There has been an increase in adoption of building information modeling (BIM) by commercial contractors over the past few years but there is not a clear indication of current application or the frequency of each use. This research was undertaken to determine the frequency and best practices of using BIM in commercial construction. Leading commercial contracting firms were contacted and employees were selected to complete a telephone survey that was designed to determine how they are using BIM. It was found that BIM is being used with more frequency on projects, with plans to continue to implement it even further on all projects that allow for its use. Most companies are incorporating trade contractors in their coordination meetings but are taking the lead to ensure a high standard is maintained. Trade contractors are given responsibility to resolve smaller clashes while bringing the larger problems to the clash detection meetings. The model quality being received is increasing as well the frequency of models being received from the design team; however, there is a lack of consensus as to the level of development requirement to ensure the model can be used for facilities management. Commercial contractors are adopting building information modeling for a variety of reasons. These include client requirements, innovative technology strategies, and fear of being left behind. Overall its use improves processes within their companies. The general agreement is that BIM is having a positive impact on profitability, schedules and sales. BIM further improves the efficiency, effectiveness and competitiveness. The best practices undertaken by commercial contractors include using BIM as a sales and marketing tool, utilizing subcontractors knowledge and allowing them to resolve minor clashes, having combination of BIM teams and BIM experts and investing in training employees and further development of BIM uses.

This thesis researched the accuracy of measured energy data in comparison to estimated hand calculation data and estimated building energy performance simulation data. In the facility management industry, there is minimal evidence that building energy performance software is being used as a benchmark against measured energy usage within a building. Research was conducted to find examples of measured energy data compared to simulated data. The study examined the accuracy of a simulation software and hand calculations to measured energy data. Data suggests that comparisons may be made between building energy performance simulated data and measured data, though comparisons are solely based on each individual case. Data suggests that heating load simulation data is more accurate for benchmarks than cooling load simulation data. Importing models into Autodesk Green Building Studio (GBS) was not as successful as was expected. When only four of the initial ten building models chosen imported successfully, the remaining twenty-five other building models were imported. Only two of the twenty-five models successfully imported into GBS. The sample size of this research changed from ten to six. The results of this study show that GBS simulated data was close to actual data for the heating loads. For the cooling loads, however, GBS simulated data was consistently low in comparison to the actual data. The results of this study show that hand calculations were consistently low and not as close as GBS simulated data when compared to the actual data for the heating loads. The opposite was true with the cooling loads as hand calculations were consistently high in comparison to actual data.

The location of distribution centers is an important strategic decision in supply chain design, particularly as it relates to service quality, productivity, and profitability of the firm. There has been extensive research performed on distribution location models which require the use of complex algorithms and assumptions that make use of these models difficult in practice for small and medium enterprises (SMEs) that have limited capital and resources. Studies have also failed to capture and quantify potential business results of using more sophisticated methods. In this study, a deterministic and static location-allocation model is designed using a prototype software tool. The tool is a collection of Excel/VBA programs formulated as a mixed integer programming (MIP) model. Research was done in conjunction with a personal care products company that provided a unique opportunity to evaluate the manual methods typically used in SMEs with the results of the software tool and the potential business impact. Both quantitative data, including customer locations and order information, as well as qualitative data were collected from the company. A total of five models were simulated using the prototype software tool, including one model of the current supply chain for use as a base comparison, and four future-state models of potential distribution center (DC) location scenarios. The objective in each of these models was to minimize transportation costs while maintaining the desired service fulfillment levels. The use of the prototype software tool resulted in a more optimal supply chain solution. The optimized DC location resulted in a network design with a 6.5% reduction in transportation costs from the base model, and a 0.8% reduction in transportation costs from a location previously chosen by the company. The results also provided insight into considering weighted shipping volume in location analysis as it can serve as a magnifier of business impact and rapid diminishing returns when shipping product below an average of 10 pounds. The use of an optimization tool was shown to mitigate many issues SMEs encounter in attempting to synthesize multiple variables in the DC location problem.

