Automatic Generation of Semantically Rich As‐Built Building Information Models Using 2D Images: A Derivative‐Free Optimization Approach

Abstract

Over the past decade a considerable number of studies have focused on generating semantically rich as-built building information models (BIMs). However, the prevailing methods rely on laborious manual segmentation or automatic but error-prone segmentation. In addition, the methods failed to make good use of existing semantics sources. This article presents a novel segmentation-free derivative-free optimization (DFO) approach that translates the generation of asbuilt BIMs from 2D images into an optimization problem of fitting BIM components regarding architectural and topological constraints. The semantics of the BIMs are subsequently enriched by linking the fitted components with existing semantics sources. The approach was prototyped in two experiments using an outdoor and an indoor case, respectively. The results showed that in the outdoor case 12 out of 13 BIM components were correctly generated within 1.5 hours, and in the indoor case all target BIM components were correctly generated with a root-mean-square deviation (RMSD) of 3.9 cm in about 2.5 hours. The main computational novelties of this study are: (1) to translate the automatic as-built BIM generation from 2D images as an optimization problem; (2) to develop an effective and segmentation-free approach that is fundamentally different from prevailing methods; and (3) to exploit online open BIM component information for semantic enrichment, which, to a certain extent, alleviates the dilemma between information inadequacy and information overload in BIM development.

Building Information Modeling (BIM) for Structural Engineering: A Bibliometric Analysis of the Literature

“…With BIM in mind, future studies can target the unique characteristics of cast-in-place concrete in developing future versions of IFC, overlapping of structural elements, use of reinforcement bars, and the need for precision in loads and material considerations [103]. Automated creation of centralized accurate semantically rich as-built building information models of structural elements also remains a fertile area for future research, given various challenges that affect successful implementation of BIM for such purposes [104][105][106][107].…”

From Semantic Segmentation to Semantic Registration: Derivative-Free Optimization–Based Approach for Automatic Generation of Semantically Rich As-Built Building Information Models from 3D Point Clouds

“…In construction and civil engineering, Kaveh et al's (2011) applied CMA-ES for optimal design of a 26-story-tower space truss exhibited a 31% improvement in weight over a previous result by genetic algorithm, and Athanasiou et al's (2011) CMA-ES for structural system identification in earthquake engineering saw a 53% improvement from previous studies. Xue et al (2018b) applied the CMA-ES to as-built BIM generation and reconstructed an outdoor scene of a demolished building and an indoor furniture scene with a root-mean-squareerror (RMSE) of 3.9 cm. However, several hours were spent generating the necessary small BIMs, which feature just a few components, the major issue being the thousands of model manipulations (e.g., creating, moving, rotating, scaling, and detecting components, while projecting 3D BIM to 2D images) on commercial BIM platforms.…”

“…Based on the COBIMG, which stands for constrained optimization-based BIM generator (available at: https://github.com/ffxue/cobimg) software library (Xue et al, 2018b), the authors developed the "COBIMG-Revit", which is an automatic as-built BIM generation plugin for a popular BIM platform (i.e., Autodesk Revit). Figure 2 shows the three major modules of COBIMG-Revit.…”

“…Volk et al (2014) delineate these approaches as either data-driven or model-driven. A data-driven method performs modeling based on the extraction of features, shapes, materials, and statistics from the preprocessed measurement data, while a model-driven method generally refers to comparing the known components against the datasets (e.g., point clouds or imagery), recognizing components, and fitting them in a BIM based on pre-defined rules (Xue et al, 2018b). Manual creation of as-built BIMs by professional modelers is often adopted but this process is extremely tedious, time-consuming, and error-prone (Pătrăucean et al, 2015;Perez-Perez et al, 2016;Xue et al, 2018a;Tang et al, 2010;Jung et al, 2014), and these limitations become insurmountable when modeling at an urban scale (i.e., inputting BIM with CIM).…”

Employing Simulated Annealing Algorithms to Automatically Resolve MEP Clashes in Building Information Modeling Models

“…Lin and Lin [4] took advantage of API to propose a final as-built BIM model management system for owners to handle the inspection, modification, and confirmation work beyond project closeout. Based on a repetitive trial-and-error procedure, Xue and Lu [11] presented a novel segmentation-free, derivative-free optimization approach that translates as-built BIMs from two-dimensional images into an optimization problem of fitting BIM components within architectural and topological constraints. Moreover, to evaluate the overall thermal transfer value of the building envelope and the cost of construction, Lim and Majid [12] developed a BIM-GA optimization method by using the functionalities of BIM software, Autodesk Revit, the iterated learning of GA, and the computer programming of PHP.…”