Abstracts – Browse Results
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Cunningham Emord, P (2009) Building evaluation capacity with appreciative inquiry: An exploratory case study, Unpublished PhD Thesis, , The University of New Mexico.
Garrido Martins, C (2019) Assessment of project risks in fast-track construction projects, Unpublished PhD Thesis, , University of New Mexico.
Gatti, U C (2012) Measuring and evaluating physical strain to improve construction workforce productivity, Unpublished PhD Thesis, , University of New Mexico.
Han, F (2021) Measurement of resilience performance for infrastructure construction project delivery, Unpublished PhD Thesis, , The University of New Mexico.
Jafari, A (2018) A decision-making framework for the selection of sustainable alternatives for energy-retrofits, Unpublished PhD Thesis, , University of New Mexico.
Jaramillo, L V (2019) Implementations of resilience engineering for natural system disturbances: A panarchical perspective, Unpublished PhD Thesis, , University of New Mexico.
Martens, R (2018) An analysis of the relationship between leadership style and lean expressed through respect, proactivity, and innovative work behavior, Unpublished PhD Thesis, , University of New Mexico.
Nauman, R A (1999) The United States air force academy: A case study of rhetoric and reality in the making of modernism, Unpublished PhD Thesis, , University of New Mexico.
Phillips, H C (2009) A proposed method to determine confidence in a construction schedule, Unpublished PhD Thesis, , University of New Mexico.
Santamaria Carrera, J L (2017) Quantifying the effect of construction site factors on concrete quality, costs and production rates, Unpublished PhD Thesis, , University of New Mexico.
- Type: Thesis
- Keywords: optimization; skills; construction site; construction phase; production process; experiment
- ISBN/ISSN:
- URL: https://www.proquest.com/docview/2026176071
- Abstract:
Factors affecting concrete can be categorized as structured factors or unstructured factors. The first group of factors consists of those related to the production process of concrete including water-cement ratio, properties of raw materials and mix proportions. Unstructured factors or construction site factors are related to labor skills and local conditions during the construction process of a project. Concrete compressive strength as a quality metric, costs and production rates may be affected significantly by such factors while performing concrete operations at the jobsite. Several prior studies have investigated the effect of structured factors on concrete. However literature is limited regarding the effects of unstructured factors during the construction phase of a facility. This study proposes a systematic methodology to identify and quantify the effects of construction site factors including crew experience, compaction method, mixing time, curing humidity and curing temperature on concrete quality, costs and production rates using fuzzy inference systems. First, the perceived importance of construction-related factors is identified and evaluated through literature review and a survey deployed to construction experts. Then, the theory of design of experiments (DOE) is used to conduct a full 25 factorial experiment consisting of 32 runs and 192 compressive strength tests to identify statistically significant unstructured factors. Fuzzy inference systems (FISs) are proposed for predicting concrete compressive strength, costs and production rate effects through the use of adapted network-based fuzzy inference system (ANFIS). Finally, an optimization model is formulated and tested for managing concrete during the construction process of a facility. Literature review and survey results showed that curing humidity, crew experience, and compaction method are the top three factors perceived by construction experts to affect concrete compressive strength, whereas crew experience, mixing time and compaction method are the top three factors affecting concrete costs and production rates. Additionally, crew experience, compaction and mixing time were found to dominate global ranking of perceived affecting factors through the application of the relative importance index (RII). When conducting designed experiments and analysis of variance (ANOVA), compaction method, mixing time, curing humidity and curing temperature were identified to be statistically significant construction site factors for concrete compressive strength whereas crew experience, compaction method and mixing time were statistically significant factors for cost and production rates. A Sugeno type fuzzy inference system (FIS) for quantifying compressive strength, cost and production rate effects was created by using ANFIS, having correlation coefficients (R-squared values) greater than 93%, indicating that resulting models predict new observations well. Curing temperature (i.e. , on-site curing temperature) was identified to be the most affecting condition for concrete compressive strength while mixing time had the biggest impact on concrete cost and production rates. The developed FISs can be used as a decision–support tool that allows for determining desired operating conditions, that ensures specified compressive strength, saves resources and maximizes profits when fabricating, placing and curing concrete.
Severn, B W (1980) A simplified methodology for evaluating rural road proposals for less developed countries, Unpublished PhD Thesis, , University of New Mexico.
Zhang, S (2017) Pavement surface distress detection, assessment, and modeling using geospatial techniques, Unpublished PhD Thesis, , University of New Mexico.