Monday , July 16 2018

Application of MCDM and BIM for Evaluation of
Asset Redevelopment Solutions

Department of Construction Technology and Management,
Faculty of Civil Engineering, Vilnius Gediminas Technical University,
Saulėtekio al. 11, LT-10223 Vilnius, Lithuania,,

* Corresponding author

Abstract: The current paper analyses the application of Multiple Criteria Decision Making (MCDM) and Building Information Modelling (BIM) techniques for integrated information management when assessing redevelopment solutions of former industrial buildings with emphasis on sustainable development. The theoretical approach of complex decision-making model for asset redevelopment is proposed and practical case study of old equipment factory redevelopment is presented. Sixteen criteria combined in three groups as economic, environmental and technological indicators are analysed both with ranking of possible redevelopment alternatives of the building using Weighted Aggregated Sum Product Assessment method with grey attributes scores (WASPAS-G). The selection of the most rational redevelopment solution for analysed projects is also supported by BIM techniques and allows implementation of whole project life-cycle management strategy in real life.

Keywords: information management, sustainable asset redevelopment, MCDM, WASPAS-G, BIM, building life cycle.

>>Full text
Application of MCDM and BIM for Evaluation of Asset Redevelopment Solutions, Studies in Informatics and Control, ISSN 1220-1766, vol. 25(3), pp. 293-302, 2016.

1. Introduction

Reliable techniques are necessary to manage construction project information flows as well as to reduce uncertainties and risks when solving technological and economic challenges in construction with effective decision support [2, 12, 24]. Analysing possibilities of application of automated design and information management [17], the need for delivering mutual information in a timely manner between all the responsible and competent participants, data archiving and working in a common area with a unified computer-aided design system tools is emphasized [1, 26, 27].

Building Information Modelling (BIM) is now globally considered to be a universal digital technology which is argued to have the potential to revolutionise information management in construction industry [1, 20]. A number of studies about researches in the area as well as BIM implementation can be found, but it is dominated by application to new buildings, while conversion of existing assets or reconstruction not yet widely supported [4, 27]. However, with regard to progressive ideas of sustainable development and sustainable construction, more attention should be given to redevelopment of abandoned assets instead of expanding new urban areas and erecting new buildings. Much attention is paid to redevelopment of urban areas, including problematic issues to be addressed, analysis of potential solutions and their social and economic efficiency justification [16].

It is claimed, that due to successful redevelopment the assets become more attractive to live or to invest [6, 14], produce economic and environmental savings [10, 18]. However, proper methods and tools are important to manage the information and to take the most effective solution [8, 13].

The solutions can be supported by different digital construction techniques [2, 17], but BIM can be considered to be the best technique ensuring the quality of project [1, 21, 26]. It is applicable in any project lifecycle phase and involves a lot of benefits both for new construction and existing assets [20, 27] to ensure project information and quality control.

Every solution needs to be evaluated in regard to a lot of technical, economic, social and environmental indicators, such as physical condition of load-bearing structures, historical and architectural value of the building or area, infrastructure, potential contamination of the territory, carbon emissions [13, 34]. Strategic solutions regarding redevelopment of existing assets usually should be evaluated with respect to sustainability, building heritage and modern demands [3, 5, 15, 23]. Therefore Multiple Criteria Decision Making (MCDM) can be useful for handling numerous information and for decision support [11, 28, 29].

Uncertainty of information and risk level are the largest in the beginning of construction project, while their influence decline to an acceptable level in the elaboration of the information in the course of the project, i.e. adjusting the data over the entire building lifecycle. Therefore, the most important is to manage and control the information at the beginning of a project and to select the most effective solution.

Accordingly, the aim of the current research is to suggest application of MCDM and BIM techniques for integrated information management, when selecting and implementing the most effective sustainable asset redevelopment solution. Integrated decision-making model, emphasizing interconnections between the techniques, is presented in Chapter 2. Due to high level of uncertainty, using Weighted Aggregated Sum Product Assessment method with grey numbers is suggested. A case study of abandoned industrial building redevelopment, involving 3D modelling and multiple criteria evaluation, is presented in Chapter 3.

The outcomes of the study revealed that the suggested information management and decision-making model is a reliable technique for project selection and management in uncertain environment. The objectives of the study were effectively attained by determining the priority order of the analysed asset redevelopment solutions and making it possible to implement the selected project using Digital Construction techniques.


