Combination of GIS and SHM in prognosis and diagnosis of bridges in earthquake-prone locations

Bridge infrastructures are essential nodes in the transportation network. In earthquake-prone areas, seismic performance assessment of infrastructure is vital to identify, retrofit, reconstruct, or, if necessary, demolish the structural systems based on optimal decision-making processes. This research proposes the combined use of advanced tools used in the management and monitoring of bridges such as Geographical Information Systems (GIS) and Structural Health Monitoring (SHM) in a synergistic manner that can enable observation of bridges to construct an earthquake damage model. Post-earthquake disaster data can enhance and update this model to mitigate further damages both to the structure and transportation network in the future. Implications of new technologies such as drones and mobile devices in this scheme constitute the next step toward the future of the Cyber-Physical SHM systems. The proposed intelligent and sustainable cloud-based framework of SHM-GIS in this paper lays the core behind more robust impending systems. The synergistic behavior of the offered framework reduces the overall cost in large scale implementation and increases the accuracy of the results leading to a decision-making platform easing the management of bridges

Dashcam-Enabled Deep Learning Applications for Airport Runway Pavement Distress Detection

23-8193Pavement distress detection plays a vital role in ensuring the safety and longevity of runway infrastructure. This project presents a comprehensive approach to automate distress detection and geolocation on runway pavement using state-of-the-art deep learning techniques. A Faster R-CNN model is trained to accurately identify and classify various distress types, including longitudinal and transverse cracking, weathering, rutting, and depression. The developed model is deployed on a dataset of high-resolution dashcam images captured along the runway, allowing for real-time detection of distresses. Geolocation techniques are employed to accurately map the distresses onto the runway pavement in real-world coordinates. The system implementation and deployment are discussed, emphasizing the importance of a seamless integration into existing infrastructure. The developed distress detection system offers significant benefits to the Utah Department of Transportation (UDOT) by enabling proactive maintenance planning, optimizing resource allocation, and enhancing runway management capabilities. Future potential for advanced distress analysis, integration with other data sources, and continuous model improvement are also explored. The project showcases the potential of low-cost dashcam solutions combined with deep learning for efficient and cost-effective runway distress detection and management

Mobi̇l yapı sağlığı taki̇p destekli̇ akıllı ulaşım ağları ve köprü performans değerlendi̇rmesi̇

Bridge infrastructures are critical nodes in a transportation network. In earthquake-prone areas, seismic performance assessment of infrastructure is essential to identify, retrofit, reconstruct, or, if necessary, demolish the infrastructure systems based on optimal decision-making processes. As one of the crucial components of the transportation network, any bridge failure would impede the post-earthquake rescue operation. Not only the failure of such high-risk critical components during an extreme event can lead to significant direct damages, but it also affects the transportation road network. The consequences of these secondary effects can easily lead to congestion and long queues if the performance of the transportation system before or after an event was not analyzed. These indirect losses can be more prominent compared to the actual damage to bridges. Recent technological advancement in mobile sensors such as smartphones and Structural Health Monitoring (SHM) brought the opportunity to improve the accuracy of mathematical models by using experimental data and model calibration with field measurements. Engaging mobile SHM platforms with Intelligent Transportation System (ITS) and Geographical Information Systems (GIS), one can develop cost-effective and sustainable transportation infrastructure monitoring solutions targeting structural and transportation network resiliency. In line with this notion, this thesis study brings about seismic performance assessment for the Northern Cyprus transportation network from which the decision-making platform can be modeled and implemented based on the combination of SHM and ITS. This study employs a seismic hazard analysis based on generated USGS ShakeMap scenarios for the risk assessment of the transportation network. Furthermore, identification of the resiliency and vulnerability of transportation road network is carried out by utilizing the Graph Theory concept at the network level. Moreover, link performance measures, i.e., traffic modeling of the study region is simulated in a Dynamic Traffic Assignment (DTA) simulation environment. Finally, for earthquake loss analysis of the bridges, the Hazus loss estimation tool is used. The case study of this thesis is the Western part of Northern Cyprus, comprising 20 bridges with a transportation network that is consisting of 134 links and 94 nodes with a total length of about 174 km. The results of our investigations for three different earthquake scenarios have shown that seismic retrofitting of bridges is a cost-effective measure to reduce the structural and operational losses in the region.Köprü altyapıları, ulaşım ağındaki kritik noktalardır. Depreme meyilli alanlarda, altyapının sismik performans değerlendirmesi, optimum karar verme süreçlerine dayalı olarak altyapı sistemlerini belirlemek, güçlendirmek, yeniden inşa etmek veya gerekirse yıkmak için gereklidir. Ulaşım ağının en önemli bileşenlerinden biri olan köprülerdeki herhangi bir arıza, deprem sonrası kurtarma operasyonunu engelleyecektir. Bu tür yüksek riskli kritik bileşenlerin olağandışı bir olay sırasında arızalanması doğrudan ve belirgin hasarlara yol açmakla kalmaz, aynı zamanda ulaşım yolu ağını da etkiler. Bu ikincil etkilerin sonuçları, ulaşım sisteminin performansı bir olaydan önce veya sonra analiz edilmemişse kolayca tıkanıklığa ve uzun kuyruklara yol açabilir. Dolaylı kayıplar, köprülerde oluşan gerçek hasarla karşılaştırıldığında daha mühim olabilir. Akıllı telefonlar ve Yapı Sağlığı İzleme (YSİ) gibi mobil sensörlerdeki son teknolojik gelişmeler, deneysel verileriyle ve saha ölçümleriyle model kalibrasyonunu kullanarak matematiksel modellerin doğruluğunu geliştirme fırsatı verdi. Mobil YSİ platformlarını Akıllı Ulaşım Sistemi (AUS) ve Coğrafi Bilgi Sistemleri (CBS) ile birleştirerek, yapısal ve ulaşım ağı dayanıklılığını hedefleyen uygun maliyetli ve sürdürülebilir ulaşım altyapısı izleme çözümleri geliştirilebilir. Bu fikir doğrultusunda, bu tez çalışması, Kuzey Kıbrıs ulaşım ağı için, YSİ ve AUS kombinasyonuna dayalı karar verme platformunun modellenip uygulanabileceği sismik performans değerlendirmesi yapmaktadır. Bu çalışma, oluşturulan USGS ShakeMap senaryolarına dayalı bir sismik tehlike analizi kullanır. Ayrıca, ulaşım yolu ağının dayanıklılığı ve kırılganlığının belirlenmesi, ağ düzeyinde Grafik Teorisi kavramı kullanılarak gerçekleştirilmiştir. Ayrıca, çalışma bölgesinin trafik modellemesi gibi olan bağlantı performans ölçümleri, Dinamik Trafik Atama (DTA) simülasyon ortamında simüle edilmiştir. Son olarak, köprülerin deprem kayıp analizi için Hazus kayıp tahmin aracı kullanılmıştır. Bu tezin vaka çalışması, toplam uzunluğu yaklaşık 174 km olan 134 bağlantı ve 94 bağlantı noktasından oluşan ulaşım ağına sahip 20 köprüden oluşan Kuzey Kıbrıs'ın batı kesimini kapsamaktadır. Üç farklı deprem senaryosu için yapılan araştırmanın sonuçları, köprülerin depreme karşı güçlendirilmesinin bölgedeki yapısal ve operasyonel kayıpları azaltmak için uygun maliyetli bir önlem olduğunu göstermiştir.M.S. - Master of Scienc

