Enhanced Concrete Bridge Assessment Using Artificial Intelligence and Mixed Reality

Conventional methods for visual assessment of civil infrastructures have certain limitations, such as subjectivity of the collected data, long inspection time, and high cost of labor. Although some new technologies (i.e. robotic techniques) that are currently in practice can collect objective, quantified data, the inspector\u27s own expertise is still critical in many instances since these technologies are not designed to work interactively with human inspector. This study aims to create a smart, human-centered method that offers significant contributions to infrastructure inspection, maintenance, management practice, and safety for the bridge owners. By developing a smart Mixed Reality (MR) framework, which can be integrated into a wearable holographic headset device, a bridge inspector, for example, can automatically analyze a certain defect such as a crack that he or she sees on an element, display its dimension information in real-time along with the condition state. Such systems can potentially decrease the time and cost of infrastructure inspections by accelerating essential tasks of the inspector such as defect measurement, condition assessment and data processing to management systems. The human centered artificial intelligence (AI) will help the inspector collect more quantified and objective data while incorporating inspector\u27s professional judgment. This study explains in detail the described system and related methodologies of implementing attention guided semi-supervised deep learning into mixed reality technology, which interacts with the human inspector during assessment. Thereby, the inspector and the AI will collaborate/communicate for improved visual inspection

Türkiyedeki geniş yastık kirişli dolgu çerçeve sistemlerinin sismik performans değerlendirmesi.

Wide-beam frame buildings are prevalent in Turkey since 1980’s due to their advantageous characteristics such as ease of construction, construction speed and cost efficiency. However; according to recent experimental studies, wide beam systems demonstrate poor energy dissipation capacity under earthquake. The capacities of the wide-beams may not fully developed at the beam-column joints. The beam reinforcements that do not anchor to the core area of the columns may not reach their full capacities unless special measures are taken. The goal of this thesis is to simulate the behavior of wide beam buildings under earthquake excitation through the earthquake simulation software OpenSees (Open System for Earthquake Simulation). The software occupies Modified Ibarra-Krawinkler Deterioration Model in order to model the hysteresis behavior of interior and exterior wide-beam connections. The hysteresis cycles of several experimental studies are calibrated through a procedure developed based on Haselton’s calibration equations. Later this calibration is implemented to the simulation of a wide-beam building model and a vulnerability study is carried out. The deformation limits of Immediate Occupancy (IO), Life Safety (LS) and Collapse Prevention (CP) are first identified through a pushover analysis. Later, a set of earthquake data is used in consecutive time history analyses and the maximum inter-story drift ratios are recorded for each ground-motion input. Finally, a set of fragility curves are produced for different types of ground motion parameters. The results are compared with the results of typical conventional moment frames and flat slab buildings.M.S. - Master of Scienc

Comparative Life Cycle Assessment Of Sport Utility Vehicles With Different Fuel Options

Purpose: Sport utility vehicles typically have lower fuel economy due to their high curb weights and payload capacities as well as their potential to cause serious environmental impacts. In light of this fact, a life cycle assessment is carried out in this study to assess their cradle-to-grave environmental impacts for life cycle phases ranging from manufacturing to end-of-life recycling. Methods: A hybrid economic input-output life cycle assessment (EIO-LCA) method is used in this research paper to estimate the environmental impacts (greenhouse gas emissions, energy consumption, and water withdrawal) of sport utility vehicles. This life cycle assessment is also supplemented with a sensitivity analysis, using a Monte Carlo simulation to estimate the possible ranges for total mileage of operation and fuel economy, and to account for the sensitivity of the EIO-LCA output. Results and discussion: The operation phase is the major contributor to the overall life cycle impact of sport utility vehicles in each fuel/power category. Furthermore, among the selected vehicles in this study, the battery electric vehicle has the lowest greenhouse gas emissions (77.2 tonnes) and the lowest energy consumption (1046.8 GJ) even though the environmental impact indicators for the battery manufacturing process are significantly large. The plug-in hybrid vehicle, on the other hand, demonstrates an optimal performance between energy use and water withdrawal (1172.9 GJ of energy consumption and 1370 kgal of water withdrawal). In addition, all of the fuel-powered vehicles demonstrated similar environmental performances in terms of greenhouse gas emissions, which ranged between 100 and 110 tonnes, but the hydrogen fuel cell vehicle had a significantly large water withdrawal (2253.2 kgal). Conclusions: Since the majority of the overall impact stems from the operation of the vehicle in question, their complete elimination of tailpipe emissions and their high energy efficiency levels make battery electric vehicles a viable green option for sport utility vehicles. However, there are certain uncertainties beyond the scope of this study that can be considered in future studies to improve upon this assessment, including (but not limited to) regional differences in source of electricity generation and socio-economic impacts