  1. ABANDA, F. H., C. VIDALAKIS, A. H. OTI, J. H. M. TAH, A Critical Analysis of Building Information Modelling Systems used in Construction Projects, Advances in Engineering Software, vol. 90, 2015, 183-201.
  2. ARASHPOUR, M., R. WAKEFIELD, E. W. M. LEE, R. CHAN, M. REZA HOSSEINI, Analysis of Interacting Uncertainties in On-site and Off-site Activities: Implications for Hybrid Construction, Intl. Journal of Project Management, in press, 2016, pp. 1-10.
  3. ASSIEGO DE LARRIVA, R., G. RODRÍGUEZ, J. LÓPEZ, M. RAUGEI, P. PALMER, A Decision-making LCA for Energy Refurbishment of Buildings: Conditions of Comfort, Energy and Buildings, vol. 70, 2014, pp. 333-342.
  4. BARAZZETTI, L., F. BANFI, R. BRUMANA, G. GUSMEROLI, M. PREVITALI, G. SCHIANTARELLI, Cloud-to-BIM-to-FEM: Structural Simulation with Accurate Historic BIM from Laser Scans, Sim. Model. Practice and Theory, vol. 57, 2015, pp. 71-87.
  5. BLAGOJEVIC, M. R., A. TUFEGDZIC, The New Technology Era Requirements and Sustainable Approach to Industrial Heritage Renewal, Energy and Buildings, vol. 115, 2016, pp. 148-153.
  6. CHAN, A., E. CHEUNG, I. WONG, Revitalizing Industrial Buildings in Hong Kong – A Case Review, Sustainable Cities and Society, vol. 15, 2015, pp. 57-63.
  7. CHEN, M. F., G. H. TZENG, Combining Grey Relation and TOPSIS Concepts for Selecting an Expatriate Host Country, Mathematical and Computer Modelling, vol. 40(13), 2004, pp. 1473-1490.
  1. CHENG, J. C. P., L. Y. H. MA, A BIM-based System for Demolition and Renovation Waste Estimation and Planning, Waste Management, vol. 33(6), 2013, pp. 1539-1551.
  2. DENG, J., Control Problems and Grey Systems, Systems and Control Letters, vol. 1(5), 1982, pp. 288-294.
  3. FERREIRA, J., M. DUARTE PINHEIRO, J. DE BRITO, Economic and Environmental Savings of Structural Buildings Refurbishment with Demolition and Reconstruction – A Portuguese Benchmarking, J. of Building Engineering, vol. 3, 2015, pp. 114-126.
  4. FILIP, F. G., Decision Support and Control for Large-scale Complex Systems, Annual Reviews in Control, vol. 32(1), 2008, pp. 61-70.
  5. FILIP, F. G., A. M. SUDUC, M. BîZOI, DSS in Numbers, Technological and Economic Development of Economy, 20(1), 2014, pp. 154-164.
  6. GASPAR, L. P., A. L. SANTOS, Embodied Energy on Refurbishment vs. Demolition: a Southern Europe Case Study, Energy and Buildings, vol. 87, 2015, pp. 386-394.
  7. HAMNETT, C., City Centre Gentrification: Loft Conversions in London’s City Fringe, Urban Policy and Research, vol. 27(3), 2009, pp. 277-287.
  8. ILTER, D., E. ERGEN, BIM for Building Refurbishment and Maintenance: Current Status and Research Directions, Structural Survey, Journal of Building Pathology and Refurbishment, vol. 33(3), 2015, pp. 228-256.
  9. KRUTILOVA, O., I. P. AVILOVA, Osobenosti ekonomiceskoi i socialnoi effektivnosti investicii v loft-proekty, SWorld, Moscow, 2014 (in Russian).
  10. KULAHCIOGLU, T., J. DANG, C. TOKLU, A 3D Analyzer for BIM-enabled Life Cycle Assessment of the Whole Process of Construction, HVAC&R Research, vol. 18(1-2), 2012, pp. 283-293.
  11. LI, D. Z., H. X. CHEN, E. C. M. HUI, J. B. ZHANG, Q. M. LI,   A Methodology for Estimating the Life-cycle Carbon Efficiency of a Residential Building, Build. & Envir., vol. 59, 2013, pp. 448-455.
