Towards spatial reasoning on building information models

The paper presents a conceptual study on the application of spatial reasoning on building information models. In many cases, building regulations and client demands imply traints on the building design with inherent spatial semantics. If we are able to represent these spatial constraints in a computerinterpretable way, the building design can be checked for fulfilling them. In this context, spatial reasoning technology can be applied in two different ways. First, we can check the consistency of the spatial constraints in effect, i.e. find out whether there are contradictions between them. Second, we can check whether a concrete building design is compliant with these constraints. The paper gives a detailed overview on the currently available spatial calculi and introduces two possible implementation pproaches

An Artificial Neural Network Framework for Pedestrian Walking Behavior Modeling and Simulation

Movement behavior models of pedestrian agents form the basis of computational crowd simulations. In contemporary research, a large number of models exist. However, there is still no walking behavior model that can address the various influence factors of movement behavior holistically. Thus, we endorse the use of artificial neural networks to develop walking behavior models because machine learning methods can integrate behavioral factors efficiently, automatically, and data-driven. In this paper, we support this approach by providing a framework that describes how to include artificial neural networks into a pedestrian research context. The framework comprises 5 phases: data, replay, training, simulation, and validation. Furthermore, we describe and discuss a prototype of the framework

Integrating RC bridge defect information into BIM models

Reinforced Concrete bridges are a vitally important part of our infrastructure. The status of this infrastructure needs to be monitored on a continuous basis in order to ensure its safety and functionality. This is currently being done by authorities worldwide via bridge inspection reports. The format and storage of these reports varies considerably across different authorities around the world and is sometimes comprised into a bridge management system (BMS). The lack of standardization severely hinders the use of inspection information for knowledge generation use cases of both practitioners and researchers. This paper presents an exploratory analysis and as a result an information model and a candidate binding to IFC to categorize inspection information of RC bridges and to standardize storage of this information in a format that is suitable for sharing and comparing it between different users and varying requirements. We were able to show in three steps, that IFC in its latest version IFC 4 provides sufficient functionality to serve as a basis for integrating relevant defect information and imagery. Firstly, we extracted types of defects and properties needed for bridge assessment from existing bridge inspection manuals. Secondly, we modelled the defect entities, their properties and relations and thirdly, mapped them to appropriate IFC entities. A prototypical implementation serves as a proof of concept for automated sharing and comparing of information needed in RC bridge inspections and for establishing a knowledge base for bridge performance over time and across authorities.This work is partly funded by Trimble Inc. and by the European Union’s Seventh Framework Programme for research, technological development and demonstration under grant agreement no. 31109806.0007. SeeBridge is co-funded by Funding Partners of the ERA-NET Plus Infravation and the European Commission. The Funding Partners of the Infravation 2014 Call are: Ministerie van Infrastructuur en Milieu, Rijkswaterstaat, Bundesministerium für Verkehr, Bau und Stadtentwicklung, Danish Road Directorate, Statens Vegvesen Vegdirektoratet, Trafikverket – Trv, Vegagerðin, Ministere de L’ecologie, du Developpement Durable et de L’energie, Centro para el Desarrollo Tecnologico Industrial, Anas S.P.A., Netivei Israel – National Transport Infrastructure Company Ltd. and Federal Highway Administration USDOT

Void-growing: a novel Scan-to-BIM method for manhattan world buildings from point cloud

The automated generation of 3D models of buildings from point clouds is still under heavy research. Currently, this Scan-to-BIM process requires high manual effort, and the previous research focuses on simple room structure under low occlusion level. We propose a novel “void-growing” approach that extracts walls, floors, and ceilings automatically. Different from the majority of current approaches starting with detecting surfaces of elements in point clouds, our approach grows the void volume space inside a room first. Our approach performs well in occluded environments. It can reconstruct simple cuboid rooms as well as complex rooms like L-shape and U-shape rooms. Different ceiling heights caused by suspended ceilings can also be represented properly.TUM-IAS Hans Fisher Senior Fellowshi

Towards predicting Pedestrian Evacuation Time and Density from Floorplans using a Vision Transformer