Project teams that deliver high risk, complex projects in the civil construction industry need tools to enable successful delivery. Construction Manager/General Contractor (CM/GC) is an innovative alternate delivery method, providing one such a tool. CM/GC furnishes public agencies with an attractive option for delivering projects in a less adversarial and more constructive manner by involving the contractor during design. The sophisticated public owner does not have to relinquish control of the details of the design in order to accelerate the schedule or see the benefits of real-time cost estimating data. There are also significant cost and schedule benefits with not degradation in quality. However, because CM/GC is relatively unknown to the civil construction industry much remains to be investigated about how CM/GC processes effect successful project delivery. This research investigated how CM/GC processes affect the three critical elements of construction process risks (including quality, schedule, cost and collaboration), project specific risks, and innovation. By identifying the processes that benefit these elements, successes can be repeated and increased. Additionally, an understanding of the differences in the perception of CM/GC processes, given by contractors, owners, and design engineers, provided perspective into improving the process. Analyzing data on current CM/GC projects and programs, as well as the compiled experience of field-experienced project teams, provided the information the industry needs to pursue implementation. Identified advantages of the process can be tied to strategies for successful delivery. Identified disadvantages expose barriers to implementation to be overcome by the project team. Project teams state that while the process does have disadvantages, many are perceptional and not fatal flaws to the method. Findings of this research link CM/GC processes to robust risk management results and the opportunity for successful innovation.

Traditional wireless site surveys produce a heat-map of link strength or quality over a target area, usually on the ground plane. In recent years research has gone into using aerial drones in network attack and surveillance, making three dimensional awareness of wireless coverage areas of interest. A multi-rotor drone and data collection module were built and tested as part of this research. Site assessments were conducted both in open space and near structures. Collected data was interpolated across the target area, and visualized as points and contours. These visualizations were exported to a Keyhole Markup Language (KML) for visualization in context. Resulting visualizations proved to be beneficial in identifying the coverage area of both authorized and rogue access points.

Technical disciplines are evolving rapidly leading to changes in their associated vocabularies. Confusion in interdisciplinary communication occurs due to this evolving terminology. Two causes of confusion are multiple definitions (overloaded terms) and synonymous terms. The formal names for these two problems are polysemy and synonymy. Termediator-I, a web application built on top of a collection of glossaries, uses definition count as a measure of term confusion. This tool was an attempt to identify confusing cross-disciplinary terms. As more glossaries were added to the collection, this measure became ineffective. This thesis provides a measure of term polysemy. Term polysemy is effectively measured by semantically clustering the text concepts, or definitions, of each term and counting the number of resulting clusters. Hierarchical clustering uses a measure of proximity between the text concepts. Three such measures are evaluated: cosine similarity, latent semantic indexing, and latent Dirichlet allocation. Two linkage types, for determining cluster proximity during the hierarchical clustering process, are also evaluated: complete linkage and average linkage. Crowdsourcing through a web application was unsuccessfully attempted to obtain a viable clustering threshold by public consensus. An alternate metric of polysemy, convergence value, is identified and tested as a viable clustering threshold. Six resulting lists of terms ranked by cluster count based on convergence values are generated, one for each similarity measure and linkage type combination. Each combination produces a competitive list, and no clear combination can be determined as superior. Semantic clustering successfully identifies polysemous terms, but each similarity measure and linkage type combination provides slightly different results.

Building Information Modeling (BIM) use in the contracting industry has grown significantly in recent years. With this change in the construction industry, consensus has not been reached as to what BIM is, who is using it and what they are using it for. The purpose of this research was to determine current BIM practices of US-based commercial MEP contractors. Executive, middle management, and field personnel were interviewed to determine the current BIM practices in their companies. The majority of companies interviewed were using BIM and most were using it on a significant portion of their projects. The majority of MEP contractors using BIM were seeing positive results in many of six key performance indicators, profitability, schedule duration, field efficiency, change orders, rework, and safety. The top uses of BIM for MEP contractors were clash detection coordination, prefabrication, design creation, and quantity take-off/cost estimating. Most MEP contractors have not yet incorporated BIM for scheduling, sequencing, or safety analysis. Additionally most MEP contractors did not have a formal BIM training program in their company.