A Novel Decision Support System for Long-Term Management of Bridge Networks

Developing a bridge management strategy at the network level with efficient use of capital is very important for optimal infrastructure remediation. This paper introduces a novel decision support system that considers many aspects of bridge management and successfully implements the investigated methodology in a web-based platform. The proposed decision support system uses advanced prediction models, decision trees, and incremental machine learning algorithms to generate an optimal decision strategy. The system aims to achieve adaptive and flexible decision making while entailing powerful utilization of nondestructive evaluation (NDE) methods. The NDE data integration and visualization allow automatic retrieval of inspection results and overlaying the defects on a 3D bridge model. Furthermore, a deep learning-based damage growth prediction model estimates the future condition of the bridge elements and utilizes this information in the decision-making process. The decision ranking takes into account a wide range of factors including structural safety, serviceability, rehabilitation cost, life cycle cost, and societal and political factors to generate optimal maintenance strategies with multiple decision alternatives. This study aims to bring a complementary solution to currently in-use systems with the utilization of advanced machine-learning models and NDE data integration while still equipped with main bridge management functions of bridge management systems and capable of transferring data to other systems

Advanced bridge visual inspection using real-time machine learning in edge devices

Abstract Conventional methods for bridge inspection are labor intensive and highly subjective. This study introduces an optimized approach using real-time learning-based computer vision algorithms on edge devices to assist inspectors in localizing and quantifying concrete surface defects. To facilitate a better AI-human interaction, localization and quantification are separated in this study. Two separate learning-based computer vision models are selected for this purpose. The models are chosen from several available deep learning models based on their accuracy, inference speed, and memory size. For defect localization, Yolov5s shows the most promising results when compared to several other Convolutional Neural Network architectures, including EfficientDet-d0. For the defect quantification model, 12 different architectures were trained and compared. UNet with EfficientNet-b0 backbone was found to be the best performing model in terms of inference speed and accuracy. The performance of the selected model is tested on multiple edge-computing devices to evaluate its performance in real-time. This showed how different model quantization methods are considered for different edge computing devices. The proposed approach eliminates the subjectivity of human inspection and reduces labor time. It also guarantees human-verified results, generates more annotated data for AI training, and eliminates the need for post-processing. In summary, this paper introduces a novel and efficient visual inspection methodology that uses a learning-based computer vision algorithm optimized for real-time operation in edge devices (i.e., wearable devices, smartphones etc.)

Seismic Fragility of Wide-Beam Infill-Joist Block Reinforced Concrete Frame Buildings

Wide-beam infill-joist block (WBIJB) reinforced concrete (RC) frame is a popular construction technique in Turkey and the Mediterranean basin. Even though there were previous observations and considerations about the problematic seismic behavior of the WBIJB frames, the behavior was concealed behind the general weaknesses of the RC frames. Currently WBIJB RC frames are not considered as a separate sub-class of RC frame systems during regional damage and loss estimation studies. However, the recent earthquakes shed a light on the possible inferior behavior of this class of building structures. The main difference of the WBIJB frames from conventional moment resisting frames is the presence of wideshallow beams as an architectural preference. Due to shallow beams, the lateral stiffness of these systems decrease which resulted an increase in the period of the buildings. Hence, increased earthquake drift demands resulted. The main goal of the presented study is to develop the fragility curves for this specific class of RC frame buildings, which constitute a significant portion of the existing building stock in Turkey. The resulting fragility information could be employed in future regional loss estimation studies. For this purpose, a generic two dimensional frame is designed and modeled by considering the properties of the existing WBIJB frame buildings, the current seismic regulations and the construction practice in Turkey. Pushover and nonlinear time history analyses are conducted in order to quantify seismic demand and capacity. Then this information is used to calculate the probabilities of exceeding the predefined limit states as a function of seismic ground motion intensity parameters. In the last part of the study, the estimated fragility curves are compared with the available fragility curves of similar construction types

