Matching Items (6)
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- All Subjects: Construction Management
- Creators: Wiezel, Avi
- Creators: Ford, Emily Lucile
- Status: Published
Description
Owner organizations in the architecture, engineering, and construction (AEC) industry are presented with a wide variety of project delivery approaches. Implementation of these approaches, while enticing due to their potential to save money, reduce schedule delays, or improve quality, is extremely difficult to accomplish and requires a concerted change management effort. Research in the field of organizational behavior cautions that perhaps more than half of all organizational change efforts fail to accomplish their intended objectives. This study utilizes an action research approach to analyze change message delivery within owner organizations, model owner project team readiness and adoption of change, and identify the most frequently encountered types of resistance from lead project members. The analysis methodology included Spearman's rank order correlation, variable selection testing via three methods of hierarchical linear regression, relative weight analysis, and one-way ANOVA. Key findings from this study include recommendations for communicating the change message within owner organizations, empirical validation of critical predictors for change readiness and change adoption among project teams, and identification of the most frequently encountered resistive behaviors within change implementation in the AEC industry. A key contribution of this research is the recommendation of change management strategies for use by change practitioners.
ContributorsLines, Brian (Author) / Sullivan, Kenneth (Thesis advisor) / Wiezel, Avi (Committee member) / Badger, William (Committee member) / Arizona State University (Publisher)
Created2014
Description
The current paradigm to addressing the marginal increases in productivity and quality in the construction industry is to embrace new technologies and new programs designed to increase productivity. While both pursuits are justifiable and worthwhile they overlook a crucial element, the human element. If the individuals and teams operating the new technologies or executing the new programs lack all of the necessary skills the efforts are still doomed for, at best, mediocrity. But over the past two decades researchers and practitioners have been exploring and experimenting with a softer set of skills that are producing hard figures showing real improvements in performance.
ContributorsMischung, Joshua (Author) / Sullivan, Kenneth T. (Thesis advisor) / El Asmar, Mounir (Committee member) / Wiezel, Avi (Committee member) / Arizona State University (Publisher)
Created2014
Description
The construction industry faces important performance problems such as low productivity, poor quality of work, and work-related accidents and injuries. Creating a high reliability work system that is simultaneously highly productive and exceptionally safe has become a challenge for construction practitioners and scholars. The main goal of this dissertation was to create an understanding of high reliability construction work systems based on lessons from the production practices of high performance work crews. High performance work crews are defined as the work crews that constantly reach and maintain a high level of productivity and exceptional safety record while delivering high quality of work. This study was conceptualized on findings from High Reliability Organizations and with a primary focus on lean construction, human factors, safety, and error management. Toward the research objective, this dissertation answered two major questions. First, it explored the task factors and project attributes that shape and increase workers' task demands and consequently affect workers' safety, production, and quality performance. Second, it explored and investigated the production practices of construction field supervisors (foremen) to understand how successful supervisors regulate task and project demands to create a highly reliable work process. Employing case study methodology, this study explored and analyzed the work practices of six work crews and crew supervisors in different trades including concrete, masonry, and hot asphalt roofing construction. The case studies included one exceptional and one average performing crew from each trade. Four major factors were considered in the selection of exceptional crew supervisors: (1) safety performance, (2) production performance, (3) quality performance, and (4) the level of project difficulty they supervised. The data collection was carried out in three phases including: (1) interview with field supervisors to understand their production practices, (2) survey and interview with workers to understand their perception and to identify the major sources of task demands, and (3) several close field observations. Each trade's specific findings including task demands, project attributes, and production practices used by crew supervisors are presented in a separate chapter. At the end the production practices that converged to create high reliability work systems are summarized and presented in nine major categories.
ContributorsMemarian, Babak (Author) / Bashford, Howard (Thesis advisor) / Boren, Rebecca (Committee member) / Wiezel, Avi (Committee member) / Arizona State University (Publisher)
Created2012
Description
The discipline of continuing professional development (CPD) is well defined and established within a variety of industries, such as medical, legal, and financial. The built environment is a less defined and mature industry with respect to educational pathways and professional education, with no uniform structure. Occupational licensing, such as registered nurses, certified professional accountants, and others are well known within both their industries and the public. Additionally, occupational core-competencies are well established. Planning is a core skill set within the built environment and construction management. Definitions of the term “planning” vary quite broadly across the built environment, but generally includes activities such as risk identification, scope identification, and scheduling. Understanding how professionals in the built environment learn to plan is critical to meeting CPD needs for planning skills and the ability of a professional to “plan” effectively. Many planning tools and software have been developed, but often rely on an individual professional’s personal experiences and abilities. Limited literature in the field of professional education in the built environment has left a gap on the topic of how to train professionals in planning competencies. Survey results indicate that current training is not meeting the expectations of professionals, as only 16 percent of professionals are trained how to plan using their preferred method of learning. While on-the-job training is the primary format, the most preferred format is internal company training, but only 54 percent of companies provide this format. Mann-Whitney U and Kruskal Wallis tests were conducted and revealed that organizations with internal training programs have higher employee satisfaction with their organization’s planning process. Further, organizations with internal training programs are seen as having a more formal internal planning process. Research is needed to develop CPD within construction management and provide the foundation upon which a professional education structure can be created. An andragogically-centered schema for a heuristic approach to construction CPD is developed and tested on a seminar for pre-project planning. The full instructional design of the seminar using the model is disclosed and seminar results showed positive results and participants achieved high levels of learning.
