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arXiv 2024年

作者： Du, Changyu Esser, Sebastian Nousias, Stavros Borrmann, André Chair of Computational Modeling and Simulation Technical University of Munich Munich80333 Germany

The conventional BIM authoring process typically requires designers to master complex and tedious modeling commands in order to materialize their design intentions within BIM authoring tools. This additional cognitive burden complicates the design process and hinders the adoption of BIM and model-based design in the AEC (Architecture, Engineering, and Construction) industry. To facilitate the expression of design intentions more intuitively, we propose Text2BIM, an LLM-based multi-agent framework that can generate 3D building models from natural language instructions. This framework orchestrates multiple LLM agents to collaborate and reason, transforming textual user input into imperative code that invokes the BIM authoring tool's APIs, thereby generating editable BIM models with internal layouts, external envelopes, and semantic information directly in the software. Furthermore, a rule-based model checker is introduced into the agentic workflow, utilizing predefined domain knowledge to guide the LLM agents in resolving issues within the generated models and iteratively improving model quality. Extensive experiments were conducted to compare and analyze the performance of three different LLMs under the proposed framework. The evaluation results demonstrate that our approach can effectively generate high-quality, structurally rational building models that are aligned with the abstract concepts specified by user input. Finally, an interactive software prototype was developed to integrate the framework into the BIM authoring software Vectorworks, showcasing the potential of modeling by chatting. © 2024, CC BY-NC-SA.

关键词： Software prototyping

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AEC Digital Twin Data - Why Structure Matters 19th

AEC Digital Twin Data - Why Structure Matters

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19th International Conference on Computing in Civil and Building Engineering, ICCCBE 2022

作者： Borrmann, André Schlenger, Jonas Bus, Nicolas Sacks, Rafael Chair of Computational Modeling and Simulation Technical University of Munich Munich Germany Centre Scientifique et Technique du Bâtiment Sophia-Antipolis France Technion – Israel Institute of Technology Haifa Israel

ISBN: (纸本)9783031353987

With the increasing adoption of the Digital Twin concept in the construction industry in the operations and maintenance phase, researchers and practitioners are increasingly seeking suitable technological solutions for the design and construction phases. While it is widely accepted that the required platforms hosting the digital twin must be cloud-based to fulfill the requirements of ubiquitous accessibility and centralized consistency, questions regarding the need for data schema remain. Some academics argue that a structure-free organization of data is suitable for realizing digital twins and the data streams from and to the respective platform. Hands-on experience in the BIM2TWIN project supports a counter argument, i.e., that structure-free data is insufficient for most use cases around AEC Digital Twins. The sheer information complexity of construction projects requires well-defined data structures enabling unambiguous and error-less interpretation. This becomes apparent when reflecting on the well-established concept of the data-information-knowledge pyramid describing that raw data must be processed into understandable and meaningful high-level information for human decision makers, subsequently providing the basis for cross-project domain knowledge. Based on this observation, we highlight that object-oriented modeling is a widely recognized information modeling technique that facilitates the structuring of complex domain information. We compare it with ontology-based model concepts that provide a similar, yet more abstract means for information modeling. © 2024, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Ontology

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Recognition of temporary vertical objects in large point clouds of construction sites

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Proceedings of the Institution of Civil Engineers: Smart Infrastructure and Construction 2022年第4期174卷 134-149页

作者： Torres, Miguel A. Vega Braun, Alexander Noichl, Florian Borrmann, André Bauer, Heiko Wohlfeld, Denis Chair of Computational Modeling and Simulation Technical University of Munich Munich Germany CIO TUM Group Lead Digital Twinning Chair of Computational Modeling and Simulation Technical University of Munich Munich Germany FARO Labs Faro Europe GmbH and Co. KG Korntal-Münchingen Germany Director of Research Networks FARO Labs Faro Europe GmbH and Co. KG Korntal-Münchingen Germany

Although adherence to the project schedule is the most critical performance metric among project owners, still 53% of typical construction projects exhibit schedule delays. While construction progress monitoring is key to allow effective project management, it is still a largely manual, error-prone and inefficient process. To contribute to more efficient construction progress monitoring, this research proposes a method to detect automatically the most common temporary object classes in large-scale laser scanner point clouds of construction sites. Finding the position of these objects in the point cloud can help determine the current state of construction progress and verify compliance with safety regulations. The proposed workflow includes a combination of several techniques: image processing over vertical projections of point clouds, finding patterns in three-dimensional (3D) detected contours and performing checks over vertical cross-sections with deep learning methods. After applying and testing the method on three real-world point clouds and testing with three object categories (cranes, scaffolds and formwork), the results reveal that the authors' technique achieves rates above 88% for precision and recall and outstanding computational performance. These metrics demonstrate the capability of the method to support automatic 3D object detection in point clouds of construction sites. © 2022 ICE Publishing: All rights reserved.

