A MULTI-CRITERIA DECISION-MAKING SYNTHESIS METHOD TO DETERMINE THE MOST EFFECTIVE OPTION FOR MODERNISING A PUBLIC BUILDING

The study presents a sustainable building modernisation model that uses knowledgebased decision-making methods to general reconstruct old public buildings, intending to achieve the best level of energy use on the design scene. The rapid development and dissemination of standards cause multiple research opportunities in the fields of process automation and adaptation of BIM technologies to the prerequisites of existing buildings. Decision-making was widely supported by imitating structures used in the late stages of design. However, its application is not sufficient at the beginning, which affects design solutions with a significant impact on the performance of the completed building. Construction design is a multifaceted discipline where architects, engineers, contractors, and builders influence design decisions. This modernisation way uses digital systems and simulations to estimate the expected energy consumption of construction faster and economically. BIM and critical characteristics are the basis of the model, where design and general processing needs to follow to pre-built instructions. This solution allows estimating energy demand in reconstructed buildings and correlation of parameters.

[1]  Xiangyu Wang,et al.  A mixed review of the adoption of Building Information Modelling (BIM) for sustainability , 2017 .

[2]  Thomas L. Saaty Fundamentals of decision making and priority theory , 2000 .

[3]  Zeshui Xu,et al.  Multi-attribute decision making methods based on reference ideal theory with probabilistic hesitant information , 2019, Expert Syst. Appl..

[4]  Timothy L. Hemsath,et al.  Energy Modeling in Architectural Design , 2017 .

[5]  E. Zavadskas,et al.  A combined compromise solution (CoCoSo) method for multi-criteria decision-making problems , 2019, Management Decision.

[6]  Nuttasit Somboonwit,et al.  Obstacles to the Automation of Building Performance Simulation: Adaptive Building Integrated Photovoltaic (BIPV) design , 2017 .

[7]  Ricardo Mateus,et al.  Optimising building sustainability assessment using BIM , 2019, Automation in Construction.

[8]  Edmundas Kazimieras Zavadskas,et al.  Design of Products with Both International and Local Perspectives based on Yin-Yang Balance Theory and Swara Method , 2013 .

[9]  Ardeshir Mahdavi,et al.  Predicting people's presence in buildings: An empirically based model performance analysis , 2015 .

[10]  Ching-Lai Hwang,et al.  Methods for Multiple Attribute Decision Making , 1981 .

[11]  B. Melnikas,et al.  Operationalising Responsible Research and Innovation – tools for enterprises , 2019, Engineering Management in Production and Services.

[12]  Zenonas Turskis,et al.  Integrated evaluation of external wall insulation in residential buildings using SWARA-TODIM MCDM method , 2014 .

[13]  L. Ustinovicius,et al.  A New Approach to Assessing the Biases of Decisions based on Multiple Attribute Decision making Methods , 2012 .

[14]  Youngsoo Jung,et al.  Building information modelling (BIM) framework for practical implementation , 2011 .

[15]  Seung-Yeon Choo,et al.  MODEL STUDY OF DESIGN COMPONENTS FOR ENERGY-PERFORMANCE-BASED ARCHITECTURAL DESIGN USING BIM LOD 100 , 2015 .

[16]  David Bryde,et al.  The project benefits of Building Information Modelling (BIM) , 2013 .

[17]  Liyuan Wei,et al.  Visualizing Sustainability Research in Business and Management (1990–2019) and Emerging Topics: A Large-Scale Bibliometric Analysis , 2019, Sustainability.

[18]  Zenonas Turskis,et al.  EVALUATION OF THE EXPEDIENCY OF TECHNOLOGY COMMERCIALIZATION: A CASE OF INFORMATION TECHNOLOGY AND BIOTECHNOLOGY , 2020, Technological and Economic Development of Economy.

[19]  Edmundas Kazimieras Zavadskas,et al.  Selection of low-e windows in retrofit of public buildings by applying multiple criteria method COPRAS: A Lithuanian case , 2006 .

[20]  Kannan Govindan,et al.  Sustainable material selection for construction industry – A hybrid multi criteria decision making approach , 2016 .

[21]  Burcu Akinci,et al.  Analysis of modeling effort and impact of different levels of detail in building information models , 2011 .

[22]  Vygantas Žėkas,et al.  A quantitative evaluation of theoretical renewable energy potential of the building site , 2014 .

[23]  Arvind R. Singh,et al.  A review of multi criteria decision making (MCDM) towards sustainable renewable energy development , 2017 .

