Rehabilitation of Longitudinal Joints in Double-Tee Girder Bridges

311238Precast double-tee girders are common on county bridges in South Dakota (SD) because of the ease of construction, the short construction time, and the low cost. However, the longitudinal joints of these bridges are rapidly deteriorating, imposing girder replacement after only 45 years of service. Currently, there are more than 700 double-tee bridges in SD incorporating this joint detailing. The present study was conducted to develop, construct, and evaluate rehabilitation methods for this type of bridge

Repairable Precast Bridge Bents for Seismic Events

69A3551947137Bridges designed with current seismic codes exhibit large displacement capacities, and the bridge total collapse is prevented. However, damage of ductile members is allowed at this performance level. In reinforced concrete bridges excited by ground shaking, columns are the target ductile members in which concrete cover, core, and reinforcement may damage, and the column may not return to its original position. Minor damages are usually repaired but excessive damages such as core crushing, bar buckling, and/or bar fracture are hard to repair and will usually result in the column or bridge replacement. According to FHWA, approximately 25% of the US bridges require rehabilitation, repair, or total replacement

Post-earthquake Serviceability Assessment of RC Bridge Columns Using Computer Vision

Modern seismic design codes ensure a large displacement capacity and prevent total collapse for bridges. However, this performance objective is usually attained at the cost of damage to target ductile members. For reinforced concrete (RC) bridges, the columns are usually the main source of ductility during an earthquake in which concrete cover, core, and reinforcement may damage, and the column may experience a large permanent lateral deformation. A significant number of the US bridges will experience large earthquakes in the next 50 years that may result in the bridge closure due to excessive damage. A quick assessment of bridges immediately after severe events is needed to maximize serviceability and access to the affected sites, and to minimize casualties and costs. The main goal of this project was to accelerate post-earthquake RC bridge column assessment using \u201ccomputer vision\u201d. When sending trained personnel to the affect sites is limited or will take time, local personnel equipped with an assessment software (on various platforms such as mobile applications, cloud-based tools, or built-in with drones) can be deployed to evaluate the bridge condition. The project in this phase was focused on the damage assessment of modern RC bridge columns after earthquakes. Substandard columns, other bridge components, and other hazards were not included

A Deep Learning Approach to Estimate Blood Pressure from PPG Signals

Blood Pressure (BP) is one of the four primary vital signs indicating the status of the body vital (life-sustaining) functions. BP is difficult to continuously monitor using a sphygmomanometer (i.e. a blood pressure cuff), especially in everyday-setting. However, other health signals which can be easily and continuously acquired, such as photoplethysmography (PPG), show some similarities with the Aortic Pressure waveform. Based on these similarities, in recent years several methods were proposed to predict BP from the PPG signal. Building on these results, we propose an advanced personalized data-driven approach that uses a three-layer deep neural network to estimate BP based on PPG signals. Different from previous work, the proposed model analyzes the PPG signal in time-domain and automatically extracts the most critical features for this specific application, then using a variation of recurrent neural networks called Long-Short-Term-Memory (LSTM) to map the extracted features to the BP value associated with that time window. Experimental results on two separate standard hospital datasets, yielded absolute errors mean and absolute error standard deviation for systolic and diastolic BP values outperforming all prior approaches

A Non-invasive IoT-based Personalized Monitoring and Detection Framework for Physical and Mental Health

Non-invasive assessment of physical and mental health has been the core of an extensive body of research in recent years. Due to the development of wearable bio-sensors and the feasibility of wearing them in long time-frames (e.g. smartwatches), a tremendous opportunity lies in development and improvement of monitoring and detection methods and models for physical and mental health, based on biophysical data.We start by developing an advanced personalized data-driven approach that uses a three-layer deep neural network to estimate Blood Pressure (BP) based on photoplethysmography (PPG) signals. The model takes windows of PPG signals, and automatically extracts features which it uses for the regression problem. The proposed model outperforms prior methods and passes two long-standing standards for BP monitoring systems. In the next steps, we move forward with the development of a system for mental health assessment. Most existing health signal based tracking and monitoring approaches achieve better results presuming a large pre-labeled dataset is available and can be utilized for training the models. This is not a challenge-free assumption since these datasets usually do not exist and are not easy to collect particularly for new applications. Furthermore, these signals and labels are highly subjective and show different data distributions among different subjects. We propose a Q-learning based human-in-the-loop active learning model which releases the restriction of having a large pre-labeled dataset and aims at collecting labels for instances that are expected to improve the model's performance more efficiently. The goal is to not only minimize human annotation while maximizing the model performance through personalizing the models, but also analyzing temporal correlations to optimize the query times. Given that the experiment occurs in everyday settings (also known as \textit{in-the-wild}) and over long periods of time (time frame of few weeks to few months), maximizing user engagement is another important factor we considered in the proposed model. The proposed Q-learning based agent that selects certain instances to be labeled by the users, through analyzing the instance itself as well as the contextual and behavioral information from the subject, all in real time. The framework iteratively updates the agent based on subjective response behavior and also updates the model with the new subjective data. We performed three rounds of field studies with groups of volunteers, to collect data and evaluate the performance of the proposed framework. Extensive experiments demonstrate the superiority of the proposed model to existing unsupervised and active learning models. The proposed context-aware agent is able to provide the same level of personalization as random selection and a standard active learning method, with up to 88% and 32% fewer queries from users, respectively

A Non-invasive IoT-based Personalized Monitoring and Detection Framework for Physical and Mental Health

Non-invasive assessment of physical and mental health has been the core of an extensive body of research in recent years. Due to the development of wearable bio-sensors and the feasibility of wearing them in long time-frames (e.g. smartwatches), a tremendous opportunity lies in development and improvement of monitoring and detection methods and models for physical and mental health, based on biophysical data.We start by developing an advanced personalized data-driven approach that uses a three-layer deep neural network to estimate Blood Pressure (BP) based on photoplethysmography (PPG) signals. The model takes windows of PPG signals, and automatically extracts features which it uses for the regression problem. The proposed model outperforms prior methods and passes two long-standing standards for BP monitoring systems. In the next steps, we move forward with the development of a system for mental health assessment. Most existing health signal based tracking and monitoring approaches achieve better results presuming a large pre-labeled dataset is available and can be utilized for training the models. This is not a challenge-free assumption since these datasets usually do not exist and are not easy to collect particularly for new applications. Furthermore, these signals and labels are highly subjective and show different data distributions among different subjects. We propose a Q-learning based human-in-the-loop active learning model which releases the restriction of having a large pre-labeled dataset and aims at collecting labels for instances that are expected to improve the model's performance more efficiently. The goal is to not only minimize human annotation while maximizing the model performance through personalizing the models, but also analyzing temporal correlations to optimize the query times. Given that the experiment occurs in everyday settings (also known as \textit{in-the-wild}) and over long periods of time (time frame of few weeks to few months), maximizing user engagement is another important factor we considered in the proposed model. The proposed Q-learning based agent that selects certain instances to be labeled by the users, through analyzing the instance itself as well as the contextual and behavioral information from the subject, all in real time. The framework iteratively updates the agent based on subjective response behavior and also updates the model with the new subjective data. We performed three rounds of field studies with groups of volunteers, to collect data and evaluate the performance of the proposed framework. Extensive experiments demonstrate the superiority of the proposed model to existing unsupervised and active learning models. The proposed context-aware agent is able to provide the same level of personalization as random selection and a standard active learning method, with up to 88% and 32% fewer queries from users, respectively