The problems related to construction production are multi-faceted and complex. This has promoted the search for different methods/approaches for analizing the data which supports the decision-making process in the construction industry. In the article the authors focus their attention on well-known methods and tools, and on some new approaches to solving decision-making problems. The aim of the article is to analyze the methods used to analyse data in a construction company, convey their advantages and disadvantages, and specify the degree of efficiency in the discussed area.

In order to explore creativity in design, a computational model based on Case-Based Reasoning (CBR) (an approach to employing old experiences to solve new problems) and other soft computing techniques from machine learning, is proposed in this paper. The new model is able to address the four challenging issues: generation of a design prototype from incomplete requirements, judgment and improvement of system performance given a sparse initial case base library, extraction of critical features from a given feature space, adaptation of retrieved previous solutions to similar problems for deriving a solution to a given design task. The core principle within this model is that different knowledge from various level cases can be explicitly explored and integrated into a practical design process. In order to demonstrate the practical significance of our presented computational model, a case-based design system for EM devices, which is capable of deriving a new design prototype from a real-world device case base with high dimensionality, has been developed.

The ability of case-based reasoning systems to solve new problems mainly depends on their case adaptation knowledge and adaptation strategies. In order to carry out a successful case adaptation in our case-based reasoning system for a low frequency electromagnetic device design, we make use of semantic networks to organize related domain knowledge, and then construct a rule-based inference system which is based on the network. Furthermore, based on the inference system, a novel adaptation algorithm is proposed to derive a new device case from a real-world induction motor case-base with high dimensionality.

The authors developed the definition of construction defect and fault and construction defect management based on Polish and foreign publications. In order to assist identification of faults and their analysis in the process of home collection, the authors applied the Case Based Reasoning (CBR) method. In the paper, the authors used Case Based Reasoning (CBR) to support acceptance of apartments. The CBR method allows to determine the magnitude of global similarity for the problem under consideration between the new and old case from the Case Base, using weighted sums of local similarities using criteria weights as coefficients. As a result of CBR-based solutions, an Employer’s representative receives information about the type of construction defects that can be expected, their location and significance, occurrence frequency, and estimated repair cost.

This article presents a computer system for the identification of casting defects using the methodology of Case-Based Reasoning. The

system is a decision support tool in the diagnosis of defects in castings and is designed for small and medium-sized plants, where it is not

possible to take advantage of multi-criteria data. Without access to complete process data, the diagnosis of casting defects requires the use

of methods which process the information based on the experience and observations of a technologist responsible for the inspection of

ready castings. The problem, known and studied for a long time, was decided to be solved with a computer system using a CBR (CaseBased

Reasoning) methodology. The CBR methodology not only allows using expert knowledge accumulated in the implementation

phase, but also provides the system with an opportunity to "learn" by collecting new cases solved earlier by this system. The authors

present a solution to the system of inference based on the accumulated cases, in which the main principle of operation is searching for

similarities between the cases observed and cases stored in the knowledge base.

Hundreds or even thousands of defects can be found during the building acceptance, hence the need for solutions which will facilitate the defect management, including identification, costing and repair. The aim of the paper is to present the possible use of BIM to support the defect management process during the acceptance of apartments in multifamily residential buildings. The paper presents a concept of quality control support application called MWBIM (Map of Knowledge BIM) which will collect data about discovered construction defects, their recording and servicing with the BIM technology. MWBIM will run based on Building Information Modelling (BIM), Augmented Reality (AR), Case-Based Reasoning (CBR) and maps of knowledge. There are three phases in the operation of the application: preparatory phase (planning the order of acceptance meetings and elements to be checked), acceptance phase (data collection and assigning them to the building information model) and the reporting phase (reports generation, assigning defects to contractors, follow-up of repair status). The intended uses of the application are mainly personnel involved in the acceptance of apartments.