Prefabricated bridge substructures provide new possibility for designers in terms of efficiency of creativity, fast construction, geometry control and cost. Even though prefabricated bridge columns are widely adopted as a substructure system in the bridge construction project recently, lack of deeper understanding of the seismic behavior of prefabricated bridge substructures cause much concern on their performance in high seismic zones. In this paper, experimental research works are presented to verify enhanced design concepts of prefabricated bridge piers. Integration of precast segments was done with continuity of axial prestressing tendons and mild reinforcing bars throughout the construction joints. Cyclic tests were conducted to investigate the effects of the design parameters on seismic performance. An analytical method for moment-curvature analysis of prefabricated bridge columns is conducted in this study. The method is validated through comparison with experimental results and the fiber model analysis. A parametric study is conducted to observe the seismic behavior of prefabricated bridge columns using the analytical study based on strain compatibility method. The effects of continuity of axial steel and tendon, and initial prestressing level on the load-displacement response characteristics, i.e., the strain of axial mild steels and posttensioned tendon at fracture and concrete crushing strain at the extreme compression fiber are investigated. The analytical study shows the layout of axial mild steels and posttensioned tendons in this experiment is the optimized arrangement for seismic performance.

This study is intended to propose a precast bridge deck system, which has ribbed loop joints between the decks and lacks internal tendons to improve the workability of existing precast deck system. A composite bridge deck specimen was fabricated using the proposed precast deck system, and static and fatigue load tests were conducted to evaluate the structural behavior and the crack pattern of the deck. Leakage test of the deck joints was also conducted and finite element analysis was carried out to compare with the test results.

In this study, the transfer length of 2400 MPa, seven-wire high-strength steel strands with a 15.2 mm diameter in pretensioned prestressed concrete (PSC) beams utilizing high strength concrete over 58 MPa at prestress release was evaluated experimentally. 32 specimens, which have the variables of concrete compressive strength, concrete cover depth, and the number of PS strands, were fabricated and corresponding transfer lengths were measured. The strands were released gradually by slowly reducing the pressure in the hydraulic stressing rams. The measured results of transfer length showed that the transfer length decreased as the concrete compressive strength and concrete cover depth increased. The number of strands had a very small effect, and the effect varied with both the concrete cover depth and concrete strength. The results were compared to current design codes and transfer lengths predicted by other researchers. The comparison results showed that the current transfer length prediction models in design codes may be conservatively used for 2400 MPa high-strength strands in high-strength concrete beams exceeding 58 MPa at prestress release.

In this study, an innovative and smart glass fiber-reinforced polymer (GFRP) hybrid bar was developed for stronger durability of concrete structures. As comparing with the conventional GFRP bar, the smart GFRP Hybrid bar can promise to enhance the modulus of elasticity so that it makes the cracking reduced than the case when the conventional GFRP bar is used. Besides, the GFRP Hybrid bar can effectively resist the corrosion of conventional steel bar by the GFRP outer surface on the steel bar. In order to verify the bond performance of the GFRP hybrid bar for structural reinforcement, uniaxial pull-out test was conducted. The variables were the bar diameter and the number of strands and pitch of the fiber ribs. Tensile tests showed a excellent increase in the modulus of elasticity, 152.1 GPa, as compared to that of the pure GFRP bar (50 GPa). The stress-strain curve was bi-linear, so that the ductile performance could be obtained. For the bond test, the entire GFRP hybrid bar test specimens failed in concrete splitting due to higher shear strength resulting in concrete crushing as a function of bar deformation. Investigation revealed that an increase in the number of strands of fiber ribs enhanced the bond strength, and the pitch guaranteed the bond strength of 19.1 mm diameter hybrid bar with 15.9 mm diameter of core section of deformed steel the ACI 440 1R-15 equation is regarded as more suitable for predicting the bond strength of GFRP hybrid bars, whereas the CSA S806-12 prediction is considered too conservative and is largely influenced by the bar diameter. For further study, various geometrical and material properties such as concrete cover, cross-sectional ratio, and surface treatment should be considered.

This study presents a new approach of surrogate modeling for time-consuming finite element analysis. A surrogate model is widely used to reduce the computational cost under an iterative computational analysis. Although a variety of the methods have been widely investigated, there are still difficulties in surrogate modeling from a practical point of view: (1) How to derive optimal design of experiments (i.e., the number of training samples and their locations); and (2) diagnostics of the surrogate model. To overcome these difficulties, we propose a sequential surrogate modeling based on Gaussian process model (GPM) with self-adaptive sampling. The proposed approach not only enables further sampling to make GPM more accurate, but also evaluates the model adequacy within a sequential framework. The applicability of the proposed approach is first demonstrated by using mathematical test functions. Then, it is applied as a substitute of the iterative finite element analysis to Monte Carlo simulation for a response uncertainty analysis under correlated input uncertainties. In all numerical studies, it is successful to build GPM automatically with the minimal user intervention. The proposed approach can be customized for the various response surfaces and help a less experienced user save his/her efforts.

Although the deployment of wireless sensors for structural sensing and monitoring is becoming popular, supplying power to these sensors remains as a daunting task. To address this issue, there have been large volume of ongoing energy harvesting studies that aimed to find a way to scavenge energy from surrounding ambient energy sources such as vibration, light and heat. In this study, a magnetic resonance based wireless power transfer (MR-WPT) system is proposed so that sensors inside a concrete structure can be wirelessly powered by an external power source. MR-WPT system offers need-based active power transfer using an external power source, and allows wireless power transfer through 300-mm thick reinforced concrete with 21.34% and 17.29% transfer efficiency at distances of 450 mm and 500 mm, respectively. Because enough power to operate a typical wireless sensor can be instantaneously transferred using the proposed MR-WPT system, no additional energy storage devices such as rechargeable batteries or supercapacitors are required inside the wireless sensor, extending the expected life-span of the sensor.

In this paper, dynamic displacement is estimated with high accuracy by blending high-sampling rate acceleration data with low-sampling rate displacement measurement using a two-stage Kalman estimator. In Stage 1, the two-stage Kalman estimator first approximates dynamic displacement. Then, the estimator in Stage 2 estimates a bias with high accuracy and refines the displacement estimate from Stage 1. In the previous Kalman filter based displacement techniques, the estimation accuracy can deteriorate due to (1) the discontinuities produced when the estimate is adjusted by displacement measurement and (2) slow convergence at the beginning of estimation. To resolve these drawbacks, the previous techniques adopt smoothing techniques, which involve additional future measurements in the estimation. However, the smoothing techniques require more computational time and resources and hamper real-time estimation. The proposed technique addresses the drawbacks of the previous techniques without smoothing. The performance of the proposed technique is verified under various dynamic loading, sampling rate and noise level conditions via a series of numerical simulations and experiments. Its performance is also compared with those of the existing Kalman filter based techniques.

Advances in sensor technologies have led to the instrumentation of sensor networks for bridge monitoring and management. For a dense sensor network, enormous amount of sensor data are collected. The data need to be managed, processed, and interpreted. Data management issues are of prime importance for a bridge management system. This paper describes a data management infrastructure for bridge monitoring applications. Specifically, NoSQL database systems such as MongoDB and Apache Cassandra are employed to handle time-series data as well the unstructured bridge information model data. Standard XML-based modeling languages such as OpenBrIM and SensorML are adopted to manage semantically meaningful data and to support interoperability. Data interoperability and integration among different components of a bridge monitoring system that includes on-site computers, a central server, local computing platforms, and mobile devices are illustrated. The data management framework is demonstrated using the data collected from the wireless sensor network installed on the Telegraph Road Bridge, Monroe, MI.