Horizontal Directional Drilling (HDD) is a growing and expanding trenchless method utilized to install pipelines from 2 to 60 inch diameters for lengths over 10,000 foot. To date, there are not many public documents where direct costs and bid prices incurred by HDD installations are available and analyzed. The objective is to provide a better understanding of the factors affecting the bid prices of these projects. The first section of the thesis analyzes how project parameters such as product diameter, bore length and soil conditions affect the bid price of water and wastewater pipeline installations using HDD. Through multiple linear regressions, the effect of project parameters on bid prices of small, medium and large rigs projects is extracted. The results were further investigated to gain a better understanding of bid factors that influence the relationship between total cost and the project parameters. The second section uses unit cost, based on bid prices, to compare the costs incurred by defined categories. Parameters such as community type, product type, soil conditions, and geographical region were used in the analysis. Furthermore, using average unit cost from 2001 to 2009, HDD project cost trends are briefly analyzed against the main variations of the US economy from the same time horizon by using economic indicators. It was determined that project geometric factors influence more the bid price of small rig projects than large rig projects because external factors including market rates and economic situation have an increasing impact on bid prices when rig size increases. It was observed that bid price variation of HDD projects over years followed the same trend as the US economic variation described by economic indicators.

Fluctuating flow releases on regulated rivers destabilize downstream riverbanks, causing unintended, unnatural, and uncontrolled geomorphologic changes. These flow releases, usually a result of upstream hydroelectric dam operations, create manmade tidal effects that cause significant environmental damage; harm fish, vegetation, mammal, and avian habitats; and destroy riverbank camping and boating areas. This work focuses on rivers regulated by hydroelectric dams and have banks formed by sediment processes. For these systems, bank failures can be reduced, but not eliminated, by modifying flow release schedules. Unfortunately, comprehensive mitigation can only be accomplished with expensive rebuilding floods which release trapped sediment back into the river. The contribution of this research is to optimize weekly hydroelectric dam releases to minimize the cost of annually mitigating downstream bank failures. Physical process modeling of dynamic seepage effects is achieved through a new analytical unsaturated porewater response model that allows arbitrary periodic stage loading by Fourier series. This model is incorporated into a derived bank failure risk model that utilizes stochastic parameters identified through a meta-analysis of more than 150 documented slope failures. The risk model is then expanded to the river reach level by a Monte Carlos simulation and nonlinear regression of measured attenuation effects. Finally, the comprehensive risk model is subjected to a simulated annealing (SA) optimization scheme that accounts for physical, environmental, mechanical, operations, and flow constraints. The complete risk model is used to optimize the weekly flow release schedule of the Glen Canyon Dam, which regulates flow in the Colorado River within the Grand Canyon. A solution was obtained that reduces downstream failure risk, allows annual rebuilding floods, and predicts a hydroelectric revenue increase of more than 2%.

The construction industry is becoming more aware of its impact on the environment. It has become more sensitive to how it operates and how it can reduce the carbon footprint of the construction process. This research identifies the source of and quantities of the carbon emissions created by an operating modular home fabrication plant in producing, transporting and installing modular structures. This study demonstrates how to measure the carbon footprint created in the production of a modular home. It quantifies and reports the results on a home, on a single module and on a per square foot basis. The primary conclusions of this study are: a) electricity was found to be the largest energy source used in this fabrication process; b) the modular fabrication process consumes a significant amount of electrical energy per month; c) production volume has a bearing on the carbon footprint of each home since the carbon footprint for each period is allocated to every home produced in that period; and d) transportation of fabricated modules and set-up add to the carbon footprint. Further, a carbon calculator was produced and is included with the study. The tool calculates the impact of energy consumption on the carbon footprint of a modular factory or a modular home. It may be expanded to other process driven fabrication entities. This research is valuable to developers and builders who wish to measure the carbon impact of a modular new home delivery system. The study also provides a methodology for modular home fabricators to measure the carbon footprint of their factories and factory production.

Front End Planning (FEP) is a critical process for uncovering project unknowns, while developing adequate scope definition following a structured approach for the project execution process. FEP for infrastructure projects assists in identifying and mitigating issues such as right-of-way concerns, utility adjustments, environmental hazards, logistic problems, and permitting requirements. This thesis describes a novel and effective risk management tool that has been developed by the Construction Industry Institute (CII) called the Project Definition Rating Index (PDRI) for infrastructure projects. Input from industry professionals from over 30 companies was used in the tool development which is specifically focused on FEP. Data from actual projects are given showing the efficacy of the tool. Critical success factors for FEP of infrastructure projects are shared. The research shows that a finite and specific list of issues related to scope definition of infrastructure projects can be developed. The thesis also concludes that the PDRI score indicates the current level of scope definition and corresponds to project performance. Infrastructure projects with low PDRI scores outperform projects with high PDRI scores.