Conventional pedestrian simulators are inevitable tools in the design process of a building, as they enable project engineers to prevent overcrowding situations and plan escape routes for evacuation. However, simulation runtime and the multiple cumbersome steps in generating simulation results are potential bottlenecks during the building design process. Data-driven approaches have demonstrated their capability to outperform conventional methods in speed while delivering similar or even better results across many disciplines. In this work, we present a deep learning-based approach based on a Vision Transformer to predict density heatmaps over time and total evacuation time from a given floorplan. Specifically, due to limited availability of public datasets, we implement a parametric data generation pipeline including a conventional simulator. This enables us to build a large synthetic dataset that we use to train our architecture. Furthermore, we seamlessly integrate our model into a BIM-authoring tool to generate simulation results instantly and automatically

A System Dynamics based Perspective to Help to Understand the Managerial Big Picture in Respect of Urban Event Dynamics

AbstractIn the PED-community, a lot of conducted work focuses on a detailed aspect of the big picture in respect of pedestrian dynamics and disaster avoidance. Surprisingly, the field of research does not offer a lot of models including a managerial macro perspective to explain – for example – why there are mass gatherings that result in high density pedestrian conditions. To improve the mental models of researchers, managers and policy makers, this paper tries to tackle this research gap, by using the methodology of System Dynamics to explain with causal loop diagrams occurring dynamics of urban events to avoid critical situations beforehand

Introducing causal inference in the energy-efficient building design process

“What-if” questions are intuitively generated and commonly asked during the design process. Engineers and architects need to inherently conduct design decisions, progressing from one phase to another. They either use empirical domain experience, simulations, or data-driven methods to acquire consequential feedback. We take an example from an interdisciplinary domain of energy-efficient building design to argue that the current methods for decision support have limitations or deficiencies in four aspects: parametric independency identification, gaps in integrating knowledge-based and data-driven approaches, less explicit model interpretation, and ambiguous decision support boundaries. In this study, we first clarify the nature of dynamic experience in individuals and constant principal knowledge in design. Subsequently, we introduce causal inference into the domain. A four-step process is proposed to discover and analyze parametric dependencies in a mathematically rigorous and computationally efficient manner by identifying the causal diagram with interventions. The causal diagram provides a nexus for integrating domain knowledge with data-driven methods, providing interpretability and testability against the domain experience within the design space. Extracting causal structures from the data is close to the nature design reasoning process. As an illustration, we applied the properties of the proposed estimators through simulations. The paper concludes with a feasibility study demonstrating the proposed framework's realization

Virtual Engineering: Informations-, Simulations- und Kooperationsmodelle für den ingenieurgerechten Entwurfsprozess

Entwurfsprozesse in den Ingenieurwissenschaften finden in aller Regel nicht linear sondern in rückgekoppelten Zyklen statt. Dabei spielt Kommunikation und Kooperation eine entscheidende Rolle, zumal der Entwurf komplexer technischer Produkte nahezu immer in multidisziplinären Teams zu erfolgen hat. Neuere Entwicklungen im Bereich der Produkt- und Prozessmodelle sowie der Anbindung von Höchstleistungscomputern ermöglichen es heute, selbst für komplexe Simulationsaufgaben eine direkte Interaktion des Ingenieurs mit dem Modell sogar während der Berechnung zu erlauben und auf diese Weise diesen Rückkopplungsprozess deutlich zu beschleunigen. Noch einen Schritt weiter gehen so genannten Computational Steering Systeme, welche die Zusammenarbeit von Ingenieurteams über schnelle Computernetze durch gemeinsame ‚virtuelle Projekträume’ unterstützen. Der Aufsatz gibt einen Überblick über neuere Entwicklungen von computergestützten Informations-, Simulations- und Kooperationsmodellen im Bau- und Umweltingenieurwesen und zeigt Beispiele zu einer netzgestützten Simulation der Luftströmung in Innenräumen

Integration of Constraints into Digital Building Models for Cooperative Planning Processes

The uniqueness and the long life cycle of buildings imply a dynamically modifiable building model. The technological foundation for the management of digital building models, a dynamic model management system (MMS), developed by our research group, allows to explicitly access and to modify the object model of the stored planning data. In this paper, the integration of constraints in digital building models will be shown. Constraints are conditions, which apply to the instances of domain model classes, and are defined by the user at runtime of the information system. For the expression of constraints, the Constraint Modelling Language (CML) has been developed and will be described in this paper. CML is a powerful, intuitively usable object-oriented language, which allows the expression of constraints at a high semantic level. A constrained-enabled MMS can verify, whether an instance fulfils the applying constraints. To ensure flexibility, the evaluation of constraints is not implicitly performed by the systems, but explicitly initiated by the user. A classification of constraint types and example usage scenarios are given