Past, present and future of information and knowledge sharing in the construction industry: Towards semantic service-based e-construction

The paper reviews product data technology initiatives in the construction sector and provides a synthesis of related ICT industry needs. A comparison between (a) the data centric characteristics of Product Data Technology (PDT) and (b) ontology with a focus on semantics, is given, highlighting the pros and cons of each approach. The paper advocates the migration from data-centric application integration to ontology-based business process support, and proposes inter-enterprise collaboration architectures and frameworks based on semantic services, underpinned by ontology-based knowledge structures. The paper discusses the main reasons behind the low industry take up of product data technology, and proposes a preliminary roadmap for the wide industry diffusion of the proposed approach. In this respect, the paper stresses the value of adopting alliance-based modes of operation

Comparison of knowledge representation in PDM and by semantic networks

\u27Nowadays, computer-aided tools have enabled the creation of electronic design documents on an unprecedented scale, while determining and finding what can be reused for a new design is like searching for a \u27needle in a haystack\u27. (…) The availability of such extensive knowledge resources is creating new challenges as well as opportunities for research on how to retrieve and reuse the knowl-edge from existing designs.\u27 [1] If the requested knowledge is implicit (which means that it is only in the minds of the employees of a company) the retrieval and reuse of knowledge is even more com-plicated. By representing the (engineering) data backbone of a company, PDM systems are the software implementation which should support the designer to retrieve information about existing and successful design projects. This paper shows that the known data classification approaches of common PDM systems are not applicable to represent implicit (tacit) knowledge. Furthermore a new approach to knowledge representation is introduced by using Semantic Networks. The feasibility of the presented work is shown by a use-case scenario in which the conventional PDM system supported product development process is compared with the proposed way by using the soft-ware \u27The Semaril\u27 — a software tool developed at the Institute of Engineering Design/CAD based on Semantic Networks [2]

Similarity Assessment and Retrieval of CAD Models

Ph.DDOCTOR OF PHILOSOPH

Relaxed lightweight assembly retrieval using vector space model

International audienceAssembly searching technologies are important for the improvement of design reusability. However, existing methods require that assemblies possess high-level information, and thus cannot be applied in lightweight assemblies. In this paper, we propose a novel relaxed lightweight assembly retrieval approach based on a vector space model (VSM). By decomposing the assemblies represented in a watertight polygon mesh into bags of parts, and considering the queries as a vague specification of a set of parts, the resilient ranking strategy in VSM is successfully applied in the assembly retrieval. Furthermore, we take the scale-sensitive similarities between parts into the evaluation of matching values, and extend the original VSM to a relaxed matching framework. This framework allows users to input any fuzzy queries, is capable of measuring the results quantitatively, and performs well in retrieving assemblies with specified characteristics. To accelerate the online matching procedure, a typical parts based matching process, as well as a greedy strategy based matching algorithm is presented and integrated in the framework, which makes our system achieve interactive performance. We demonstrate the efficiency and effectiveness of our approach through various experiments on the prototype system

AI based geometric similarity search supporting component reuse in engineering design

Today, companies are faced with the challenge to develop and produce individualized products in the shortest possible time at very low cost in order to remain attractive under strong competitive pressure. For reasons of efficiency, products are therefore often developed in generations. Proven components are adopted in a new product generation and only some of the components are newly developed to meet new customer requirements. Many companies, therefore, have a large database of 3D CAD product models containing years of engineering experience. Nevertheless, it is often difficult to execute database queries to find which products or components already exist and could be reused or adapted for a new product generation or variant. As a result, many duplicates are created, which are associated with high effort and costs, and the risk of introducing design errors increases. Therefore, the aim of this paper is to develop an automated approach for geometric similarity search that also takes company-specific features of components into account. Machine learning methods are capable of automatically extracting relevant geometric features by learning a suitable representation of the corresponding 3D object. For this purpose, an autoencoder is developed which is trained to extract class-specific feature vectors. To improve the representativeness of those vectors for the similarity search, the architecture and hyperparameters of the autoencoder are optimized based on several experiments. Considering a real use case with a data set from the field of mechanical engineering, it is shown that geometrically similar CAD models can be found very quickly using the learned representation, and that better results are obtained than with conventional methods based on meta information, e.g. volume and bounding box. On the one hand, the fast finding of similar models encourages the reuse of existing solutions. On the other hand, standardization and, thus, economy of scale is promoted

Towards a priori mesh quality estimation using Machine Learning Techniques

Since the quality of FE meshes strongly affects the quality of the FE simulations, it is known to be very important to generate good quality meshes. Thus, it is crucial to be able to estimate very early what can be the expected quality of a mesh without having to play in loop with several control parameters. This paper addresses the way the quality of FE meshes can be estimated a priori, i.e. before meshing the CAD models. In this way, designers can generate good quality meshes at first glance. Our approach is based on the use of a set of rules which allow estimating what will be the mesh quality according to the shape characteristics of the CAD model to be meshed. Those rules are built using Machine Learning Techniques, notably classification ones, which analyse a huge amount of configurations for which the shape characteristics of both the CAD models and meshes are known. For an unknown configuration, i.e. for a CAD model not yet meshed, the learnt rules help understanding what can be the expected classes of quality, or in another way what are the control parameters to be set up to reach a given mesh quality. The proposed approach has been implemented and tested on academic and industrial examples

Integrating case based reasoning and geographic information systems in a planing support system: Çeşme Peninsula study

Thesis (Doctoral)--Izmir Institute of Technology, City and Regional Planning, Izmir, 2009Includes bibliographical references (leaves: 110-121)Text in English; Abstract: Turkish and Englishxii, 140 leavesUrban and regional planning is experiencing fundamental changes on the use of of computer-based models in planning practice and education. However, with this increased use, .Geographic Information Systems. (GIS) or .Computer Aided Design.(CAD) alone cannot serve all of the needs of planning. Computational approaches should be modified to deal better with the imperatives of contemporary planning by using artificial intelligence techniques in city planning process.The main aim of this study is to develop an integrated .Planning Support System. (PSS) tool for supporting the planning process. In this research, .Case Based Reasoning. (CBR) .an artificial intelligence technique- and .Geographic Information Systems. (GIS) .geographic analysis, data management and visualization techniqueare used as a major PSS tools to build a .Case Based System. (CBS) for knowledge representation on an operational study. Other targets of the research are to discuss the benefits of CBR method in city planning domain and to demonstrate the feasibility and usefulness of this technique in a PSS. .Çeşme Peninsula. case study which applied under the desired methodology is presented as an experimental and operational stage of the thesis.This dissertation tried to find out whether an integrated model which employing CBR&GIS could support human decision making in a city planning task. While the CBS model met many of predefined goals of the thesis, both advantages and limitations have been realized from findings when applied to the complex domain such as city planning