Abstracts – Browse Results
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Abdelaty, A F A (2017) Data-driven algorithms for enhanced transportation infrastructure asset management, Unpublished PhD Thesis, , Iowa State University.
Al Shalabi, F A (2016) BIM framework for energy and maintenance performance assessment for facility management, Unpublished PhD Thesis, , Iowa State University.
AlBughdadi, A J M (2022) Advanced decision support tools for bridge asset management, Unpublished PhD Thesis, , Iowa State University.
Azmy, N (2012) The role of team effectiveness in construction project teams and project performance, Unpublished PhD Thesis, Civil, Construction, and Environmental Engineering, Iowa State University.
Barbosa Resende, C (2021) Post-construction evaluation for work zones and construction engineers' training, Unpublished PhD Thesis, Civil, Construction, and Environmental Engineering, Iowa State University.
Barutha, P J (2018) Integrated project delivery for industrial projects, Unpublished PhD Thesis, , Iowa State University.
Canales Fernandez, A R (2004) Developing effective integration between American supervisors and hispanic craft workers in construction, Unpublished PhD Thesis, , Iowa State University.
Christensen, A N (2019) Identifying private construction project owner inefficiencies that affect project goals, Unpublished PhD Thesis, , Iowa State University.
El-adaway, I H (2008) Construction dispute mitigation through multi-agent based simulation and risk management modeling, Unpublished PhD Thesis, , Iowa State University.
Elmisalami, T E (2001) Developing a multi-attribute utility model (MAUM) for selecting information technologies in the construction industry, Unpublished PhD Thesis, , Iowa State University.
Enz, D L (2009) Construction project administration and management for mitigating work zone crashes and fatalities: An integrated risk management model, Unpublished PhD Thesis, , Iowa State University.
Gad, G M (2012) Effect of culture, risk, and trust on the selection of dispute resolution methods in international construction contracts, Unpublished PhD Thesis, , Iowa State University.
Gao, Z (2004) Investigation of technologies to improve the visualization of design documents and construction process, Unpublished PhD Thesis, Civil, Construction, and Environmental Engineering, Iowa State University.
Harmelink, D J (1995) Linear scheduling model: The development of a linear scheduling model with microcomputer applications for highway construction project control, Unpublished PhD Thesis, , Iowa State University.
Kaewmoracharoen, M (2009) Feasibility of visualization and simulation applications to improve work zone safety and mobility, Unpublished PhD Thesis, Civil, Construction, and Environmental Engineering, Iowa State University.
Kawal, D E (1970) A systems model of a construction firm, Unpublished PhD Thesis, , Iowa State University.
Lueth, P L O (2008) The architectural design studio as a learning environment: A qualitative exploration of architecture design student learning experiences in design studios from first- through fourth-year, Unpublished PhD Thesis, , Iowa State University.
Mahfouz, T S (2009) Construction legal support for differing site conditions (DSC) through statistical modeling and machine learning (ML), Unpublished PhD Thesis, , Iowa State University.
- Type: Thesis
- Keywords: construction law; data mining; differing site conditions; litigation prediction; machine learning; statistical modeling; USA
- ISBN/ISSN:
- URL: https://www.proquest.com/docview/304905140
- Abstract:
The objective of this dissertation is to provide a coherent and integrated methodology for construction legal decision support for Differing Site Conditions (DSC) disputes through statistical modeling and machine learning. To attain this goal, the current study designed and implemented a 4 step methodology targeting the following goals: (1) to extract a comprehensive set of legal factors that govern DSC litigation outcomes in the construction industry; (2) to devise a litigation prediction model for DSC disputes in the construction industry based on the extracted set of legal factors; (3) to create a methodology for automated extraction of significant legal factors that governs DSC litigation outcomes from case documents; and (4) to develop an automated retrieval model for identifying DSC precedent cases from a large corpus based on similarity to newly introduced ones. The 4 steps of this methodology were implemented incrementally, and each step relied on the outcome of its predecessor. First, a comprehensive set of significant legal factors that govern DSC litigation cases verdicts were extracted through statistical modeling. Binary Probit and Logit Choice Models were developed (a) to identify the effect of each extracted factor on the prediction of the winning party; (b) to identify the best combination of factors with the highest significance on the prediction model; and (c) to perform a sensitivity analysis to prioritize the most significant legal factors. Among the main findings of this step are (1) in general, cases in which the Federal Government is a party of the dispute, judgments are in favor of the government (owner) over contractor; (2) "the presence of evident facts that the encountered conditions caused a change in the nature and cost of the contract" had the highest impact among variables causing a decrease in the prediction of judgment in favor of the owner, and causing an increase of 17. 