Nanjing 210096, China

Abstract
Traffic load and volume is one of the most important physical quantities for bridge safety evaluation and maintenance strategies formulation. This paper aims to conduct the statistical analysis of traffic volume information and the multimodal modeling of gross vehicle weight (GVW) based on the monitoring data obtained from the weigh-in-motion (WIM) system instrumented on the arch Jiubao Bridge located in Hangzhou, China. A genetic algorithm (GA)-based mixture parameter estimation approach is developed for derivation of the unknown mixture parameters in mixed distribution models. The statistical analysis of one-year WIM data is firstly performed according to the vehicle type, single axle weight, and GVW. The probability density function (PDF) and cumulative distribution function (CDF) of the GVW data of selected vehicle types are then formulated by use of three kinds of finite mixed distributions (normal, lognormal and Weibull). The mixture parameters are determined by use of the proposed GA-based method. The results indicate that the stochastic properties of the GVW data acquired from the field-instrumented WIM sensors are effectively characterized by the method of finite mixture distributions in conjunction with the proposed GA-based mixture parameter identification algorithm. Moreover, it is revealed that the Weibull mixture distribution is relatively superior in modeling of the WIM data on the basis of the calculated Akaike

Key Words
structural health monitoring; weigh-in-motion; gross vehicle weight; finite mixture distributions; mixture parameter estimation; genetic algorithm

Address
X.W. Ye, Y.H. Su, P.S. Xi and B. Chen: Department of Civil Engineering, Zhejiang University, Hangzhou 310058, China
J.P. Han: School of Civil Engineering, Lanzhou University of Technology, Lanzhou 730050, China

Abstract
Studies on dynamic characteristics of the hanger vibration using field monitoring data are important for the design and evaluation of high-speed railway truss arch bridges. This paper presents an analysis of the hanger\'s dynamic displacement responses based on field monitoring of Dashengguan Yangtze River Bridge, which is a high-speed railway truss arch bridge with the longest span throughout the world. The three vibration parameters, i.e., dynamic displacement amplitude, dynamic load factor and vibration amplitude, are selected to investigate the hanger\'s vibration characteristics in each railway load case including the probability statistical characteristics and coupled vibration characteristics. The influences of carriageway and carriage number on the hanger\'s vibration characteristics are further investigated. The results indicate that: (1) All the eight railway load cases can be successfully identified according to the relationship of responses from strain sensors and accelerometers in the structural health monitoring system. (2) The hanger\'s three vibration parameters in each load case in the longitudinal and transverse directions have obvious probabilistic characteristics. However, they fall into different distribution functions. (3) There is good correlation between the hanger\'s longitudinal/transverse dynamic displacement and the main girder\'s transverse dynamic displacement in each load case, and their relationships are shown in the hysteresis curves. (4) Influences of the carriageway and carriage number on the hanger\'s three parameters are different in both longitudinal and transverse directions; while the influence on any of the three parameters presents an obvious statistical trend. The present paper lays a good foundation for the further analysis of train-induced hanger vibration and control.

Key Words
steel truss arch bridge; railway; hanger; dynamic displacement; static displacement; dynamic load factor; correlation; monitoring

Address
Youliang Ding and Chao Wang: School of Civil Engineering, Key Laboratory of C&PC Structures of the Ministry of Education, Southeast University, Nanjing 210096, China
Yonghui An : Department of Civil Engineering, State Key Laboratory of Coastal and Offshore Engineering,
Dalian University of Technology, Dalian 116023, China




Techno-Press: Publishers of international journals and conference proceedings.       Copyright © 2017 Techno-Press
P.O. Box 33, Yuseong, Daejeon 34186 Korea, Tel: +82-42-828-7996, Fax : +82-42-828-7997, Email: info@techno-press.com