Marvin W. Halling

This study addresses the feasibility of using a single-span bridge as a weigh-in-motion (WIM) tool to quantify the gross vehicle weights (GVWs) of trucks inexpensively with a small number of sensors and without using axle detectors. Field testing was performed on an interstate without any lane closures. Four preweighed trucks with different axle configurations traveled over a bridge at three different speeds and in two separate lanes. Measured strain data were used to implement bridge weigh-in-motion (B-WIM) algorithms and calculate the corresponding velocities and GVWs. A comparison was made between calculated and actual measured static weights, and between the calculated and specified speeds of the trucks. In addition to field testing, a finite-element (FE) model of the tested bridge was created and calibrated based on the measured strains at different locations. This calibrated FE model enabled the acquisition of the influence values for the bridge at any location (influence surface). Ten diffe...

This paper describes the experimental testing of 48-year-old concrete bridge girders that were fabricated using lightweight concrete. After the bridge was decommissioned, three girders were transported to the Systems, Material, and Structural Health (SMASH) Laboratory at Utah State University (USU). The double-tee girders were tested to quantify the effective prestress, flexural capacity, and deck punching shear strength. The experimental results were compared with calculated values based on recommended procedures in the AASHTO LRFD bridge design specifications. The AASHTO refined method underestimated the loss of prestress of the three girders by an average of 17.6%. The calculated flexural capacities were overestimated by an average of 34.0% when compared with the measured values. This unconservative result is believed to be a result of the deck deterioration. The average calculated punching shear capacity was within 3.0% of the measured values. The experimental results were also compared with a...

Modal parameters are subjected to significant changes not only by damage, but also by environmental conditions. Without quantifying environmental effects, applying vibration-based damage detection techniques may result in false damage identification. Collecting vibration and temperature data continuously have enabled researchers to investigate the correlation between environmental effects and identified modal parameters. Amid different environmental effects, temperature variations have been found to have the biggest impact on vibrational characteristics of bridges. In this paper, the effect of temperature on vibrational characteristics of a continuously monitored bridge, located in Perry, Utah, is presented. This bridge is instrumented with a monitoring system that collects vibration and temperature data at different locations of the bridge every hour. For dimensionality reduction, principle components of temperature measurements were extracted by applying principle component analysis (PCA). Natural frequencies of the structure are identified from ambient vibration data using the natural excitation technique (NExT) along with the Eigen system realization (ERA) algorithm. Variability of identified natural frequencies were investigated based on statistical properties of identified frequencies. A nonlinear ARX model and nonlinear polynomial models were used to find the relationship between the measured temperature and natural frequencies. The nonlinear ARX model showed natural frequencies are dependent to previously measured temperatures. This model was validated by using different sets of available data. These two models were evaluated and compared based on the statistical properties.

As part of a study to quantify temperature effects on integral-abutment box-girder bridges, field instrumentation was monitored continuously for one year on a bridge near Sacramento, California. Thermocouples were installed throughout the deck as well as over the height of the superstructure. Temperature changes were used to obtain maximum and minimum average temperatures as well as positive and negative thermal gradients. These values were compared with recommended respective temperature ranges. Strain changes at various locations were obtained by driving trucks along five load paths. These data, along with strain changes resulting from daily temperature variations, were used to validate a finite-element model using solid elements for the superstructure and springs at the abutments, in order to replicate the partial fixity of the bridge. The validated finite-element model was used to evaluate the effects that the partial fixity and temperature gradients have on the temperature stress for a typical integral-abutment bridge.

Vibration-based structural health monitoring (SHM) has become a tool in evaluating the condition of civil structures. For bridge structures, vibration-based SHM systems may use the dynamic response of a bridge to measure modal parameters. A change in a structure's modal parameters may indicate damage or other changes to the structural system. These same modal parameters are also sensitive to environmental conditions, primarily temperature. Statistical models have been utilized to differentiate the modal parameter changes caused by temperature and those caused by physical changes. Statistical models also help describe the relationship between modal parameters and environmental conditions. The Lambert Road Bridge is a concrete integral abutment bridge located in California on Interstate 5 (I-5), south of Sacramento. In this study, a SHM system installed on the Lambert Road Bridge was utilized to monitor the bridge's modal parameters and temperatures. The measurements from the SHM system were used to create multiple linear regression models that helped describe the relationship between the structure's modal parameters and temperatures.

Various research studies have been carried out to improve wireless structural monitoring. The improvements can be categorized into two general categories: data processing (software) and sensors technology (hardware). Advances in data processing offer new strategies for analyzing the data while novel sensor technologies mostly solve the challenges in power consumption and data transmission. These hardware challenges may also be affected by improvements in the data analyzing process which can help in reducing the quantity of data that needs to be transferred and significantly decreases the power consumption. In this study, the random decrement (RD) technique along with the Ibrahim Time Domain (ITD) method were evaluated for modal identification of structures through both wired and wireless sensors. This paper shows how smaller segments of data can be used for extracting the modal properties with similar accuracy. A simple laboratory beam test was performed to compare the data from co-located wireless and wired sensors in the time-domain. Then, the modal identification of the beam through RD and ITD method shows the comparison between wired and wireless structural identification. Additionally, this study investigates the capability of the wireless sensors in providing accurate modal parameters through different data analyzing processes and system identification techniques under actual field conditions. These techniques were used for structural identification of a bridge located in Perry, Utah. The performance and capability of these methods were evaluated and show that detecting the modal parameters of a bridge can be achieved by not transmitting the entire collected package of data.

A bridge's performance depends on different factors including superstructure type, support conditions, construction materials, environmental conditions and external loading. Identifying and evaluating the loads applied to a bridge, during its effective life, are necessary for assessing the condition and remaining life of that bridge. Furthermore, the influence of dynamic loading on the structural response of the bridge is important to be considered for the safety of the structure. Although ambient loads can be helpful to assess the changes in bridge condition as monitored over time, applying identified loads allows for a more clear understanding as to how the bridge responds to different types of loads. As a part of the Long Term Bridge Performance (LTBP) program, a bridge located in Perry, Utah was subjected to four sets of high speed, live-load tests. For evaluating the bridge response to different types of loads, these live load tests were conducted with four different truck configurations that traversed the bridge in both right and left lanes. In addition, the different trucks traversed the bridge at three different speeds to make it possible to assess the effect of dynamic response of the bridge. Moreover, these different trucks varied in weight and axle spacing. The data were collected through multiple types of sensors, located on different points and some co-located on same points. The sensors used in this study are strain gauges and the response of the co-located sensors was compared to evaluate the reliability of the results. Two different B-WIM methods were used to calculate the speed of the trucks. These methods include the correlation of signals and the derivative of the strains. The performance of these methods for evaluating the accurate speed was studied. The axle distances of the trucks were also calculated through the derivative of the strains. Through these B-WIM algorithms and the long-term monitoring system installed on the bridge, the traffic on the bridge and the bridge dynamic response to the truck loadings can be quantified with very simple and inexpensive calculations.