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  • A Recommender System to Assist the SemanticEnrichment of IFC models

    Diana Gouveia Ribeiro

    Thesis to obtain the Master of Science Degree in

    Information Systems and Computer Engineering

    Supervisor: Prof. Paulo Jorge Fernandes Carreira

    Examination Committee

    Chairperson: Jos Manuel da Costa Alves MarquesSupervisor: Prof. Paulo Jorge Fernandes Carreira

    Member of the Committee: Jos Alberto Rodrigues Pereira Sardinha

    November 2016

  • Strive not to be a success, but rather to be of value.Albert Einstein

  • Acknowledgments

    First of all, I would like to thank my parents for all the support and motivation during my academic

    journey. Without them, none of it would have been possible. I dedicate this work to them.

    I would like to thank Professor Paulo Carreira and Professor Antnio Aguiar Costa, for the support,

    the guidance to achieve this work and for believing in me and this works potential.

    I would also like to thank my boyfriend, Ricardo Paradela, for the unconditional support and motiva-

    tion along the duration of this work.

    Last, but not least, I would like to thank my friends for their support in times of need.

    iii

  • Abstract

    Activity planning, in construction projects is time consuming and error prone. This task is one among

    many that is a practical application of performing semantic enrichment in IFC models. Planning practice

    demonstrates that a large number of the activities assigned to a project are determined by the projects

    construction elements and profile. Considering that, ideally there should be a way of automatically

    deriving the corresponding activities for a construction project. The main hindrance to automation is that

    the assignment of activities to the same building elements displays high variability and relies to a great

    extent on tacit knowledge of the project planner regarding the project. This work aims at implementing

    and validating a system that performs automatic suggestion of activities given an IFC model of the

    building. Our solution harvests expert knowledge through a recommendation algorithm that collects

    data from previous projects, and learn when users review the assignments, add missing ones or identify

    incorrect ones. The validation of this solution shows that the time used in activity planning is reduced.

    Keywords

    BIM, IFC, Semantic Enrichment, Recommendation Algorithms, Omniclass

    v

  • Resumo

    O planeamento de actividades em projectos de construo demorado e propenso a erros. Esta

    tarefa uma das muitas aplicaes prticas do enriquecimento semntico de modelos IFC. A prtica

    do planeamento de actividades mostra que um grande nmero das actividades atribudas a um projecto

    so determinadas pelos elementos de construo e perfil do projecto. Posto isto, idealmente deveria

    haver uma forma automtica de inferir as actividades correspondendes a um projecto de construo.

    O maior problema da automao que a atribuio de actividades para os mesmos elements de con-

    struo apresenta um grande nvel de variabilidade, e depende em grande parte no conhecimento do

    planeador do projecto em relao ao mesmo. Este trabalho tem como objectivo implementar e validar

    um sistema que realiza sugestes automticas de actividades dado um modelo IFC de um projecto de

    construo. Esta soluo recolhe conhecimento especializado atravs de um algoritmo de recomen-

    dao que colecciona dados de projectos anteriores, e aprende quando os utilizadores validam as

    atribuies, adicionam as que faltam ou corrigem as que esto incorrectas.

    Palavras Chave

    BIM, IFC, Enriquecimento Semntico, Algoritmos de Recomendao, Omniclass

    vii

  • Contents

    1 Introduction 1

    1.1 Motivation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3

    1.2 Methodology and Contributions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3

    1.3 Document Organization . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4

    2 Concepts 5

    2.1 Building Information Modelling . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6

    2.1.1 BIM Tools, Platforms and Environments . . . . . . . . . . . . . . . . . . . . . . . . 6

    2.1.2 BIM Schemas . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7

    2.1.3 Model View Definitions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7

    2.1.4 Exchange Formats . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7

    2.1.5 Industry Foundation Classes . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8

    2.1.6 COBie . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9

    2.2 Omniclass Classification System . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10

    2.2.1 OmniClass Tables . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11

    2.2.2 Uniclass . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11

    2.3 Ontologies . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12

    2.4 Recommendation Algorithms . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13

    2.4.1 Association Rules . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13

    2.4.2 K-Nearest Neighbours . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13

    2.4.3 K-Means Clustering . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 14

    2.4.4 Evaluation of recommendation algorithms . . . . . . . . . . . . . . . . . . . . . . . 14

    3 Related Work 17

    3.1 Building Information Models . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 18

    3.1.0.A SeeBIM for semantic enrichment . . . . . . . . . . . . . . . . . . . . . . . 18

    3.1.0.B Object Profile Manager for semantic interoperability . . . . . . . . . . . . 19

    3.2 Recommender Systems . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 19

    3.2.0.C Amazon.coms Item-to-item collaborative filtering . . . . . . . . . . . . . . 20

    3.2.0.D eBay.coms Feedback Profile . . . . . . . . . . . . . . . . . . . . . . . . . 20

    3.2.0.E MovieFinder.coms Match Maker . . . . . . . . . . . . . . . . . . . . . . . 20

    3.2.0.F Reel.coms Movie Map . . . . . . . . . . . . . . . . . . . . . . . . . . . . 20

    ix

  • 3.2.0.G HeyStaks . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 21

    3.2.0.H BibSonomy . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 21

    3.3 Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 22

    4 Solution 25

    4.0.1 Solution Overview . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 26

    4.1 Requirements . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 26

    4.1.1 Use Case . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 27

    4.1.2 Conceptual Overview . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 27

    4.1.2.A The relevant information from IFC . . . . . . . . . . . . . . . . . . . . . . 27

    4.1.2.B Formal definition of the recommendation algorithm . . . . . . . . . . . . . 28

    4.2 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 29

    5 Implementation 31

    5.1 Data Model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 32

    5.2 Architecture . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 32

    5.2.1 The Model View Controller Pattern . . . . . . . . . . . . . . . . . . . . . . . . . . . 32

    5.2.2 Data and Domain Layers . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 32

    5.2.3 Service Layer . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 35

    5.3 Application Layer . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 35

    5.3.1 Distributed Architecture . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 36

    5.4 Main features . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 37

    5.4.1 User profile and project creation . . . . . . . . . . . . . . . . . . . . . . . . . . . . 37

    5.4.2 File upload and IFC Tools Project . . . . . . . . . . . . . . . . . . . . . . . . . . . . 37

    5.4.3 File parsers . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 38

    5.4.4 Recommendation algorithm and creation of associations . . . . . . . . . . . . . . 39

    6 Validation 41

    6.0.5 Methodology . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 42

    6.0.5.A Validating the algorithm . . . . . . . . . . . . . . . . . . . . . . . . . . . . 42

    6.0.5.B User satisfaction validation . . . . . . . . . . . . . . . . . . . . . . . . . . 42

    6.0.6 Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 42

    6.0.6.A Recommendation Algorithm . . . . . . . . . . . . . . . . . . . . . . . . . 42

    6.0.6.B User Satisfaction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 43

    7 Conclusion 45

    7.1 Future Work . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 46

    7.2 Final Remarks . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .