Towards the application of learning systems for decision support in construction engineering and management

Construction managers need to make decisions in an uncertain environment, where unexpected variables are present everyday and everywhere. To adequately support their decisions and decrease any negative impact and collateral effect, they use computational tools called decision support systems (DSS). DSS are widely used in the construction industry, presenting many advantages such as historical data available with fast processing of information, accuracy and effectiveness of output with a friendly interface.;However, a review of ninety-three DSS in the construction over the past 30 years showed that most of them are static, where the model has fixed its parameters, and member functions. Static models can quickly become obsolete; requiring manual adjustment to be relevant in a dynamic environment such as the construction engineering and management field. A better approach to solving the problem of changes of the decision environment within the construction industry is to develop dynamic models based on learning systems.;This research explores the application of learning capabilities in decision support systems in the construction industry by examining questions such as: what is the history and current state of DSS in construction engineering and management research?, what are the key components of a learning system for decision support?, and what are the characteristics of data in the construction engineering and management industry that must be addressed to create a general framework for applying learning systems? The outcome of this research is a general framework to apply the learning components into decision support systems for the construction engineering and management field.