Smart parking in IoT-enabled cities: A survey

The rapid growth in population has led to substantial traffic bottlenecks in recent transportation systems. This not only causes significant air pollution, and waste in time and energy, but also signifies the issue of the auto-park scarcity. In the age of Internet of Things (IoT) and smart city ecosystems, smart parking and relevant innovative solutions are necessary towards more sustainable future cities. Smart parking with the help of sensors embedded in cars and city infrastructures can alleviate the deadlocks in parking problems and provide the best quality of services and profit to citizens. However, several design aspects should be well investigated and analyzed before implementing such solutions. In this paper, we classify the smart parking systems while considering soft and hard design factors. We overview the enabling technologies and sensors which have been commonly used in the literature. We emphasize the importance of data reliability, security, privacy and other critical design factors in such systems. Emerging parking trends in the ecosystem are investigated, while focusing on data interoperability and exchange. We also outline open research issues in the current state of smart parking systems and recommend a conceptual hybrid-parking model

Bridge Network Seismic Risk Assessment Using ShakeMap/HAZUS with Dynamic Traffic Modeling

Bridge infrastructures are critical nodes in a transportation network. In earthquake-prone areas, seismic performance assessment of infrastructure is essential to identify, retrofit, reconstruct, or, if necessary, demolish the infrastructure systems based on optimal decision-making processes. As one of the crucial components of the transportation network, any bridge failure would impede the post-earthquake rescue operation. Not only the failure of such high-risk critical components during an extreme event can lead to significant direct damages, but it also affects the transportation road network. The consequences of these secondary effects can easily lead to congestion and long queues if the performance of the transportation system before or after an event was not analyzed. These indirect losses can be more prominent compared to the actual damage to bridges. This paper brings about seismic performance assessment for the Cyprus transportation network from which the decision-making platform can be modeled and implemented. This study employs a seismic hazard analysis based on generated USGS ShakeMap scenarios for the risk assessment of the transportation network. Furthermore, identification of the resiliency and vulnerability of the transportation road network is carried out by utilizing the graph theory concept at the network level. Moreover, link performance measures, i.e., traffic modeling of the study region is simulated in a dynamic traffic assignment (DTA) simulation environment. Finally, for earthquake loss analysis of the bridges, the HAZUS loss estimation tool is used. The results of our investigations for three different earthquake scenarios have shown that seismic retrofitting of bridges is a cost-effective measure to reduce the structural and operational losses in the region

Bridge Network Seismic Risk Assessment Using ShakeMap/HAZUS with Dynamic Traffic Modeling

Bridge infrastructures are critical nodes in a transportation network. In earthquake-prone areas, seismic performance assessment of infrastructure is essential to identify, retrofit, reconstruct, or, if necessary, demolish the infrastructure systems based on optimal decision-making processes. As one of the crucial components of the transportation network, any bridge failure would impede the post-earthquake rescue operation. Not only the failure of such high-risk critical components during an extreme event can lead to significant direct damages, but it also affects the transportation road network. The consequences of these secondary effects can easily lead to congestion and long queues if the performance of the transportation system before or after an event was not analyzed. These indirect losses can be more prominent compared to the actual damage to bridges. This paper brings about seismic performance assessment for the Cyprus transportation network from which the decision-making platform can be modeled and implemented. This study employs a seismic hazard analysis based on generated USGS ShakeMap scenarios for the risk assessment of the transportation network. Furthermore, identification of the resiliency and vulnerability of the transportation road network is carried out by utilizing the graph theory concept at the network level. Moreover, link performance measures, i.e., traffic modeling of the study region is simulated in a dynamic traffic assignment (DTA) simulation environment. Finally, for earthquake loss analysis of the bridges, the HAZUS loss estimation tool is used. The results of our investigations for three different earthquake scenarios have shown that seismic retrofitting of bridges is a cost-effective measure to reduce the structural and operational losses in the region