Evaluation of Gusset Plate Buckling, Member Eccentricity Effect, and Pin Strength for Steel Truss Bridges

Three issues on steel truss bridge evaluation and design are investigated in this research. Part 1 deals with gusset plate buckling and the associated compression strength. Gusset plates are routinely used to connect members at panel points in steel truss bridges and braced-frame buildings. Current design practice is to treat a portion of the gusset plate at the end of the connected member (e.g., diagonal brace) as a compression member. This process requires a determination of the member length, an effective length factor, and the use of a Whitmore section with a proper dispersion angle to define the cross section of the compression member. The method, first proposed by Thornton (1984), was mainly based on engineering judgment. After the collapse of the I-35W Bridge in Minneapolis, MN in 2007 with 13 casualties, significant research efforts at both federal and state levels were made, which resulted in the new design requirements for checking gusset buckling in the AASHTO LRFD Bridge Design Specifications. Yet, the new requirements are still based on the Thornton’s column analogy concept with some minor modifications. In this dissertation, a novel approach that treats gusset buckling as a phenomenon of plate buckling under shear is developed. This proposed design method properly reflects the sway buckling mode, a failure mode that is always observed in laboratory testing but is not reflected in the column-analogy method. Based on finite element simulation, the model developed considers the effects of the free edge length of the gusset plate, the brace connected length, and the angle between the brace and the gusset plate. A correlation with the available test data shows that the scatter of data is drastically reduced when compared with the conventional design method.Part 2 of the research addresses issues faced by bridge designers on the load rating of existing steel truss bridges. Built-up members in many truss bridges have centroids that do not coincide with the working lines, and such eccentricity at panel points produces moments in the members. One common practice is to analyze the bridge as a pin-connected truss, and then the end moments are computed as the axial load multiplied by either 100% or 50% of the end eccentricity, depending on the type of the connection (either pin-connected or gusset-connected) at the panel point. Together with field load testing of a truss bridge in Southern California and the associated finite element simulation, this study concludes that the Caltrans practice of handling eccentricity has no technical basis. Instead, eccentricity should be explicitly included in the finite element models. The last part of the research deals with the behavior and design of steel pins. Using steel pins to connect members at the panel points is common in many existing steel truss bridges. The AASHTO Specifications treat the pin as a beam, and a moment-shear interaction check is used to check the pin strength. Since test data, especially pins with larger diameters, is very scarce, experimental testing of twelve 2-in. diameter pins with two steel grades was conducted. Together with finite element simulation, this research shows that the beam-analogy approach adopted in the AASHTO Specifications can be very conservative. Alternative equations to predict the strength of steel pins are proposed, which would potentially eliminate many unnecessary retrofits of steel pins in existing truss bridges

State-dependent Gaussian kernel-based power spectrum modification for accurate instantaneous heart rate estimation.

Accurate estimation of the instantaneous heart rate (HR) using a reflectance-type photoplethysmography (PPG) sensor is challenging because the dominant frequency observed in the PPG signal corrupted by motion artifacts (MAs) does not usually overlap the true HR, especially during high-intensity exercise. Recent studies have proposed various MA cancellation and HR estimation algorithms that use simultaneously measured acceleration signals as noise references for accurate HR estimation. These algorithms provide accurate results with a mean absolute error (MAE) of approximately 2 beats per minute (bpm). However, some of their results deviate significantly from the true HRs by more than 5 bpm. To overcome this problem, the present study modifies the power spectrum of the PPG signal by emphasizing the power of the frequency corresponding to the true HR. The modified power spectrum is obtained using a Gaussian kernel function and a previous estimate of the instantaneous HR. Because the modification is effective only when the previous estimate is accurate, a recently reported finite state machine framework is used for real-time validation of each instantaneous HR result. The power spectrum of the PPG signal is modified only when the previous estimate is validated. Finally, the proposed algorithm is verified by rigorous comparison of its results with those of existing algorithms using the ISPC dataset (n = 23). Compared to the method without MA cancellation, the proposed algorithm decreases the MAE value significantly from 6.73 bpm to 1.20 bpm (p < 0.001). Furthermore, the resultant MAE value is lower than that obtained by any other state-of-the-art method. Significant reduction (from 10.89 bpm to 2.14 bpm, p < 0.001) is also shown in a separate experiment with 24 subjects

