This study presents established methods, along with new algorithmic developments, to automate the point cloud processing within the Field Information Modeling (FIM)™ framework. More specifically, given an n-D designed information model, and the point cloud’s spatial uncertainty, the problem of automatic assignment of the point clouds to their corresponding elements within the designed model is considered. The methods addressed two classes of field conditions, namely, (i) negligible construction errors; and (ii) existence of construction errors. The emphasis was given to describing and defining the assumptions in each method and shed light on some of their potentials and limitations in practical settings. Considering the shortcomings of current point cloud processing frameworks, three new and generic algorithms were developed to help solve the point cloud to model assignment in field conditions with both negligible, and existence (or speculation) of construction errors. The effectiveness of the new methods was demonstrated in real-world point clouds, acquired from construction projects, with promising results.

In Germany, as in many developed countries, over 60% of buildings were constructed before 1978, where most are in critical condition, requiring either demolition with plans for redevelopment or renovation and rehabilitation. Given the urgency of climate action and relevant sustainable development goals set by the United Nations, more attention must be shifted toward the various sustainability aspects when deciding on the strategy for renovation or redevelopment of existing buildings. To this end, this study focused on developing a smart decision support framework for aging buildings based on lifecycle sustainability considerations. The framework integrated digital technological advancements, such as building information modeling (BIM), point clouds processing with field information modeling (FIM)®, and structural optimization, together with life-cycle assessment to evaluate and rate the environmental impact of different solutions. Three sustainability aspects, namely, cost, energy consumption, and carbon emissions, were quantitatively evaluated and compared in two scenarios, namely, renovation, and demolition or deconstruction combined with redevelopment. A real building constructed in 1961 was the subject of the experiments to validate the framework. The result outlined the limitations and advantages of each method in terms of economics and environmental impact. It was further observed that optimizing the building design with the goal to reduce embodied energy and carbon in compliance with modern energy standards, was crucial to improve overall energy performance. This work demonstrated that the developed BIM-based framework to assess the environmental impact of rehabilitation work in aging buildings can provide effective ratings to guide decision-making in real-world projects.

This paper examines some of the largely neglected areas of opportunity to utilize skeletal space structure systems in support of modular, industrialized, economical, sustainable and digital future of the construction industry. In this context, the feasibility of future use of skeletal space structures is studied for a few classes of engineering structures, namely, residential apartment buildings, and offshore platforms, along with their suitability for reconstruction, renovation, modernization and retrofit of damaged buildings and urban areas of cultural heritage significance. Finally, the particulars of lean project management of space structures are discussed with emphasis on engineering and economic factors, production management, environmental aspects, quality management, and reliability, maintainability and sustainability considerations. This article concludes that skeletal space structures can fulfil many of the essential construction requirements of modern societies, especially those facing environmental challenges, all while allowing for design flexibility and mass customization.

The optimal structural design is imperative to minimize material consumption and reduce the environmental impact of construction. Given the complexity in the formulation of structural design problems, the process of optimization is commonly performed using artificial intelligence (AI) global optimization, such as Genetic Algorithm (GA). However, the integration of AI-based optimization together with visual programming (VP) in building information modeling (BIM) projects warrants further investigation. This study proposes a workflow by combining structure analysis, VP, BIM, and GA to optimize trusses. The methodology encompasses several steps including: (i) generation of parametric trusses in Dynamo VP; (ii) performing the finite element modeling (FEM) using Robot Structural Analysis (RSA); (iii) retrieving and evaluating the FEM results interchangeably between Dynamo and RSA; (iv) finding the best solution using GA; and (v) importing the optimized model into Revit, enabling the user to perform simulations and engineering analysis, such as life cycle assessment (LCA) and quantity surveying. The feasibility of the proposed workflow was tested on benchmark problems and compared with open literature. The outcomes of this study offer insight into the opportunities and limitations of combining VP, GA, FEA, and BIM for structural optimization applications, particularly to enhance structural efficiency and sustainability in construction.