Traditionally, it is not easy to carry out tests to identify modal parameters from existing railway bridges because of the testing conditions and complicated nature of civil structures. A six year (2007-2012) research program was conducted to monitor a group of 25 railway bridges. One of the tasks was to devise guidelines for identifying their modal parameters. This paper presents the experience acquired from such identification. The modal analysis of four representative bridges of this group is reported, which include B5, B15, B20 and B58A, crossing the Carajás railway in northern Brazil using three different excitations sources: drop weight, free vibration after train passage, and ambient conditions. To extract the dynamic parameters from the recorded data, Stochastic Subspace Identification and Frequency Domain Decomposition methods were used. Finite-element models were constructed to facilitate the dynamic measurements. The results
show good agreement between the measured and computed natural frequencies and mode shapes. The findings provide some guidelines on methods of excitation, record length of time, methods of modal analysis including the use of projected channel and harmonic detection, helping researchers and maintenance teams obtain good dynamic characteristics from measurement data.
railway bridges; operational modal analysis; stochastic subspace identification; frequency domain decomposition
Regina Sampaio: Faculty of Civil Engineering, Federal University of Pará - UFPA, Belem 66075-110 Brazil
Tommy H.T. Chan: Faculty of Science and Engineering, Queensland University of Technology - QUT, Brisbane 4001, Australia
One of the main causes of a limited use of nondestructive evaluation (NDE) technologies in bridge deck assessment is the speed of data collection and analysis. The paper describes development and implementation of the RABIT (Robotics Assisted Bridge Inspection Tool) for data collection using multiple NDE technologies. The system is designed to characterize three most common deterioration types in concrete bridge decks: rebar corrosion, delamination, and concrete degradation. It implements four NDE technologies: electrical resistivity (ER), impact echo (IE), ground-penetrating radar (GPR), and ultrasonic
surface waves (USW) method. The technologies are used in a complementary way to enhance the interpretation. In addition, the system utilizes advanced vision to complement traditional visual inspection. Finally, the RABIT collects data at a significantly higher speed than it is done using traditional NDE equipment. The robotic system is complemented by an advanced data interpretation. The associated platform for the enhanced interpretation of condition assessment in concrete bridge decks utilizes data integration, fusion, and deterioration and defect visualization. This paper concentrates on the validation and field implementation of two NDE technologies. The first one is IE used in the delamination detection and characterization, while the second one is the USW method used in the assessment of concrete quality. The validation of performance of the two methods was conducted on a 9 m long and 3.6 m wide fabricated bridge structure with numerous artificial defects embedded in the deck
Nenad Gucunski and Basily Basily: Department of Civil and Environmental Engineering, Rutgers University, Piscataway,
New Jersey 08854, USA
Seong-Hoon Kee: Department of Architectural Engineering, Dong-A University, Busan, 604-714, Korea
Hung La: Computer Science and Engineering, University of Nevada, Reno, NV 89557, USA
Ali Maher: Center for Advanced Infrastructure and Transportation, Rutgers University, USA
The main objective of this study is to evaluate the long-term performance of various concrete composites in natural marine environment prevailing in the Gulf region. Durability assessment studies of such nature are usually carried out under aggressive environments that constitute seawater, chloride and sulfate laden soils and wind, and groundwater conditions. These studies are very vital for sustainable development of marine and off shore reinforced concrete structures of industrial design such as petroleum
installations. First round of testing and evaluation, which is presented in this paper, were performed by standard tests under laboratory conditions. Laboratory results presented in this paper will be corroborated with test outcome of ongoing three years field exposure conditions. The field study will include different parameters of investigation for high performance concrete including corrosion inhibitors, type of reinforcement, natural and industrial pozzolanic additives, water to cement ratio, water type, cover thickness, curing conditions, and concrete coatings. Like the laboratory specimens, samples in the field will be monitored for corrosion induced deterioration signs and for any signs of failureover initial period ofthree years. In this paper, laboratory results pertaining to microsilica (SF), ground granulated blast furnace slag (GGBS), epoxy coated rebars and calcium nitrite corrosion inhibitor are very conclusive. Results affirmed that the supplementary cementing materials such as GGBS and SF significantly impacted and enhanced concrete resistivity to chloride ions penetration and hence decrease the corrosion activities on steel bars protected by such concretes. As for epoxy coated rebars applications under high chloride laden conditions, results showed great concern to integrity of the epoxy coating layer on the bar and its stability. On the other hand corrosion inhibiting admixtures such as calcium nitrite proved to be more effective when used in combination with the pozzolanic additives such as GGBS and microsilica.
