{"id":{"repo_id":"vt","oai_identifier":"oai:vtechworks.lib.vt.edu:10919/137704"},"canonical_url":"https://search.dev.ndltd.org/etd/vt/oai:vtechworks.lib.vt.edu:10919/137704","repository":{"repo_id":"vt","name":"Virginia Tech","base_url":"https://vtechworks.lib.vt.edu/oai/request"},"display":{"title":"An Interactive Learning Framework for Understanding Infrastructure Health Monitoring and Leveraging Machine Learning for Safety Improvement","abstract":"Crash mitigation in modern transportation systems requires more than just reactive safety strategies. It demands a comprehensive understanding of how infrastructure health conditions directly influence crash occurrence and severity patterns. As road networks continue to deteriorate and traffic complexity increases, ensuring roadway safety increasingly depends on integrating advanced predictive modeling capabilities with real-time infrastructure health monitoring systems that can detect safety-critical conditions before they contribute to accidents. The first manuscript presents a machine learning framework to predict crash injury severity using real-world crash data and roadway characteristics. By applying models such as Artificial Neural Networks (ANN), Light Gradient Boosting Machine (LightGBM), Random Forest (RF), K-Nearest Neighbors (KNN), and Ordinal Logistic Regression (OLR), the study quantifies the potential injury reduction benefits of infrastructure improvements. These data-driven predictions are validated against established Crash Modification Factors (CMFs), offering a practical method to prioritize safety investments based on injury prevention impact. Recognizing that infrastructure condition directly influences safety outcomes, the second manuscript introduces a vehicle-infrastructure-integrated digital twin platform that enables real-time monitoring of structural health (e.g., strain and vibration). This educational system combines physical sensors, Raspberry Pi microcontrollers, and a live dashboard to simulate intelligent infrastructure capable of interacting with traffic environments. The platform not only supports hands-on learning but also highlights the critical role of continuous infrastructure monitoring in proactive crash mitigation.","abstract_html":"Crash mitigation in modern transportation systems requires more than just reactive safety strategies. It demands a comprehensive understanding of how infrastructure health conditions directly influence crash occurrence and severity patterns. As road networks continue to deteriorate and traffic complexity increases, ensuring roadway safety increasingly depends on integrating advanced predictive modeling capabilities with real-time infrastructure health monitoring systems that can detect safety-critical conditions before they contribute to accidents. The first manuscript presents a machine learning framework to predict crash injury severity using real-world crash data and roadway characteristics. By applying models such as Artificial Neural Networks (ANN), Light Gradient Boosting Machine (LightGBM), Random Forest (RF), K-Nearest Neighbors (KNN), and Ordinal Logistic Regression (OLR), the study quantifies the potential injury reduction benefits of infrastructure improvements. These data-driven predictions are validated against established Crash Modification Factors (CMFs), offering a practical method to prioritize safety investments based on injury prevention impact. Recognizing that infrastructure condition directly influences safety outcomes, the second manuscript introduces a vehicle-infrastructure-integrated digital twin platform that enables real-time monitoring of structural health (e.g., strain and vibration). This educational system combines physical sensors, Raspberry Pi microcontrollers, and a live dashboard to simulate intelligent infrastructure capable of interacting with traffic environments. 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It demands a comprehensive understanding of how infrastructure health conditions directly influence crash occurrence and severity patterns. As road networks continue to deteriorate and traffic complexity increases, ensuring roadway safety increasingly depends on integrating advanced predictive modeling capabilities with real-time infrastructure health monitoring systems that can detect safety-critical conditions before they contribute to accidents. The first manuscript presents a machine learning framework to predict crash injury severity using real-world crash data and roadway characteristics. By applying models such as Artificial Neural Networks (ANN), Light Gradient Boosting Machine (LightGBM), Random Forest (RF), K-Nearest Neighbors (KNN), and Ordinal Logistic Regression (OLR), the study quantifies the potential injury reduction benefits of infrastructure improvements. These data-driven predictions are validated against established Crash Modification Factors (CMFs), offering a practical method to prioritize safety investments based on injury prevention impact. Recognizing that infrastructure condition directly influences safety outcomes, the second manuscript introduces a vehicle-infrastructure-integrated digital twin platform that enables real-time monitoring of structural health (e.g., strain and vibration). This educational system combines physical sensors, Raspberry Pi microcontrollers, and a live dashboard to simulate intelligent infrastructure capable of interacting with traffic environments. 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To prevent these incidents, it is important to understand not only how and why crashes happen, but also how the condition of roads and infrastructure can affect safety. This thesis explores two ways to make our roads safer: predicting where and how serious crashes might be and using technology to observe the health of road conditions in real time. In the first part of this manuscript Logistic Regression and Machine Learning models are used to study traffic crash data and predict how roads change such as adding roundabouts or better lighting can reduce injuries. These predictions help transportation planners make smarter decisions about where to invest in safety improvements. The second manuscript introduces a hands-on system that uses small computers (Raspberry Pi), sensors, and a live dashboard to monitor road and bridge conditions. 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