Rapid developments are occurring in the arena of activity-based microsimulation models. Advances in computational power, econometric methodologies and data collection have all contributed to the development of microsimulation tools for planning applications. There has also been interest in modeling child daily activity-travel patterns and their influence on those of adults in the household using activity-based microsimulation tools. It is conceivable that most of the children are largely dependent on adults for their activity engagement and travel needs and hence would have considerable influence on the activity-travel schedules of adult members in the household. In this context, a detailed comparison of various activity-travel characteristics of adults in households with and without children is made using the National Household Travel Survey (NHTS) data. The analysis is used to quantify and decipher the nature of the impact of activities of children on the daily activity-travel patterns of adults. It is found that adults in households with children make a significantly higher proportion of high occupancy vehicle (HOV) trips and lower proportion of single occupancy vehicle (SOV) trips when compared to those in households without children. They also engage in more serve passenger activities and fewer personal business, shopping and social activities. A framework for modeling activities and travel of dependent children is proposed. The framework consists of six sub-models to simulate the choice of going to school/pre-school on a travel day, the dependency status of the child, the activity type, the destination, the activity duration, and the joint activity engagement with an accompanying adult. Econometric formulations such as binary probit and multinomial logit are used to obtain behaviorally intuitive models that predict children's activity skeletons. The model framework is tested using a 5% sample of a synthetic population of children for Maricopa County, Arizona and the resulting patterns are validated against those found in NHTS data. Microsimulation of these dependencies of children can be used to constrain the adult daily activity schedules. The deployment of this framework prior to the simulation of adult non-mandatory activities is expected to significantly enhance the representation of the interactions between children and adults in activity-based microsimulation models.

This dissertation research is concerned with the study of two important traffic phenomena; merging and lane-specific traffic behavior. First, this research investigates merging traffic behavior through empirical analysis and evaluation of freeway merge ratios. Merges are important components of freeways and traffic behavior around them have a significant impact in the evolution and stability of congested traffic. At merges, drivers from conflicting traffic branches take turns to merge into a single stream at a rate referred to as the “merge ratio”. In this research, data from several freeway merges was used to evaluate existing macroscopic merge models and theoretical principles of merging behavior. Findings suggest that current merge ratio estimation methods can be insufficient to represent site-specific merge ratios, due to observed within-site variations and unaccounted effects of downstream merge geometry. To overcome these limitations, merge ratios were formulated based on their site-specific lane flow distribution (LFD), the proportion of flow in each freeway lane, for two types of merge geometries. Results demonstrate that the proposed methods are able to improve merge ratio estimates, reproduce within-site variations of merge ratio, and represent more effectively disproportionate redistribution of merging flow for merges where vehicles compete directly to merge due a downstream lane reduction.

Second, this research investigates lane-specific traffic behavior through empirical analysis and statistical modeling of lane flow distribution. Lane-specific traffic behavior is also an important component in evaluating freeway performance and has a significant impact in the mechanism of queue evolution, particularly around merges, and bottleneck discharge rate. In this research, site-specific linear LFD trends of three-lane congested freeways were investigated and modeled. A large-scale data collection process was implemented to systematically characterize the effects of several traffic and geometric features of freeways in the occurrence of between-site LFD variations. Also, an innovative three-stage modeling framework was used to model LFD behavior using multiple logistic regression to describe between-site LFD variations and Dirichlet regression to model recurrent combinations of linear LFD trends. This novel approach is able to represent both between and within site variations of LFD trends better, while accounting for the unit-sum constraint and distribution assumptions inherent of proportions data. Results revealed that proximity to freeway merges, a site’s level of congestion, and the presence of HOV lanes are significant factors that influence site-specific recurrent LFD behavior.

Findings from this work significantly improve the state-of-the-art knowledge on merging and lane-specific traffic behavior, which can help to improve traffic operations and reduce traffic congestion in freeways.

Vegetative filter strips (VFS) are an effective methodology used for storm water management particularly for large urban parking lots. An optimization model for the design of vegetative filter strips that minimizes the amount of land required for stormwater management using the VFS is developed in this study. The resulting optimization model is based upon the kinematic wave equation for overland sheet flow along with equations defining the cumulative infiltration and infiltration rate.

In addition to the stormwater management function, Vegetative filter strips (VFS) are effective mechanisms for control of sediment flow and soil erosion from agricultural and urban lands. Erosion is a major problem associated with areas subjected to high runoffs or steep slopes across the globe. In order to effect economy in the design of grass filter strips as a mechanism for sediment control & stormwater management, an optimization model is required that minimizes the land requirements for the VFS. The optimization model presented in this study includes an intricate system of equations including the equations defining the sheet flow on the paved and grassed area combined with the equations defining the sediment transport over the vegetative filter strip using a non-linear programming optimization model. In this study, the optimization model has been applied using a sensitivity analysis of parameters such as different soil types, rainfall characteristics etc., performed to validate the model

Engineering education can provide students with the tools to address complex, multidisciplinary grand challenge problems in sustainable and global contexts. However, engineering education faces several challenges, including low diversity percentages, high attrition rates, and the need to better engage and prepare students for the role of a modern engineer. These challenges can be addressed by integrating sustainability grand challenges into engineering curriculum.

