2875 research outputs found
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Tasks Prompts
No full textThis data set contains the prompts for the writing tasks the students completed (three fictional pieces of writing in French, and two metalinguistic reflections in English)
Texas Birdge M&R Data
No full textThe M&R dataset that included information of 489 bridges and bridge-class culverts that recevied M&R actions in the 2014-2016 time period
Supplemental Materials
No full textThis folder includes materials developed in support of a scoping review exploring the efficacy of social work interventions with patients in hospital settings. Included in this folder are the full search strategy employed including dates and numbers of search results, a bibliography of included studies, a PRISMA diagram, eligibility criteria, and coding documents
Tsung-Jui Wu PhD Dissertation Appendix
No full textThis dataset includes supplemental materials of the Ph.D. thesis of Tsung-Jui Wu (UH ID: 1618063). Four appendices are listed below:
Appendix A Compiled published age and Nd-Sr isotopic composition of late Cretaceous to Cenozoic NE Asia igneous rock
Appendix B Data and reference of the early Cretaceous adakitic rocks in NE Asia
Appendix C New zircon U-Pb age result of Sikhote-Alin igneous rocks
Appendix D Whole-rock geochemical result of SIkhote-Alin igneous rock
Stiffness and Strength of Stabilized Organic Soils—Part II/II: Parametric Analysis and Modeling with Machine Learning
No full textPredicting the range of achievable strength and stiffness from stabilized soil mixtures is critical for engineering design and construction, especially for organic soils, which are often considered “unsuitable” due to their high compressibility and the lack of knowledge about their mechanical behavior after stabilization. This study investigates the mechanical behavior of stabilized organic soils using machine learning (ML) methods. ML algorithms were developed and trained using a database from a comprehensive experimental study (see Part I), including more than one thousand unconfined compression tests on organic clay samples stabilized by wet soil mixing (WSM) technique. Three different ML methods were adopted and compared, including two artificial neural networks (ANN) and a linear regression method. ANN models proved reliable in the prediction of the stiffness and strength of stabilized organic soils, significantly outperforming linear regression models. Binder type, mixing ratio, soil organic and water content, sample size, aging, temperature, relative humidity, and carbonation were the control variables (input parameters) incorporated into the ML models. The impacts of these factors were evaluated through rigorous ANN-based parametric analyses. Additionally, the nonlinear relations of stiffness and strength with these parameters were developed, and their optimum ranges were identified through the ANN models. Overall, the robust ML approach presented in this paper can significantly improve the mixture design for organic soil stabilization and minimize the experimental cost for implementing WSM in engineering projects
Sand-traps: weight and grain-size
No full textSample weight and grain-size data from sand traps samples:
excel table and pdf notes
Supplemental Files 5
No full textThis data set contains the Matlab files to conduct the material characterization and parameter identification
Ch. 3 - Socioeconomic Impacts of Changes in Storage
No full textModel code and supporting data associated with the third chapter of Justin C. Thompson's Ph.D. dissertation regarding socioeconomic impacts of changing aquifer storage conditions developed from the Groundwater Recoverability Model (GRM). Originally authored for Python 3 in Jupyter Notebooks. Aquifer parameters applied to this study and provided here are exported from the groundwater availability model developed by the Texas Water Development Board in ASCII format. Well data is provided here from the Texas Water Development Board - Submitted Driller's Report Database. Results generated by this study are provided in csv format