Successful pacemaker implantation for sinus node dysfunction in a patient with duschenne muscular dystrophy

Duschenne müsküler distrofisi (DMD) müsküler distrofiler içerisinde en sık görülen ve en ciddi seyreden formudur. Distrofin proteinindeki mutasyon sonucu iskelet ve kalp kasında dejenerasyon ve bunun yerini alan yağ dokusu ve fibrosiz ile karakterize kalıtsal bir hastalıktır. Kardiyak tutulumun %90 olduğu bu hastalıkta kalp yetersizliğine ek olarak ritim problemleri de olabilmektedir. Bu olguda DMD ile takip edilen ve senkop şikayeti olan bir hastaya elektrokardiyogramda (EKG) sinüs duraklamaları olması nedeniyle kalıcı pacemaker implantasyonu yapıldı. DMD’nin aritmik komplikasyonlarını bu vaka takdiminde kısaca tartışmaya çalıştık.Duschenne muscular dystrophy (DMD) is the most frequent and severe form of the muscular dystrophies. The syndrome is hereditary disease which is characterized with degeneration in the sceletal and cardiac muscle cells as a cause of mutation in the dystrophin pro- tein. Cardiac involvement is about 90% and in addition to heart failure, rhythm disorders may also develop. In this case report, patient with a diagnosis of DMD presented with syncope and sinus pause in the electrocardiogram was referred to the cardiology and pacemaker implantation was performed. We briefly discuss arrhythmic ccomplications of DMD in this case presentation

Bridge Inspection And Condition Assessment Using Image-Based Technologies With Uavs

This paper presents the capabilities and limitations of high-definition (HD) imaging and infrared thermography (IRT) using unmanned aerial vehicles (UAVs) for bridge inspections. Regarding HD imaging, this study shows the potential to detect the required minimum, 0.1 mm, width of cracks using simulated cracks at a distance of 1-3 m from the bridge. As for IRT application with UAVs, the effect of vibration caused by the drone was investigated. This paper concludes that the hovering flight of drones was extremely stable and IRT with UAVs can provide reliable data for bridge inspections. If IRT is used without hovering flight, the effect of flying speed must be investigated since it might also affect the IRT results. This paper shows great potential of image-based technologies with UAVs for bridge inspections without any traffic disturbance as a complimentary approach to current practices

Modeling The Effect Of Electric Vehicle Adoption On Pedestrian Traffic Safety: An Agent-Based Approach

When operated at low speeds, electric and hybrid vehicles have created pedestrian safety concerns in congested areas of various city centers, because these vehicles have relatively silent engines compared to those of internal combustion engine vehicles, resulting in safety issues for pedestrians and cyclists due to the lack of engine noise to warn them of an oncoming electric or hybrid vehicle. However, the driver behavior characteristics have also been considered in many studies, and the high end-prices of electric vehicles indicate that electric vehicle drivers tend to have a higher prosperity index and are more likely to receive a better education, making them more alert while driving and more likely to obey traffic rules. In this paper, the positive and negative factors associated with electric vehicle adoption and the subsequent effects on pedestrian traffic safety are investigated using an agent-based modeling approach, in which a traffic micro-simulation of a real intersection is simulated in 3D using AnyLogic software. First, the interacting agents and dynamic parameters are defined in the agent-based model. Next, a 3D intersection environment is created to integrate the agent-based model into a visual simulation, where the simulation records the number of near-crashes occurring in certain pedestrian crossings throughout the virtual time duration of a year. A sensitivity analysis is also carried out with 9000 subsequent simulations performed in a supercomputer to account for the variation in dynamic parameters (ambient sound level, vehicle sound level, and ambient illumination). According to the analysis, electric vehicles have a 30% higher pedestrian traffic safety risk than internal combustion engine vehicles under high ambient sound levels. At low ambient sound levels, however, electric vehicles have only a 10% higher safety risk for pedestrians. Low levels of ambient illumination also increase the number of pedestrians involved in near-crashes for both electric vehicles and combustion engine vehicles