关键词： Deep learning

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From ductile damage to unilateral contact via point-wise implicit discontinuity at the infinitesimal element level

arXiv

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arXiv 2023年

作者： Daneshyar, Alireza Herrmann, Leon Kollmannsberger, Stefan Chair of Computational Modeling and Simulation Technical University of Munich Germany

Ductile damage models and cohesive laws incorporate the material plasticity entailing the growth of irrecoverable deformations even after complete failure. This unrealistic growth remains concealed until the unilateral effects arising from the crack closure emerge. We address this issue by proposing a new strategy to cope with the entire process of failure, from the very inception in the form of diffuse damage to the final stage, i.e. the emergence of sharp cracks. To this end, we introduce a new strain field, termed discontinuity strain, to the conventional additive strain decomposition to account for discontinuities in a continuous sense so that the standard principle of virtual work applies. We treat this strain field similar to a strong discontinuity, yet without introducing new kinematic variables and nonlinear boundary conditions. In this paper, we demonstrate the effectiveness of this new strategy at a simple ductile damage constitutive model. The model uses a scalar damage index to control the degradation process. The discontinuity strain field is injected into the strain decomposition if this damage index exceeds a certain threshold. The threshold corresponds to the limit at which the induced imperfections merge and form a discrete crack. With three-point bending tests under pure mode I and mixed-mode conditions, we demonstrate that this augmentation does not show the early crack closure artifact which is wrongly predicted by plastic damage formulations at load reversal. We also use the concrete damaged plasticity model provided in Abaqus commercial finite element program for our comparison. Lastly, a high-intensity low-cycle fatigue test demonstrates the unilateral effects resulting from the complete closure of the induced crack. © 2023, CC BY-NC-ND.

关键词： Crack closure

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An Alternative Approach to Automated Code Checking – Application of Graph Neural Networks Trained on Synthetic Data for an Accessibility Check Case Study 19th

An Alternative Approach to Automated Code Checking – Applic...

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19th International Conference on Computing in Civil and Building Engineering, ICCCBE 2022

作者： Bloch, Tanya Borrmann, André Pauwels, Pieter Faculty of Civil and Environmental Engineering Technion Israel Institute of Technology Haifa Israel Chair of Computational Modeling and Simulation Technical University of Munich Munich Germany Faculty of Built Environment Eindhoven University of Technology Eindhoven Netherlands

ISBN: (纸本)9783031353987

Automated Code Checking (ACC) can be defined as a classification task aiming to classify building objects as compliant or not compliant to a code provision at hand. While Machine Learning (ML) is a useful tool to perform such classification tasks, it presents several drawbacks and limitations. Buildings are complex compositions of instances that are related to each other by functional and topological relationships. This type of data can be easily supported by property graphs that provide a flexible representation of attributes for every instance as well as the relationships between the instances. This, together with the recent developments in the field of graph-based learning led the authors to explore a novel approach for ACC supported by Graph Neural Networks (GNN). This paper presents a new workflow that implements GNNs for ACC to leverage the advantages of ML but alleviate the limitations. We illustrate the suggested workflow by training a GNN model on a synthetic data set and using the trained classifier to check compliance of a real BIM model to accessibility requirements. The accuracy of the classifier on a test set is 86% and the accuracy of obtained results during the accessibility check is 82%. This suggests that GNNs are applicable to ACC and that classifiers trained on synthetic data can be used to classify building design provided by the industry. While the results are encouraging, they also point to the need for further research to establish the scope and boundary conditions of applying GNNs to ACC. © 2024, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Machine learning

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Thermodynamics and dielectric response of BaTiO_(3)by data-driven modeling

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npj computational Materials 2022年第1期8卷 1998-2014页

作者： Lorenzo Gigli Max Veit Michele Kotiuga Giovanni Pizzi Nicola Marzari Michele Ceriotti Laboratory of Computational Science and Modeling(COSMO) Institute of MaterialsÉcole Polytechnique Fédérale de LausanneCH-1015LausanneSwitzerland Theory and Simulation of Materials(THEOS)and National Centre for Computational Design and Discovery of Novel Materials(MARVEL) École Polytechnique Fédérale de LausanneCH-1015LausanneSwitzerland

modeling ferroelectric materials from first principles is one of the successes of density-functional theory and the driver of much development effort,requiring an accurate description of the electronic processes and the thermodynamic equilibrium that drive the spontaneous symmetry breaking and the emergence of macroscopic *** demonstrate the development and application of an integrated machine learning model that describes on the same footing structural,energetic,and functional properties of barium titanate(BaTiO_(3)),a prototypical *** model uses ab initio calculations as a reference and achieves accurate yet inexpensive predictions of energy and polarization on time and length scales that are not accessible to direct ab initio *** predictions allow us to assess the microscopic mechanism of the ferroelectric *** presence of an order-disorder transition for the Ti off-centered states is the main driver of the ferroelectric transition,even though the coupling between symmetry breaking and cell distortions determines the presence of intermediate,partly-ordered ***,we thoroughly probe the static and dynamical behavior of BaTiO_(3)across its phase diagram without the need to introduce a coarse-grained description of the ferroelectric ***,we apply the polarization model to calculate the dielectric response properties of the material in a full ab initio manner,again reproducing the correct qualitative experimental behavior.

