The increasing complexity and magnitude of projects impose greater impact of delays on stakeholders. Construction delays are a major source of disputes in construction projects. Since a construction project depends on interactions and shared responsibilities among parties, research works were directed toward identifying delay causes, quantifying their impacts, and proposing ways to deal with them. Several delay analysis techniques (DATs) are available, but when applied to the same project’s delays provide different results. Thus, the selection of the DAT to use in evaluating delays becomes vital. Reviewing the literature, it has been realized that often there are disagreements, which lead to escalating a claim into a dispute on which DAT to be used. A dispute results in additional costs, time, and, in some cases, negatively impacts the relation between the parties. Some research was conducted to gather experts’ opinions on the best technique to be used; however, little research was done to quantify the reasons behind the selection and transform it into a numerical model. This research is an attempt to support different parties in selecting the most appropriate DAT for a claim by building an artificial neural network (ANN) model that utilizes data collected through experts’ judgements on various factors that influence the selection of DATs. To gain as much understanding on the topic, data were collected through several interviews and two surveys which were used to build the model. Results of these surveys were compared to other surveys conducted in several countries to come up with the final list of factors affecting the selection of DAT decision. In addition, this research provides an analysis of how different factors are perceived through different law systems.

A simple additive weighing model that quantifies experts’ opinions to score different DATs was established and used to generate dataset to train the ANN model. After the ANN model was trained, both models were tested by comparing their results to those of actual case studies. Results show that the ANN model can be a useful tool for DAT selection, as it provides acceptable level of support to users in choosing the best DAT to be applied in analyzing their claims. The ANN model is developed using MS Excel and Palisade software NeuralTools which is an add-in to Microsoft Excel and has data mining capabilities. Because of its wide array of functions and availability, MS visual basic programming language was used to create the user interface for the model and to generate the data set required for the ANN model.


School of Sciences and Engineering


Construction Engineering Department

Degree Name

MS in Construction Engineering

Graduation Date

Winter 1-31-2023

Submission Date


First Advisor

Ossama Hosny

Committee Member 1

Khaled Nassar

Committee Member 2

Emad Elbeltagi

Committee Member 3

Ibrahim Abotaleb


78 P.

Document Type

Master's Thesis

Institutional Review Board (IRB) Approval

Approval has been obtained for this item