Reflectance pulse oximetry: Practical issues and limitations

The demand for reflective-mode pulse oximetry to monitor oxygen saturation has been continuously increasing because it can be used at diverse measurement sites such as the feet, forehead, chest, and wrists. For the wrists, in particular, pulse oximeters are easily available in the form of a band or watch. In this study, we developed a reflectance pulse oximeter and used it to measure oxygen saturation levels at the fingertips and the wrist. We analyzed the performance of this oximeter to address the challenges and limitations associated with using reflective-mode oximeters at the wrist for clinical purposes

Adaptive scheduling of acceleration and gyroscope for motion artifact cancelation in photoplethysmography

Background and objective: Recently, various algorithms have been introduced using wrist-worn photo-plethysmography (PPG) to provide high accuracy of instantaneous heart rate (HR) estimation, including during high-intensity exercise. Most studies focus on using acceleration and/or gyroscope signals for the motion artifact (MA) reference, which attenuates or cancels out noise from the MA-corrupted PPG signals. We aim to open and pave the path to find an appropriate MA reference selection for MA cancelation in PPG.Methods: We investigated how the acceleration and gyroscope reference signals correlate with the MAs of the distorted PPG signals and derived both mathematically and experimentally an adaptive MA reference selection approach. We applied our algorithm to five state-of-the-art (SOTA) methods for the performance evaluation. In addition, we compared the four MA reference selection approaches, i.e. with acceleration signal only, with gyroscope signal only, with both signals, and using our proposed adaptive selection.Results: When applied to 47 PPG recordings acquired during intensive physical exercise from two differ-ent datasets, our proposed adaptive MA reference selection method provided higher accuracy than the other MA selection approaches for all five SOTA methods.Conclusion: Our proposed adaptive MA reference selection approach can be used in other MA cancelation methods and reduces the HR estimation error.Significance: We believe that this study helps researchers to address acceleration and gyroscope signals as accurate MA references, which eventually improves the overall performance for estimating HRs through the various algorithms developed by research groups

Dedicated cardiac rehabilitation wearable sensor and its clinical potential

<div><p>We describe a wearable sensor developed for cardiac rehabilitation (CR) exercise. To effectively guide CR exercise, the dedicated CR wearable sensor (DCRW) automatically recommends the exercise intensity to the patient by comparing heart rate (HR) measured in real time with a predefined target heart rate zone (THZ) during exercise. The CR exercise includes three periods: pre-exercise, exercise with intensity guidance, and post-exercise. In the pre-exercise period, information such as THZ, exercise type, exercise stage order, and duration of each stage are set up through a smartphone application we developed for iPhones and Android devices. The set-up information is transmitted to the DCRW via Bluetooth communication. In the period of exercise with intensity guidance, the DCRW continuously estimates HR using a reflected pulse signal in the wrist. To achieve accurate HR measurements, we used multichannel photo sensors and increased the chances of acquiring a clean signal. Subsequently, we used singular value decomposition (SVD) for de-noising. For the median and variance of RMSEs in the measured HRs, our proposed method with DCRW provided lower values than those from a single channel-based method and template-based multiple-channel method for the entire exercise stage. In the post-exercise period, the DCRW transmits all the measured HR data to the smartphone application via Bluetooth communication, and the patient can monitor his/her own exercise history.</p></div

Internal system block diagram of the DCRW.

<p>Internal system block diagram of the DCRW.</p

Post-exercise stage with the cardiac rehabilitation (CR) application.

<p>(a) exercise history summary, (b) calendar-based exercise history, (c) exercise analysis, (d) heart rate trace example in warm-up stage, (e) heart rate example in main exercise stage.</p

Performance comparison of the single channel, multiple channel with template updates, and multiple channel with truncated SVD.

<p>Median and standard deviation of root mean square errors (RMSEs) for walking (warm-up), jogging (main exercise), walking (warm-up), additional jogging (main exercise) and walking (cool-down).</p