A high monetary value of the construction projects is one of the reasons of frequent disputes between a general contractor (GC) and a client. A construction site is a unique, one-time, and single-product factory with many parties involved and dependent on each other. The organizational dependencies and their complexity make any fault or mistake propagate and influence the final result (delays, cost overruns). The constant will of the parties involved results in completing a construction object. The cost increase, over the expected level, may cause settlements between parties difficult and lead to disputes that often finish in a court. Such decision of taking a client to a court may influence the future relations with a client, the trademark of the GC, as well as, its finance. To ascertain the correctness of the decision of this kind, the machine learning tools as decision trees (DT) and artificial neural networks (ANN) are applied to predict the result of a dispute. The dataset of about 10 projects completed by an undisclosed contractor is analyzed. Based on that, a much bigger database is simulated for automated classifications onto the following two classes: a dispute won or lost. The accuracy of over 93% is achieved, and the reasoning based on results from DT and ANN is presented and analyzed. The novelty of the article is the usage of in-company data as the independent variables what makes the model tailored for a specific GC. Secondly, the calculation of the risk of wrong decisions based on machine learning tools predictions is introduced and discussed.

E3S Web of Conferences, open access proceedings in environment, energy and earth sciences

Interest has recently grown in the application of game theory (GT) to solve a number of diverse problems in the field of construction. The use of GT by a general contractor (GC) of construction works to indicate the best strategy leading to winning court proceedings in a situation of conflict with investor (IN), has not been investigated until now. Thus the aim of this paper is to indicate the optimal strategy from the GC viewpoint in a conflict situation with the IN. The article presents a list of the most common causes of conflict between parties of a construction work contract, defines the background of the problem and the cause of the dispute, and subsequently the authors generate a theoretical model of the game. Based on the analyzed game model, the expected payoffs for players were calculated and the probability boundary value determined in making the GC apply the indicated strategy. The study results show that while the probability of issuing a judgment favorable for the GC is at least equal to 0.69 it is justified to use an aggressive strategy. The analysis also confirms that from the financial perspective, litigation in most cases of conflicts in the area of construction should be the ultimate choice.

Urban regeneration as a complex project, generates many extremely specific threats affecting the increase of investment risk. Its unique nature causes that probability parameter, normally applied in the process of risk quantification, is extremely difficult to estimate. Due to lack of historical data urban regeneration related activities are therefore associated with uncertainty. According to the authors, a useful tool for resolving the above issues may prove to be Bayesian networks (BN). Beliefs based on expert knowledge should be considered as a subjective measure, nevertheless BN also allow to combine this information with objective results of conducted research. The authors built a model representing various urban regeneration risk areas, where the analysis covers degradation of the urban regeneration area. The article also presents selected parameters allowing for diagnostics of technical condition of buildings, road pavement and underground infrastructure in the area of urban regeneration.