corrosion; deterioration; high performance concrete; marine and off shore structures; mineral admixtures; reinforced concrete
Suad Al-Bahar and A. Husain: Construction and Buildings Materials Program, Energy and Building Research Center,
Kuwait Institute for Scientific Research, Safat 13109, Kuwait
This paper presents a method for assessing the risk of wave run-up and overtopping of existing coastal defences and for analysing the probability of failure of the structures under future hydraulic conditions. The recent UK climate projections are employed in the investigations of the influence of changing environments on the long-term performance of sea defences. In order to reduce the risk of wave run-up and overtopping caused by rising sea level and to maintain the present-day allowances for wave run-up height and overtopping discharge, the future necessary increase in crest level of existing structures is
investigated. Various critical failure mechanisms are considered for reliability analysis, i.e., erosion of crest
by wave overtopping, failure of seaside revetment, and internal erosions within earth sea dykes. The time-dependent reliability of sea dykes is analysed to give probability of failure with time. The results for an
example earth dyke section show that the necessary increase in crest level is approximately double of sea level rise to maintain the current allowances. The probability of failure for various failure modes of the earth dyke has a significant increase with time under future hydraulic conditions.
Among the destruction instances of half-through arch bridges, the shorter hangers are more likely to be ruined. For a thorough investigation of the hanger system durability, we have studied vehicle impact effect on hangers with vehicle-bridge coupling method for a half-through concrete-filled-steel-tube arch bridge. A numerical method has been applied to simulate the variation of dynamic internal force (stress) in hangers under different vehicle speeds and road surface roughness. The characteristics and differences in impact effect among hangers with different length (position) are compared. The impact effect is further analyzed comprehensively based on the vehicle speed distribution model. Our results show that the dynamic internal force induced by moving vehicles inside the shorter hangers is significantly greater than that inside the longer ones. The largest difference of dynamic internal force among the hangers could be as high as 28%. Our results well explained a common phenomenon in several hanger damage accidents occurred in China.
This work forms a basis for hanger system\'s fatigue analysis and service life evaluation. It also provides a
reference to the design, management, maintenance, monitoring, and evaluation for this kind of bridge.
The idea of using measured dynamic characteristics for damage detection is attractive because it allows for a global evaluation of the structural health and condition. However, vibration-based damage detection for complex structures such as long-span cable-supported bridges still remains a challenge. As a suspension or cable-stayed bridge involves in general thousands of structural components, the conventional damage detection methods based on model updating and/or parameter identification might result in ill-conditioning and non-uniqueness in the solution of inverse problems. Alternatively, methods that utilize, to the utmost extent, information from forward problems and avoid direct solution to inverse problems would be more suitable for vibration-based damage detection of long-span cable-supported bridges. The auto-associative neural network
(ANN) technique and the probabilistic neural network (PNN) technique, that both eschew inverse problems, have
been proposed for identifying and locating damage in suspension and cable-stayed bridges. Without the help of a
structural model, ANNs with appropriate configuration can be trained using only the measured modal frequencies
from healthy structure under varying environmental conditions, and a new set of modal frequency data acquired
from an unknown state of the structure is then fed into the trained ANNs for damage presence identification. With
the help of a structural model, PNNs can be configured using the relative changes of modal frequencies before and
after damage by assuming damage at different locations, and then the measured modal frequencies from the structure can be presented to locate the damage. However, such formulated ANNs and PNNs may still be incompetent to identify damage occurring at the deck members of a cable-supported bridge because of very low modal sensitivity to the damage. The present study endeavors to enhance the damage identification capability of ANNs and PNNs when being applied for identification of damage incurred at deck members. Effort is first made to construct combined modal parameters which are synthesized from measured modal frequencies and modal shape components to train ANNs for damage alarming. With the purpose of improving identification accuracy, effort is then made to configure PNNs for damage localization by adapting the smoothing parameter in the Bayesian classifier to different values for different pattern classes. The performance of the ANNs with their input being modal frequencies and the combined modal parameters respectively and the PNNs with constant and
adaptive smoothing parameters respectively is evaluated through simulation studies of identifying damage inflicted on different deck members of the double-deck suspension Tsing Ma Bridge.
structural health monitoring; vibration-based damage identification; auto-associative neural
network; probabilistic neural network; suspension bridge
J.Y. Wang: Department of Design Management, Hangzhou Construction Committee, Hangzhou, China
Y.Q. Ni: Department of Civil and Environmental Engineering, The Hong Kong Polytechnic University, Hung Hom, Kowloon, Hong Kong