Two main strategies have emerged for integrating sustainability grand challenges. In the stand-alone course method, engineering programs establish one or two distinct courses that address sustainability grand challenges in depth. In the module method, engineering programs integrate sustainability grand challenges throughout existing courses. Neither method has been assessed in the literature.

This thesis aimed to develop sustainability modules, to create methods for evaluating the modules’ effectiveness on student cognitive and affective outcomes, to create methods for evaluating students’ cumulative sustainability knowledge, and to evaluate the stand-alone course method to integrate sustainability grand challenges into engineering curricula via active and experiential learning.

The Sustainable Metrics Module for teaching sustainability concepts and engaging and motivating diverse sets of students revealed that the activity portion of the module had the greatest impact on learning outcome retention.

The Game Design Module addressed methods for assessing student mastery of course content with student-developed games indicated that using board game design improved student performance and increased student satisfaction.

Evaluation of senior design capstone projects via novel comprehensive rubric to assess sustainability learned over students’ curriculum revealed that students’ performance is primarily driven by their instructor’s expectations. The rubric provided a universal tool for assessing students’ sustainability knowledge and could also be applied to sustainability-focused projects.

With this in mind, engineering educators should pursue modules that connect sustainability grand challenges to engineering concepts, because student performance improves and students report higher satisfaction. Instructors should utilize pedagogies that engage diverse students and impact concept retention, such as active and experiential learning. When evaluating the impact of sustainability in the curriculum, innovative assessment methods should be employed to understand student mastery and application of course concepts and the impacts that topics and experiences have on student satisfaction.

A simplified bilinear moment-curvature model are derived based on the moment-curvature response generated from a parameterized stress-strain response of strain softening and or strain-hardening material by Dr. Barzin Mobasher and Dr. Chote Soranakom. Closed form solutions are developed for deflection calculations of determinate beams subjected to usual loading patterns at any load stage. The solutions are based on a bilinear moment curvature response characterized by the flexural crack initiation and ultimate capacity based on a deflection hardening behavior. Closed form equations for deflection calculation are presented for simply supported beams under three point bending, four point bending, uniform load, concentrated moment at the middle, pure bending, and for cantilever beam under a point load at the end, a point load with an arbitrary distance from the fixed end, and uniform load. These expressions are derived for pre-cracked and post cracked regions. A parametric study is conducted to examine the effects of moment and curvature at the ultimate stage to moment and curvature at the first crack ratios on the deflection. The effectiveness of the simplified closed form solution is demonstrated by comparing the analytical load deflection response and the experimental results for three point and four point bending. The simplified bilinear moment-curvature model is modified by imposing the deflection softening behavior so that it can be widely implemented in the analysis of 2-D panels. The derivations of elastic solutions and yield line approach of 2-D panels are presented. Effectiveness of the proposed moment-curvature model with various types of panels is verified by comparing the simulated data with the experimental data of panel test.

The need for rapid, specific and sensitive assays that provide a detection of bacterial indicators are important for monitoring water quality. Rapid detection using biosensor is a novel approach for microbiological testing applications. Besides, validation of rapid methods is an obstacle in adoption of such new bio-sensing technologies. In this study, the strategy developed is based on using the compound 4-methylumbelliferyl glucuronide (MUG), which is hydrolyzed rapidly by the action of E. coli β-D-glucuronidase (GUD) enzyme to yield a fluorogenic product that can be quantified and directly related to the number of E. coli cells present in water samples. The detection time required for the biosensor response ranged from 30 to 120 minutes, depending on the number of bacteria. The specificity of the MUG based biosensor platform assay for the detection of E. coli was examined by pure cultures of non-target bacterial genera and also non-target substrates. GUD activity was found to be specific for E. coli and no such enzymatic activity was detected in other species. Moreover, the sensitivity of rapid enzymatic assays was investigated and repeatedly determined to be less than 10 E. coli cells per reaction vial concentrated from 100 mL of water samples. The applicability of the method was tested by performing fluorescence assays under pure and mixed bacterial flora in environmental samples. In addition, the procedural QA/QC for routine monitoring of drinking water samples have been validated by comparing the performance of the biosensor platform for the detection of E. coli and culture-based standard techniques such as Membrane Filtration (MF). The results of this study indicated that the fluorescence signals generated in samples using specific substrate molecules can be utilized to develop a bio-sensing platform for the detection of E. coli in drinking water. The procedural QA/QC of the biosensor will provide both industry and regulatory authorities a useful tool for near real-time monitoring of E. coli in drinking water samples. Furthermore, this system can be applied independently or in conjunction with other methods as a part of an array of biochemical assays in order to reliably detect E. coli in water.