Organizational learning seeks to proactively expand the knowledge base of an organizational entity by incorporating knowledge, skills, and experience into the collective conscience of the company through the development of routines rather than relying on individual employees for these traits. Valuable experience gained by individuals who weather economic downturn with their companies must be absorbed into the corporate structure in a meaningful way so that it may be accessible for use during future recessions. This research asks what was learned by individuals during the most recent recession, what mechanisms were employed to retain and utilize this knowledge, and what cultures, strategies, or processes were created because of it. Companies were also compared to understand whether an increase in organizational learning within a company would translate into increased performance.

Water jetting as a means for removing anomalous materials from cast-in-drilled-hole (CIDH) piles was examined. The primary objective of this research was to establish empirical relationships between different jetting parameters and the removal of commonly occurring anomalous zone materials, including low-strength concrete, slurry mixed concrete, grout, and clay soil. Also investigated was the current standard-of-practice used by water jetting contractors within California. The testing specimens consisted of typical anomalous material with unconfined compressive strengths between 5 and 6,000 psi. The experimental work consisted of water blasting submerged specimens using rotary jets, nozzles, and pumping equipment typically used in construction practice. Two testing protocols were developed. The first testing protocol called for the nozzle to be held stationary and the second allowed the nozzle to be cycled up and down across the anomaly. During testing, material removal rates were measured as a function of jet pressure and standoff distance. Water blasted specimens were cut apart after testing to confirm erosion measurements and to permit inspection of the water blasted surfaces. Based on the results, erosion rates and the effectiveness of water jetting are primarily influenced by unconfined compressive strength, when using standard test equipment and jetting pressures. Further, aggregate size and material type in the anomalous material does not appear to influence both total erosion and erosion rate.

The construction industry is one of the most unsafe industries for workers in the United States. Advancements in wearable technology have been proven to create a safer construction environment. Despite the availability of these devices, use within the construction industry remains low. The objective of this research is to identify and analyze the causes behind the reluctance of the construction industry to implement two specific wearable safety devices, a biometric sensor, and a location tracking system. Device acceptance was analyzed from the perspective of the user (construction field labor) and company decision makers (construction managers). A modified unified theory of acceptance and use of technology (UTAUT) model was developed specific to barriers commonly found within technology adoption in the construction industry including: perceived performance expectancy, perceived effort expectancy, openness to data utilization, social influence, data security, and facilitating conditions. A structured questionnaire was designed to test for association between the mentioned constructs and either behavioral intention or actual use. The questionnaire went through an expert review process, and a pilot study was conducted prior to being distributed to industry. Once all data was received Pearson chi-squared analysis was used to test for association between the constructs. A minority (46%) of labor respondents would not agree to voluntarily use the biometric wearable sensing device. Constructs associated with this finding included perceived performance expectancy, perceived effort expectancy, and social influence. A majority (59%) of labor respondents would not agree to voluntarily use the location tracking wearable sensing device. Constructs associated with this finding included perceived performance expectancy, social influence, and data security. A majority (56%) of management respondents would not implement the biometric wearable sensing device. Constructs found to be associated with this finding included perceived performance expectancy, openness to data utilization, and social influence of the client. A supermajority (68%) of management respondents would not implement the location tracking wearable sensing device. Constructs found to be associated with this finding include perceived performance expectancy, perceived effort expectancy, openness to data utilization, social influence, and data security. This study will aid in the successful implementation of wearable sensing devices within the construction industry. Findings from this study can be used to aid those hoping to implement wearable sensing devices by identifying causes of wearable sensing device rejection. The results of this study can be used by both project managers and health and safety professionals to aid in device acceptance by field labor, and by those whose goal is to increase device use among construction firms.