Contributions to rework prevention in construction projects
Authors:   , Macarulla Martí Marcel 
Published: July 2023

Literature usually suggests that construction organization can reduce the costs derived from rework implementing quality management systems. Most common challenges and obstacles that construction organizations face during the implementation process and use of quality management systems are related to "how" the information can be recorded in an effective way, and "what" can be done with the recorded information.
The aim of this dissertation is to focus on improving the defects recording process in the construction industry, and to propose methods and tools to use defects recorded on-site to prevent and reduce rework in the construction industry.
The dissertation starts with the development of a conceptual model used to characterize defects. The current model is based on previously existing models and their adaptation to the context of the Spanish residential building sector. The model is based on the enumeration of the parameters that allow characterizing defects. The final model includes 6 parameters, with a list of standardized words and their definitions.
The pre-established vocabulary lists are based on existing classification systems proposed by recognised organisations, authors and research reports, but then adapted to the Spanish context. However, in terms of defects, no standardised list exists. For this reason a taxonomy of defects is further developed for the Spanish construction sector. The aforementioned taxonomy consists of 15 main categories and 19 subcategories.
The dissertation continues with the development of a methodology to track defects in the construction industry and its implementation in an IT tool called MoBuild. The obtained tracking system is based on images and tags. The strengths the abovementioned tracking system is to record information in a structured way and enable further statistical analysis of the recorded information. The new approach implemented in the MoBuild application allows practitioners to reduce the time of the recording process, facilitating the implementation of quality management systems, such as ISO 9000 in construction organizations.
Furthermore, research proposes a quantitative methodology for dealing with potential adverse quality risks during the pre-construction stages of residential buildings and other similar types of constructions. The strength of this methodology lies in the fact that it helps designers to explicitly consider on-site quality during the design process. Designers can compare several design alternatives during the design phase, and determine the corresponding overall quality risk levels of a construction project without their creative talents being restricted. The methodology is especially worthwhile for those less-experienced designers who lack the required skills and knowledge to recognize quality risks in developing optimal designs.
The methodology also serves as an assessment tool for construction companies. It can be used to measure the potential quality risks of construction projects and its subsequent construction activities. The suggested methodology also allows construction companies to optimize their on-site performance in the quality domain during the planning and preparation stages.
Finally, this dissertation analyses the quality perceived by the end users during the post-handover stage. Different statistical methods are used to demonstrate the usefulness of the recorded data for the construction organizations. The aim is to highlight the essential role that records play in the operation of a quality company, in particular by providing essential evidence of the operation of quality systems.
The aforementioned statistical analysis determines the type of defects detected; the elements affected by defects; the areas where defects are detected; which subcontractors produce defects; the source of the detected defects; the origin of the detected defects and; the influence of the building type and its characteristics in the number of defects detected.