77% in prediction on favor of the contractor; (3) "the presence of evident facts that the specifications included a warning against the presence of DSC from those conveyed in the contract documents" caused the highest increase in the prediction of judgment in favor of the owner amounting to an increase of 56. 56%; and (4) the development of Binary Probit and Logit Choice Models extracted a joint set of 13 statistically significant legal factors related to DSC disputes in the construction industry. This set provided the grounds for the other three steps of the current research methodology. Second, an automated litigation prediction model for DSC disputes in the construction industry through machine learning was developed based on the identified factors in the first step. The framework under this step incorporates analysis of different machine learning methodologies including support vector machines (SVM), Naïve Bayes (NB), and rule induction classifiers like Decision Trees (DT), Boosted Decision Trees (AD Tree), and PART. Ten machine learning models were developed using these machine learning methodologies to evaluate the best methodology for predicting litigation outcomes. The analysis of all developed models showed that the SVM Kernel Polynomial 3rd degree model has the best performance. This model attained an overall prediction accuracy of 98%. Third, an automated significant legal factors extraction model for DSC disputes in the construction industry through machine learning was developed. The framework under this step (1) developed 24 machine learning models in which 4 weighting schemes namely Term Frequency (tf), Logarithmic Term Frequency (ltf), Augmented Term Frequency (atf), and Term Frequency Inverse Document Frequency (tf. idf) were implemented for each type of classifier; and (2) developed two C++ algorithms for the preparation of the corpus and implementation of the required weighting mechanisms. The highest prediction rate of 84% was attained by NB classifier while implementing tf. idf weighting. The model was further validated by testing newly un-encountered cases, and a prediction precision f 81. 8% was attained. Finally, the fourth step of the methodology developed an automated machine learning model for the retrieval of supporting DSC precedent cases from large corpi. This step, therefore, (1) implemented Latent Semantic Analysis algorithm; and (2) developed 9 reduced feature spaces with feature sizes of 5, 10, 15, 20, 100, 200, 300, 400, and 500 for analysis and validation of the implemented algorithm. Among the findings of this step are (1) low dimension reduced feature spaces are more representative of documents closely related to the domain problem; (2) high dimension reduced feature spaces, are more representative to domain problems modeling dispersed and unrelated document collections; and (3) LSA reduced feature space of 10 features is the best reduced feature space to adopt for automating the extraction of similar DSC cases from a large corpus. (Abstract shortened by UMI. )
McCann, M (1986) An application of convex programming to construction scheduling, Unpublished PhD Thesis, Industrial and Manufacturing Systems Engineering, Iowa State University.
Mohd Zainudin, N (2019) Identification and analysis of worker safety hazards in Midwest agribusiness construction work sites, Unpublished PhD Thesis, Agricultural and Biosystems Engineering, Iowa State University.
Nahvi, A (2019) Integrated stochastic economics performance evaluation of horizontal infrastructure systems, Unpublished PhD Thesis, , Iowa State University.
Pinto Nunez, M D C (2017) Comprehensive partnering management model for highway construction projects delivered using traditional and alternative methods, Unpublished PhD Thesis, , Iowa State University.
Rahbar, F (1997) Computer-aided parametric planning (CAPP), Unpublished PhD Thesis, , Iowa State University.
Rueda-Benavides, J A (2016) Indefinite delivery/indefinite quantity project selection framework using stochastic techniques, Unpublished PhD Thesis, , Iowa State University.
Shrestha, K P (2016) Data analytics and visualization for enhanced highway construction cost indexes and as-built schedules, Unpublished PhD Thesis, , Iowa State University.
Sirotiak, T L (2008) The effect of problem/project-based learning on a desired skill set for construction professionals, Unpublished PhD Thesis, , Iowa State University.
Smadi, O G (2000) Knowledge-based expert system pavement management optimization, Unpublished PhD Thesis, , Iowa State University.
Talbot, J (2021) Understanding housing reconstruction in Puerto Rico after hurricanes Irma and Maria, Unpublished PhD Thesis, , Iowa State University.
Tapia, R M (2017) Linear scheduling and procurement tools to manage geotechnical risk in design-build construction projects, Unpublished PhD Thesis, Civil, Construction, and Environmental Engineering, Iowa State University.
Woldesenbet, A (2014) Highway infrastructure data and information integration & assessment framework: a data-driven decision-making approach, Unpublished PhD Thesis, Civil, Construction, and Environmental Engineering, Iowa State University.