  12. LIU, S., Y. LIN, Grey Systems: Theory and Applications, Springer-Verlagh, 2010, Berlin, Heidelberg.
  13. MIGILINSKAS, D., V. POPOV, V. JUOCEVICIUS, L. USTINOVICHIUS, The Benefits, Obstacles and Problems of Practical BIM Implementation, Procedia Engineering, vol. 57, 2013, pp. 767-774.
  14. MURPHY, M., E. MCGOVERN, S. PAVIA, Historic Building Information Modelling – Adding Intelligence to Laser and Image based Surveys of European Classical Architecture, ISPRS Journal of Photogrammetry and Remote Sensing, vol. 76, 2013, pp. 89-102.
  15. Popov, V., V. Juocevicius, D. Migilinskas, L. Ustinovichius, S. Mikalauskas, The Use of a Virtual Building Design and Construction Model for Developing an Effective Project Concept in 5D Environment, Autom. in Constr., vol. 19(3), 2010, pp. 357-367.
  16. SIOZINYTE, E., J. ANTUCHEVICIENE, V. KUTUT, Upgrading the Old Vernacular Building to Contemporary Norms: Multiple Criteria Approach, J. of Civil Engineering and Management, vol. 20(2), 2014, pp. 291-298.
  17. ŞTEFĂNOIU, D., P. BORNE, D. POPESCU, F. G. FILIP, A. EL KAMEL, Optimization in Engineering Sciences Metaheuristics, Stochastic Methods and Decision Support, Wiley, London, 2014.
  18. TURSKIS, Z., E. K. ZAVADSKAS, J. ANTUCHEVICIENE, N. KOSAREVA, A Hybrid Model based on Fuzzy AHP and Fuzzy WASPAS for Construction Site Selection, International Journal of Computers, Communications & Control, vol. 10(6), 2015, pp. 873-888.
  19. USTINOVIČIUS, L., V. POPOV, D. MIGILINSKAS, Automated Management, Modeling and Choosing of Economically Effective Variant in Construction, Transport and Telecommunications, vol. 6(1), 2005, pp. 183-189.
  20. VOLK, R., J. STENGEL, F. SCHULTMANN, Building Information Modeling (BIM) for Existing Buildings – Literature Review and Future Needs, Autom. in Constr., vol. 38, 2014, pp. 109-127.
  21. ZAVADSKAS, E. K., J. ANTUCHEVICIENE, H. R., HAJIAGHA, S. S. HASHEMI, Extension of Weighted Aggregated Sum Product Assessment with Interval-valued Intuitionistic Fuzzy Numbers (WASPAS-IVIF), Applied Soft Computing, vol. 24, 2014, pp. 1013-1021.
  22. ZAVADSKAS, E. K., J. ANTUCHEVICIENE, Modelling Multidimensional Redevelopment of Derelict Buildings, International Journal of Environment and Pollution, vol. 35(2-4), 2008, pp. 331-344.
  23. ZAVADSKAS, E. K., KAKLAUSKAS, A., TURSKIS, Z., TAMOSAITIENE, J., Multi-Attribute Decision-Making Model by Applying Grey Numbers, Informatica, vol. 20, 2009, pp. 305-320.
  24. ZAVADSKAS, E. K., Z. TURSKIS, J. ANTUCHEVICIENE, Selecting a Contractor by Using a Novel Method for Multiple Attribute Analysis: Weighted Aggregated Sum Product Assessment with grey values (WASPAS-G), Studies in Informatics and Control, vol. 24(2), 2015. pp. 141-150.
  25. ZAVADSKAS, E. K., Z. TURSKIS, J. ANTUCHEVICIENE, A. ZAKAREVICIUS, Optimization of Weighted Aggregated Sum Product Assessment, Electronics and Electrical Engineering = Elektronika ir Elektrotechnika, vol. 122(6), 2012, pp. 3-6.
  26. ZAVADSKAS, E. K., VILUTIENE, T., TURSKIS, Z., TAMOSAITIENE, J., Contractor Selection for Construction Works by Applying SAW-G and TOPSIS Grey Techniques, Journal of Business Economics and Management, vol. 11, 2010, pp. 34-
  27. ZHANG, X., F. WANG, Life-cycle Assessment and Control Measures for Carbon Emissions of Typical Buildings in China, Building and Environment, vol. 86, 2015, pp. 89-97.