关键词： transition ferroelectric dielectric

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Investigating Robot Dogs for Construction Monitoring: A Comparative Analysis of Specifications and On-site Requirements

arXiv

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arXiv 2024年

作者： Torres, Miguel A. Vega Pfitzner, Fabian Chair of Computational Modeling and Simulation TU Munich Arcisstr. 21 Munich80333 Germany

Robot dogs are receiving increasing attention in various fields of research. However, the number of studies investigating their potential usability on construction sites is scarce. The construction industry implies several human resource-demanding tasks such as safety monitoring, material transportation, and site inspections. Robot dogs can address some of these challenges by providing automated support and lowering manual effort. In this paper, we investigate the potential usability of currently available robot dogs on construction sites in terms of focusing on their different specifications and on-site requirements to support data acquisition. In addition, we conducted a real-world experiment on a large-scale construction site using a quadruped robot. In conclusion, we consider robot dogs to be a valuable asset for monitoring intricate construction environments in the future, particularly as their limitations are mitigated through technical advancements. © 2024, CC BY.

关键词： Multipurpose robots

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Generative Adversarial Networks Enable Outlier Detection and Property Monitoring for Additive Manufacturing of Complex Structures

SSRN

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SSRN 2024年

作者： Henkes, Alexander Herrmann, Leon Wessels, Henning Kollmannsberger, Stefan Computational Mechanics Group ETH Zürich Switzerland Chair of Computational Modeling and Simulation TU Munich Germany Division Data-Driven Modeling of Mechanical Systems TU Braunschweig Germany

Additive manufacturing technologies have seen significant economic growth over the past years. Although additive manufacturing processes have matured in many areas, difficulties with regard to printing accuracy persist. Possible defects are, e.g., the generation of unwanted internal pores or a lack of fusion between layers. In general, defects result in a deviation between as-planned and as-built geometries, all of which can be difficult to detect in an automatized fashion. Previous work has shown that image-based simulation can assist in quality monitoring of produced parts and may complement experimental testing. Yet, both experimental testing and simulation-based approaches are involved and not yet directly applicable to all manufactured parts in a series production. The paper at hand suggests a remedy to this problem by using a generative adversarial network. Generative adversarial networks have shown to be able to emulate as-built geometries of engineering relevance. Moreover, they can realistically reproduce the distributions of such deviations. To this end, we present how this feature can be harvested to employ generative adversarial networks for outlier detection. To this end, we use the discriminator as a classifier on as-built parts to judge whether an as-built structure is acceptable or defective. The viability of the approach is demonstrated on basic artificial structures with spherical voids as well as additively manufactured lattice structures whose geometry is acquired after production via computed tomography. The methodology is not only applicable for automated property monitoring but potentially also for reliability estimates of neural network based property predictors. © 2024, The Authors. All rights reserved.

关键词： 3D printing

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Transfer Learning Enhanced Full Waveform Inversion*

Transfer Learning Enhanced Full Waveform Inversion*

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IEEE/ASME (AIM) International Conference on Advanced Intelligent Mechatronics

作者： Stefan Kollmannsberger Divya Singh Leon Herrmann Chair of Computational Modeling and Simulation Technical University of Munich Munich Bavaria Germany

We propose a way to favorably employ neural networks in the field of non-destructive testing using Full Waveform Inversion (FWI). The presented methodology discretizes the unknown material distribution in the domain with a neural network within an adjoint optimization. To further increase efficiency of the FWI, pretrained neural networks are used to provide a good starting point for the inversion. This reduces the number of iterations in the Full Waveform Inversion for specific, yet generalizable settings.

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Fabrication information modeling: interfacing building information modeling with digital fabrication

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Construction Robotics 2022年第2期6卷 87-99页

作者： Slepicka, Martin Vilgertshofer, Simon Borrmann, André Chair of Computational Modeling and Simulation Technical University of Munich Munich Germany

Digital manufacturing methods have been successfully used in different industries for years and have since had a positive effect on the development of their productivity. These methods offer significantly greater design freedom and make it possible to develop shape-optimized and function-activated components. In the construction industry, however, these technologies are only being used reluctantly, even though additive methods could make resource-efficient construction possible. The possibly decisive disadvantage of these methods is that a significantly higher granularity of product and process information is required, thus significantly increasing the planning effort. A circumstance that the framework described in this study, fabrication information modeling (FIM), could significantly mitigate by linking digital fabrication and BIM-based digital building design via a digital chain. For this purpose, FIM provides a methodology with which the information of a digital building model can be detailed, component by component, in a fabrication-aware manner. Based on the open exchange data format IFC, the FIM framework integrates seamlessly into the BIM context and enables automated detailing of the design information.

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