The application of robotics in construction is hindered by the site environment, which is unstructured and subject to change. At the same time, however, buildings and corresponding sites can be accurately described by Building Information Modeling (BIM). Such a model contains geometric and semantic data about the construction and operation phases of the building and it is already available at the design phase. We propose a method to leverage BIM for simple yet efficient deployment of robotic systems for construction and operation of buildings. With our proposed approach, BIM is used to provide the robot with a priori geometric and semantic information on the environment and to store information on the operation progress. We present two applications that verify the effectiveness of our proposed method. This system represents a step forward towards an easier application of robots in construction.

Decision making is a relevant task in the building construction sector, and various systems and methods for decision support are emerging. By means of a systematic literature review, this article identifies the methods for decision making in building construction and the lifecycle phases for which decision support systems are proposed. The selected articles are analyzed and grouped according to the adopted decision-making methods and the defined lifecycle phases. The findings show that multiple criteria decision analysis is the most used method for decision support in building construction and that the construction phase is the most addressed phase within the relevant existing works. The findings related to the construction phase are further refined by grouping the articles into application areas and by reviewing in detail the proposed methods therein. The scarce availability of data and project cases is identified as the most common barrier for the successful development and implementation of decision support systems in the building construction sector. This work provides a basis for scientists and practitioners for identifying suitable methods for decision-making support in a specific lifecycle phase of a building.

In order to efficiently transform business processes (such as product design, product engineering, production, logistics, sales, deliveries, etc.) into digitally automated processes, new concepts have been introduced in both the manufacturing and construction industries. Under the term Industry 4.0, promising possibilities for high-performance production processes are emerging based on e.g., digital twins and cyber-physical systems. However, the construction industry lags behind in adapting these ideas, and is still facing severe productivity deficits. This paper addresses these deficits by assessing the hypothesis of Building Information Modeling—as the digital twinning methodology in construction—representing a key driver for digital automation and thus enabling more productive processes. To this end, we apply a design science research approach to develop artefacts using computational methods for the automation of business processes in a configure-to-order industry partner. The evaluation is done in the context of a pilot project together with this industry partner. The findings obtained in the pilot project revealed time savings in the phases of bid estimation and work preparation. Based on the findings, the applicability and utility of the suggested approach are discussed and allow for the conclusion that Building Information Model data can usefully streamline and automate many processes at the interface between design and production, if structured and preprocessed purposefully.

Many researchers have stated that lean and building information modeling (BIM) have positive synergies. This integrative literature review aims at exploring this body of knowledge within the scope of combinations of BIM and the Last Planner^® System, as an important Lean construction method, in the phase of construction execution. The research motivation is to find out whether a comprehensive understanding of how to take advantage of these synergies exists. Eventually, the question arises of how to condense this understanding—if existing—into a robust conceptual model for integration. As a theoretical backbone, we will make use of the original BIM-Lean interaction matrix. The hypothesis is that new BIM functionalities have been evolved since the first formulation of this interaction matrix almost 10 years ago. These new BIM functionalities cause new interactions with existing lean principles. We will focus on interactions that refer directly or indirectly to production planning and control and use them to find the most relevant literature for this review. Within the content analysis, as a part of this review, we focus on existing conceptual models and frameworks for integration of BIM and the Last Planner^® System and reveal their shortcomings. Eventually, we will propose a new conceptual model.

The construction sector is one of the major global economies and is characterised by low productivity and high inefficiencies, but could highly benefit from the introduction of robotic equipment in terms of productivity, safety, and quality. As the development and the availability of robotic solutions for the construction sector increases, the evaluation of their potential benefits compared to conventional processes that are currently adopted on construction sites becomes compelling. To this end, we exploit Bayesian decision theory and apply an axiomatic design guideline for the development of a decision-theoretic expert system that: (i) evaluates the utility of available alternatives based on evidence; (ii) accounts for uncertainty; and (iii) exploits both expert knowledge and preferences of the users. The development process is illustrated by means of exemplary use case scenarios that compare manual and robotic processes. A use case scenario that compares manual and robotic marking and spraying is chosen for describing the development process in detail. Findings show how decision making in equipment selection can be supported by means of dedicated systems for decision support, developed in collaboration with domain experts.