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Full-Text Articles in Architecture
Prediction Of Self-Consolidating Concrete Properties Using Xgboost Machine Learning Algorithm: Part 1–Workability, Amine El Mahdi Safhi, Hamed Dabiri, Ahmed Soliman, Kamal Khayat
Prediction Of Self-Consolidating Concrete Properties Using Xgboost Machine Learning Algorithm: Part 1–Workability, Amine El Mahdi Safhi, Hamed Dabiri, Ahmed Soliman, Kamal Khayat
Civil, Architectural and Environmental Engineering Faculty Research & Creative Works
The Interest in Implementing Self-Consolidating Concrete (SCC) in Major Construction Projects Has Increased Significantly in Recent Years. This Paper Reports the Results of an Extensive Survey of Experimental Data of More Than 1700 SCC Mixtures from over 100 Studies Published in the Last Decade. the Survey Included the SCC Mixture Proportioning, Key Fresh Properties Including Flowability, Passing Ability, and Segregation Resistance, as Well as Some of the Derived Properties (E.g., Paste Volume). the Statistical Analysis of the Reported Parameters Showed Wide Variations in Values. the Outcome of the Survey Indicates that SCC Mixture Design and Workability Properties Do Not Systematically …
Classifying Diseases Affecting Gait With Body Acceleration-Based Machine Learning Models, Mohammad Ali Takallou
Classifying Diseases Affecting Gait With Body Acceleration-Based Machine Learning Models, Mohammad Ali Takallou
Dissertations and Doctoral Documents from University of Nebraska-Lincoln, 2023–
This Ph.D. dissertation introduces a comprehensive framework designed to harness acceleration data as a uniquely valuable tool for early disease classification, specifically focusing on gait-related diseases. In the modern healthcare landscape, timely and accurate classification of such diseases is paramount, as it can significantly impact treatment outcomes and patient quality of life. As a compelling case study, we conducted a meticulous experiment to identify individuals afflicted with peripheral artery disease (PAD) and classify them from those without PAD. Our framework leverages acceleration data extracted from strategically placed anatomical reflective markers, including locations such as the sacrum, to train sophisticated classification …