Open Access. Powered by Scholars. Published by Universities.®
Articles 1 - 5 of 5
Full-Text Articles in Engineering
Predictions Of The Dynamic Complex Modulus Of Non-Conventional Asphalt Concrete Using Machine Learning Techniques, Annie Benson
Predictions Of The Dynamic Complex Modulus Of Non-Conventional Asphalt Concrete Using Machine Learning Techniques, Annie Benson
Electronic Theses and Dissertations
The complex dynamic modulus (|E*|) is a characterization property that defines the stiffness of an asphalt mixture. The dynamic modulus can be found through lab testing or predictions. Since lab testing can be time-consuming and expensive, the prediction method can be used as an alternative method. While a statistical method has been traditionally used for the |E*| prediction such as the Witczak’s predictive equations, machine learning (ML) is recently emerging as an alternative way that |E*| predictions can be made. This research attempted to predict the |E*| using several ML techniques including linear regression, support vector machines (SVM), decision trees, …
Audio-Based Productivity Forecasting Of Construction Cyclic Activities, Chris A. Sabillon
Audio-Based Productivity Forecasting Of Construction Cyclic Activities, Chris A. Sabillon
Electronic Theses and Dissertations
Due to its high cost, project managers must be able to monitor the performance of construction heavy equipment promptly. This cannot be achieved through traditional management techniques, which are based on direct observation or on estimations from historical data. Some manufacturers have started to integrate their proprietary technologies, but construction contractors are unlikely to have a fleet of entirely new and single manufacturer equipment for this to represent a solution. Third party automated approaches include the use of active sensors such as accelerometers and gyroscopes, passive technologies such as computer vision and image processing, and audio signal processing. Hitherto, most …
Analytical Study Of Computer Vision-Based Pavement Crack Quantification Using Machine Learning Techniques, Soroush Mokhtari
Analytical Study Of Computer Vision-Based Pavement Crack Quantification Using Machine Learning Techniques, Soroush Mokhtari
Electronic Theses and Dissertations
Image-based techniques are a promising non-destructive approach for road pavement condition evaluation. The main objective of this study is to extract, quantify and evaluate important surface defects, such as cracks, using an automated computer vision-based system to provide a better understanding of the pavement deterioration process. To achieve this objective, an automated crack-recognition software was developed, employing a series of image processing algorithms of crack extraction, crack grouping, and crack detection. Bottom-hat morphological technique was used to remove the random background of pavement images and extract cracks, selectively based on their shapes, sizes, and intensities using a relatively small number …
Data-Driven Simulation Modeling Of Construction And Infrastructure Operations Using Process Knowledge Discovery, Reza Akhavian
Data-Driven Simulation Modeling Of Construction And Infrastructure Operations Using Process Knowledge Discovery, Reza Akhavian
Electronic Theses and Dissertations
Within the architecture, engineering, and construction (AEC) domain, simulation modeling is mainly used to facilitate decision-making by enabling the assessment of different operational plans and resource arrangements, that are otherwise difficult (if not impossible), expensive, or time consuming to be evaluated in real world settings. The accuracy of such models directly affects their reliability to serve as a basis for important decisions such as project completion time estimation and resource allocation. Compared to other industries, this is particularly important in construction and infrastructure projects due to the high resource costs and the societal impacts of these projects. Discrete event simulation …
Analyses Of Crash Occurence And Injury Severities On Multi Lane Highways Using Machine Learning Algorithms, Abhishek Das
Analyses Of Crash Occurence And Injury Severities On Multi Lane Highways Using Machine Learning Algorithms, Abhishek Das
Electronic Theses and Dissertations
Reduction of crash occurrence on the various roadway locations (mid-block segments; signalized intersections; un-signalized intersections) and the mitigation of injury severity in the event of a crash are the major concerns of transportation safety engineers. Multi lane arterial roadways (excluding freeways and expressways) account for forty-three percent of fatal crashes in the state of Florida. Significant contributing causes fall under the broad categories of aggressive driver behavior; adverse weather and environmental conditions; and roadway geometric and traffic factors. The objective of this research was the implementation of innovative, state-of-the-art analytical methods to identify the contributing factors for crashes and injury …