ContributorsHurtado, Kristen (Author) / Sullivan, Kenneth (Thesis advisor) / Wiezel, Avi (Committee member) / Badger, William (Committee member) / Arizona State University (Publisher)
Created2018
Description
Productivity in the construction industry is an essential measure of production efficiency and economic progress, quantified by craft laborers' time spent directly adding value to a project. In order to better understand craft labor productivity as an aspect of lean construction, an activity analysis was conducted at the Arizona State University Palo Verde Main engineering dormitory construction site in December of 2016. The objective of this analysis on craft labor productivity in construction projects was to gather data regarding the efficiency of craft labor workers, make conclusions about the effects of time of day and other site-specific factors on labor productivity, as well as suggest improvements to implement in the construction process. Analysis suggests that supporting tasks, such as traveling or materials handling, constitute the majority of craft labors' efforts on the job site with the highest percentages occurring at the beginning and end of the work day. Direct work and delays were approximately equal at about 20% each hour with the highest peak occurring at lunchtime between 10:00 am and 11:00 am. The top suggestion to improve construction productivity would be to perform an extensive site utilization analysis due to the confined nature of this job site. Despite the limitations of an activity analysis to provide a complete prospective of all the factors that can affect craft labor productivity as well as the small number of days of data acquisition, this analysis provides a basic overview of the productivity at the Palo Verde Main construction site. Through this research, construction managers can more effectively generate site plans and schedules to increase labor productivity.
ContributorsFord, Emily Lucile (Author) / Grau, David (Thesis director) / Chong, Oswald (Committee member) / Civil, Environmental and Sustainable Engineering Programs (Contributor) / School of International Letters and Cultures (Contributor) / Barrett, The Honors College (Contributor)
Created2016-12
Description
Ultra High Performance (UHP) cementitious binders are a class of cement-based materials with high strength and ductility, designed for use in precast bridge connections, bridge superstructures, high load-bearing structural members like columns, and in structural repair and strengthening. This dissertation aims to elucidate the chemo-mechanical relationships in complex UHP binders to facilitate better microstructure-based design of these materials and develop machine learning (ML) models to predict their scale-relevant properties from microstructural information.To establish the connection between micromechanical properties and constitutive materials, nanoindentation and scanning electron microscopy experiments are performed on several cementitious pastes. Following Bayesian statistical clustering, mixed reaction products with scattered nanomechanical properties are observed, attributable to the low degree of reaction of the constituent particles, enhanced particle packing, and very low water-to-binder ratio of UHP binders. Relating the phase chemistry to the micromechanical properties, the chemical intensity ratios of Ca/Si and Al/Si are found to be important parameters influencing the incorporation of Al into the C-S-H gel.
ML algorithms for classification of cementitious phases are found to require only the intensities of Ca, Si, and Al as inputs to generate accurate predictions for more homogeneous cement pastes. When applied to more complex UHP systems, the overlapping chemical intensities in the three dominant phases – Ultra High Stiffness (UHS), unreacted cementitious replacements, and clinker – led to ML models misidentifying these three phases. Similarly, a reduced amount of data available on the hard and stiff UHS phases prevents accurate ML regression predictions of the microstructural phase stiffness using only chemical information. The use of generic virtual two-phase microstructures coupled with finite element analysis is also adopted to train MLs to predict composite mechanical properties. This approach applied to three different representations of composite materials produces accurate predictions, thus providing an avenue for image-based microstructural characterization of multi-phase composites such UHP binders. This thesis provides insights into the microstructure of the complex, heterogeneous UHP binders and the utilization of big-data methods such as ML to predict their properties. These results are expected to provide means for rational, first-principles design of UHP mixtures.
ML algorithms for classification of cementitious phases are found to require only the intensities of Ca, Si, and Al as inputs to generate accurate predictions for more homogeneous cement pastes. When applied to more complex UHP systems, the overlapping chemical intensities in the three dominant phases – Ultra High Stiffness (UHS), unreacted cementitious replacements, and clinker – led to ML models misidentifying these three phases. Similarly, a reduced amount of data available on the hard and stiff UHS phases prevents accurate ML regression predictions of the microstructural phase stiffness using only chemical information. The use of generic virtual two-phase microstructures coupled with finite element analysis is also adopted to train MLs to predict composite mechanical properties. This approach applied to three different representations of composite materials produces accurate predictions, thus providing an avenue for image-based microstructural characterization of multi-phase composites such UHP binders. This thesis provides insights into the microstructure of the complex, heterogeneous UHP binders and the utilization of big-data methods such as ML to predict their properties. These results are expected to provide means for rational, first-principles design of UHP mixtures.
ContributorsFord, Emily Lucile (Author) / Neithalath, Narayanan (Thesis advisor) / Rajan, Subramaniam D. (Committee member) / Mobasher, Barzin (Committee member) / Chawla, Nikhilesh (Committee member) / Hoover, Christian G. (Committee member) / Maneparambil, Kailas (Committee member) / Arizona State University (Publisher)
Created2020