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Predicting Pavement Structural Condition Using Machine Learning Methods, Nazmus Sakib Ahmed
Predicting Pavement Structural Condition Using Machine Learning Methods, Nazmus Sakib Ahmed
Theses and Dissertations
State departments of transportation recognize the need to incorporate pavement structural condition in their pavement performance models and/or decision processes used to select candidate projects for preservation, rehabilitation, or reconstruction at the network level. However, pavement structural condition data are costly to obtain. To this end, this paper develops and evaluates the effectiveness of two machine learning methods, Random Forest (RF) and eXtreme Gradient Boosting (XGBoost), for predicting a flexible pavement’s structural condition. The aim is to be able to predict whether a pavement section’s structural condition is poor or not based on Annual Average Daily Traffic (AADT), truck percentage, …
Evaluation Of Temporal Damage Progression In Concrete Structures Affected By Asr Using Data-Driven Methods, Vafa Soltangharaei
Evaluation Of Temporal Damage Progression In Concrete Structures Affected By Asr Using Data-Driven Methods, Vafa Soltangharaei
Theses and Dissertations
Alkali-silica reaction (ASR) is a chemical reaction, which causes damage in concrete structures such as bridges, dams, and nuclear containments and powerplant structures. The ASR-induced damage may endanger the integrity and serviceability of structures. Several methods such as visual inspection, petrographic analysis, demountable mechanical strain gauges, and cracking index have been utilized for study the effect of ASR on structures, which are not always efficient in early damage detection and some are destructive and prohibited in nuclear structures. Nondestructive methods and structural health monitoring techniques can be alternatives for the condition assessment of structures. Among the nondestructive methods, acoustic emission …