A platform for research: civil engineering, architecture and urbanism
A platform for research: civil engineering, architecture and urbanism
Factors Affecting Litigation Outcomes of Differing Site Conditions (DSC) Disputes: A Logistic Regression Models (LRM)
Construction is one of the major contributing industries to the US economy. However, its advancement and contribution have always been negatively impacted by the vast number of conflicts associated with it. Traditional conflict resolution methods require legal knowledge and expertise that are not commonly available, thus cost considerable sums of money. A significant number of construction disputes could be attributed to the uncertainty in the conditions under which projects are executed, and especially site conditions. In an attempt to provide an outcome prediction system for differing site conditions (DSC) claims in the construction industry, this paper provides, as a first step, a statistical analysis of set of precedent cases to identify, quantify, and measure the impact of significant legal factors on outcomes prediction of litigation cases. The adopted methodology developed a statistical binary choice Logistic Regression Model (LRM) (a) to identify the effect of each legal factor on the prediction of the winning party; (b) to identify the best combination of factors with the highest prediction precision; and (c) to perform a sensitivity analysis to prioritize the most significant legal factors. Among the major findings of this paper are (1) 23 significant legal factors were identified; (2) A combination of 9 legal factors were found to attain the highest prediction precision of 93.33%; (3) Generally, cases in which the Federal Government is a concerned party, judgments are in its favor.
Factors Affecting Litigation Outcomes of Differing Site Conditions (DSC) Disputes: A Logistic Regression Models (LRM)
Construction is one of the major contributing industries to the US economy. However, its advancement and contribution have always been negatively impacted by the vast number of conflicts associated with it. Traditional conflict resolution methods require legal knowledge and expertise that are not commonly available, thus cost considerable sums of money. A significant number of construction disputes could be attributed to the uncertainty in the conditions under which projects are executed, and especially site conditions. In an attempt to provide an outcome prediction system for differing site conditions (DSC) claims in the construction industry, this paper provides, as a first step, a statistical analysis of set of precedent cases to identify, quantify, and measure the impact of significant legal factors on outcomes prediction of litigation cases. The adopted methodology developed a statistical binary choice Logistic Regression Model (LRM) (a) to identify the effect of each legal factor on the prediction of the winning party; (b) to identify the best combination of factors with the highest prediction precision; and (c) to perform a sensitivity analysis to prioritize the most significant legal factors. Among the major findings of this paper are (1) 23 significant legal factors were identified; (2) A combination of 9 legal factors were found to attain the highest prediction precision of 93.33%; (3) Generally, cases in which the Federal Government is a concerned party, judgments are in its favor.
Factors Affecting Litigation Outcomes of Differing Site Conditions (DSC) Disputes: A Logistic Regression Models (LRM)
Mahfouz, Tarek (author) / Kandil, Amr (author)
Construction Research Congress 2009 ; 2009 ; Seattle, Washington, United States
Building a Sustainable Future ; 239-248
2009-04-01
Conference paper
Electronic Resource
English
| British Library Conference Proceedings | 2009
Litigation Outcome Prediction of Differing Site Condition Disputes through Machine Learning Models
| British Library Online Contents | 2012
Litigation Outcome Prediction of Differing Site Condition Disputes through Machine Learning Models
| Online Contents | 2012