Hosam Salman

ORCID Identifier(s)

0000-0003-4221-0607

Graduation Semester and Year

2021

Language

English

Document Type

Dissertation

Degree Name

Doctor of Philosophy in Civil Engineering

Department

Civil Engineering

First Advisor

Anand Puppala

Second Advisor

Bhaskar Chittoori

Abstract

Precise predictions of the design capacities of drilled shafts on weak rock formations is vital for a better design of foundations. Irregularities in rock formations due to weathering cause discrepancies in actual bearing capacities and evaluating the bearing capacity typically requires frequent in-situ testing, using load test shafts at smaller intervals, which is time consuming and expensive. The typical design in Texas currently utilizes in-situ design charts that are developed based on Texas Cone Penetrometer (TCP) blow counts. This approach is found to have limitations related to maximum number of blows, rock characterization, and difficulty in incorporating influential parameters. Problematic soils in the North Central Texas region often lead practitioners to choose deep foundations, but the current design charts do not distinguish between the various types of rock or degradable materials such as intermediate geomaterials (IGM) or shale formations,and the software currently used for deep foundation capacity design does not incorporate all of the influential parameters.The purpose of this study is to better predict and assess the design parameters that are being used for deep foundation design and to improve the current drilled shaft design by either modifying the existing charts or developing new design charts for various types of rock formations such as limestone, sandstone, and intermediate geomaterials (IGM) such as shales. (IGMs are very soft to-medium hard rocks such as shale.) The principal goal and contribution of this research is to improve the methods for predicting drilled shaft design capacities by accounting for the geologic formations and learning from past projects. The following objectives were achieved to fulfill this goal: I. A database was compiled to employ statistical learning methods, as the applicability of a statistical learning outcome relies heavily on the type of database used and there were no databases that contained site investigation data in conjunction with load test data from drilled shaft projects. A database of site investigation data and load test data was compiled from projects undertaken in various geologic formations including shale,Woodbine shale, sandstone, and limestone. About 22 projects used for regression and validation, and approximately 38 load tests were performed for this research.II. Statistical correlations were developed between TCP, unconfined (uniaxial) compressive strength (Qun), dry unit weight, moisture content, recovery, rock quality designation, skin friction, and end bearing. About 96 predicted equations were developed to accommodate the various geologic formations. III. Design charts were developed to predict drilled shaft capacities, accounting for geologic formations and weathered conditions. This research discusses the advantages of the most recent load tests performed on drilled shafts in various design-build projects in the United States and on various types of geologic formations. Several loads tests, such as the Statnamic and bi-directional (O-cell) load cell tests (Osterberg) have been performed on various geologic formations distributed across the Dallas Fort Worth area. Various tables and plots were presented for each formation to show the Pearson correlations matrix and data distribution of each engineering parameter. The research demonstrated how the data were classified and sorted into 45 subsets per three classification systems per the FHWA guidelines to ensure the identification of the best fit regression and model of each geologic formation. It also discussed the challenges of the database such as missing data and outliers, provided methods to resolve these challenges, and showed the distribution of the engineering parameters. Over-designed (OD) or under-predicted (UP) and under designed (UD) or over-predicted (UP) foundation capacities are two common forms of negative variabilities that need to be avoided. Reducing the OD and UD requires the correct assessment of the rock quality and properties. It is always recommended to perform more field load testing to better optimize and predict the appropriate design charts.This research can be directly applied to many bridge design and foundation projects across the globe to ensure that the projects are designed to be safe, long lasting and provides cost savings to the agencies.

Keywords

Drilled shafts, Statnamic, Bi-directional load cell testing, Osterberg load test, Limestone, Sandstone, Intermediate geomaterials (IGM), Shale, Rock, Texas cone penetrometer (TCP), Correlations, Regressions, Database, Site investigation, Texas

Disciplines

Civil and Environmental Engineering | Civil Engineering | Engineering

Comments

Degree granted by The University of Texas at Arlington

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