Reverse-engineering of architectural buildings based on an hybrid modeling approach

We thank MENSI and REALVIZ companies for their helpful comments and the following people for providing us images from their works: Francesca De Domenico (Fig. 1), Kyung-Tae Kim (Fig. 9). The CMN (French national center of patrimony buildings) is also acknowledged for the opportunity given to demonstrate our approach on the Hotel de Sully in Paris. We thank Tudor Driscu for his help on the English translation.This article presents a set of theoretical reflections and technical demonstrations that constitute a new methodological base for the architectural surveying and representation using computer graphics techniques. The problem we treated relates to three distinct concerns: the surveying of architectural objects, the construction and the semantic enrichment of their geometrical models, and their handling for the extraction of dimensional information. A hybrid approach to 3D reconstruction is described. This new approach combines range-based modeling and image-based modeling techniques; it integrates the concept of architectural feature-based modeling. To develop this concept set up a first process of extraction and formalization of architectural knowledge based on the analysis of architectural treaties is carried on. Then, the identified features are used to produce a template shape library. Finally the problem of the overall model structure and organization is addressed

Semantic-based modelling and representation of patrimony buildings

This article presents a methodological approach for the semantic description of patrimony buildings based both on theoretical reflections and on research experiences. To develop this approach, a first process of extraction and formalisation of architectural knowledge based on the analysis of architectural treaties is proposed. Then, the identified features are used to produce a template shape library dedicated to the buildings surveying. Finally, the problem of the overall model structuration and organization using semantic information is addressed for user handling purposes

A metadata enriched system for the documentation of multi-modal digital imaging surveys

In the field of Digital Heritage Studies, data provenance has always been an open and challenging issue. As Cultural Heritage objects are unique by definition, the methods, the practices and the strategies to build digital documentation are not homogeneous, universal or standardized. Metadata is a minimalistic  yet powerful form to source and describe a digital document. They are often required or mandatory at an advanced stage of a Digital Heritage project. Our approach is to integrate since data capture steps meaningful information to document a Digital Heritage asset as it is moreover being composed nowadays from multiple sources or multimodal imaging surveys. This article exposes the methodological and technical aspects related to the ongoing development of MEMoS; standing for Metadata Enriched Multimodal documentation System. MEMoS aims to contribute to data provenance issues in current multimodal imaging surveys. It explores a way to document CH oriented capture data sets with a versatile descriptive metadata scheme inspired from the W7 ontological model. In addition, an experiment illustrated by several case studies, explores the possibility to integrate those metadata encoded into 2D barcodes directly to the captured image set. The article lays the foundation of a three parted methodology namely describe - encode and display toward metadata enriched documentation of CH objects

A requirement mining framework to support complex sub-systems suppliers

The design of engineered socio-technical systems relies on a value chain within which suppliers must cope with larger and larger sets of requirements. Although 70 % of the total life cycle cost is committed during the concept phase and most industrial projects originally fail due to poor requirements engineering [1], very few methods and tools exist to support suppliers. In this paper, we propose to methodologically integrate data science techniques into a collaborative requirement mining framework so as to enable suppliers to gain insight and discover opportunities in a massive set of requirements. The proposed workflow is a five-phase process including: (1) the extraction of requirements from documents and (2) the analysis of their quality by using natural language processing techniques; (3) the segmentation of requirements into communities using text mining and graph theory; (4) the collaborative and multidisciplinary estimation of decision making criteria; and (5) the reporting of estimations via an analytical dashboard of statistical indicators. We conclude that the methodological integration of data science techniques is an effective way to gain insight from hundreds or thousands of requirements before making informed decisions early on. The software prototype that supports our workflow is a JAVA web application developed on top of a graph-oriented data model implemented with the NoSQL NEO4J graph database. As a future work, the semi-structured as-required baseline could be a sound input to feed a formal approach, such as model- and simulation-based systems engineering

A finite element/quaternion/asymptotic numerical method for the 3D simulation of flexible cables

In this paper, a method for the quasi-static simulation of flexible cables assembly in the context of automotive industry is presented. The cables geometry and behavior encourage to employ a geometrically exact beam model. The 3D kinematics is then based on the position of the centerline and on the orientation of the cross-sections, which is here represented by rotational quaternions. Their algebraic nature leads to a polynomial form of equilibrium equations. The continuous equations obtained are then discretized by the finite element method and easily recast under quadratic form by introducing additional slave variables. The asymptotic numerical method, a powerful solver for systems of quadratic equations, is then employed for the continuation of the branches of solution. The originality of this paper stands in the combination of all these methods which leads to a fast and accurate tool for the assembly process of cables. This is proved by running several classical validation tests and an industry-like example

CACDA: A knowledge graph for a context-aware cognitive design assistant

The design of complex engineered systems highly relies on a laborious zigzagging between computer-aided design (CAD) software and design rules prescribed by design manuals. Despite the emergence ofknowledge management techniques (ontology, expert system, text mining, etc.), companies continue tostore design rules in large and unstructured documents. To facilitate the integration of design rules andCAD software, we propose a knowledge graph that structures a large set of design rules in a computableformat. The knowledge graph organises entities of design rules (nodes), relationships among design rules(edges), as well as contextual information. The categorisation of entities and relationships in four sub-contexts: semantic, social, engineering, and IT – facilitates the development of the data model, especiallythe definition of the “design context” concept. The knowledge graph paves the way to a context-awarecognitive design assistant. Indeed, connected to or embedded in a CAD software, a context-aware cog-nitive design assistant will capture the design context in near real time and run reasoning operationson the knowledge graph to extend traditional CAD capabilities, such as the recommendation of designrules, the verification of design solutions, or the automation of design routines. Our validation experi-ment shows that the current version of the context-aware cognitive design assistant is more efficientthan the traditional document-based design. On average, participants using an unstructured design rulesdocument have a precision of 0.36 whereas participants using our demonstrator obtain a 0.61 precisionscore. Finally, designers supported by the design assistant spend more time designing than searching forapplicable design rules compared to the traditional design approach.Capgemini DEM

From the Semantic Point Cloud to Heritage-Building Information Modeling: A Semiautomatic Approach Exploiting Machine Learning

This work presents a semi-automatic approach to the 3D reconstruction of Heritage-Building Information Models from point clouds based on machine learning techniques. The use of digital information systems leveraging on three-dimensional (3D) representations in architectural heritage documentation and analysis is ever increasing. For the creation of such repositories, reality-based surveying techniques, such as photogrammetry and laser scanning, allow the fast collection of reliable digital replicas of the study objects in the form of point clouds. Besides, their output is raw and unstructured, and the transition to intelligible and semantic 3D representations is still a scarcely automated and time-consuming process requiring considerable human intervention. More refined methods for 3D data interpretation of heritage point clouds are therefore sought after. In tackling these issues, the proposed approach relies on (i) the application of machine learning techniques to semantically label 3D heritage data by identification of relevant geometric, radiometric and intensity features, and (ii) the use of the annotated data to streamline the construction of Heritage-Building Information Modeling (H-BIM) systems, where purely geometric information derived from surveying is associated with semantic descriptors on heritage documentation and management. The “Grand-Ducal Cloister” dataset, related to the emblematic case study of the Pisa Charterhouse, is discussed