[24]  İhsan Kaya,et al.  Use of MCDM techniques for energy policy and decision‐making problems: A review , 2018 .

[25]  J. Bloemhof-Ruwaard,et al.  Multi-criteria decision making approaches for green supply chains: a review , 2016, Flexible Services and Manufacturing Journal.

[26]  Edmundas Kazimieras Zavadskas,et al.  An approach to multi‐attribute assessment of indoor environment before and after refurbishment of dwellings , 2009 .

[27]  Salman Azhar,et al.  Building Information Modeling (BIM): Trends, Benefits, Risks, and Challenges for the AEC Industry , 2011 .

[28]  Shahryar Habibi Micro-climatization and real-time digitalization effects on energy efficiency based on user behavior , 2017 .

[29]  Edmundas Kazimieras Zavadskas,et al.  A Model Based on Aras-G and AHP Methods for Multiple Criteria Prioritizing of Heritage Value , 2013, Int. J. Inf. Technol. Decis. Mak..

[30]  Zenonas Turskis,et al.  NON-COOPERATIVE TWO-ECHELON SUPPLY CHAINS WITH A FOCUS ON SOCIAL RESPONSIBILITY , 2019, Technological and Economic Development of Economy.

[31]  Edmundas Kazimieras Zavadskas,et al.  Importance of occupancy information when simulating energy demand of energy efficient house: A case study , 2015 .

[32]  Edmundas Kazimieras Zavadskas,et al.  Scheme for Statistical Analysis of Some Parametric Normalization Classes , 2018, Int. J. Comput. Commun. Control.

[33]  K R MacCrimmon,et al.  Decisionmaking among Multiple-Attribute Alternatives: A Survey and Consolidated Approach , 1968 .

[34]  Henrik Linderoth,et al.  Understanding adoption and use of BIM as the creation of actor networks , 2010 .

[35]  Zhenhua Zhu,et al.  Interoperability from building design to building energy modeling , 2015 .

[36]  Jakob Brinkø Berg,et al.  10 questions concerning sustainable building renovation , 2018, Building and Environment.

[37]  Rafaela Bortolini,et al.  Facility managers’ perceptions on building performance assessment , 2018 .

[38]  H. Kreinera,et al.  A new systemic approach to improve the sustainability performance of office buildings in the early design stage , 2016 .

[39]  Kerry London,et al.  Understanding and facilitating BIM adoption in the AEC industry , 2010 .

[40]  Jérôme Frisch,et al.  Model View Definition for Advanced Building Energy Performance Simulation , 2016 .

[41]  Rodney Anthony Stewart,et al.  Achieving energy efficiency in government buildings through mandatory policy and program enforcement , 2017 .

[42]  Cezary Winkowski Classification of forecasting methods in production engineering , 2019 .

[43]  Deepak Sharma,et al.  An Overview of Multi-Criteria Decision-Making Methods in Dealing with Sustainable Energy Development Issues , 2018, Energies.

[44]  Leonas Ustinovichius,et al.  A New Synthesis Method Of Structural, Technological And Safety Decisions (SyMAD-3) , 2012 .

[45]  Kenneth T. Sullivan,et al.  How To Measure the Benefits of BIM - A Case Study Approach , 2012 .

[46]  Mohamed Al-Hussein,et al.  Building information modelling for off-site construction: Review and future directions , 2019, Automation in Construction.

[47]  Hojjat Adeli,et al.  Self-constructing wavelet neural network algorithm for nonlinear control of large structures , 2015, Eng. Appl. Artif. Intell..

[48]  Mohammad Khalilzadeh,et al.  Ranking and selecting the best performance appraisal method using the MULTIMOORA approach integrated Shannon’s entropy , 2018 .

[49]  Jurgita Antucheviciene,et al.  Hybrid multiple criteria decision-making methods: a review of applications for sustainability issues , 2016 .

[50]  Kerry London,et al.  Building information modelling for facility management: are we there yet? , 2017 .

[51]  Christoph Merschbrock,et al.  Circumventing obstacles in digital construction design - a workaround theory perspective , 2015 .

[52]  Edmundas Kazimieras Zavadskas,et al.  Housing credit access model: The case for Lithuania , 2004, Eur. J. Oper. Res..

[53]  Ming Hu,et al.  Does zero energy building cost more? – An empirical comparison of the construction costs for zero energy education building in United States , 2019, Sustainable Cities and Society.

[54]  Hugo Rodrigues,et al.  Building life cycle applied to refurbishment of a traditional building from Oporto, Portugal , 2018 .

[55]  Jurgita Antucheviciene,et al.  Measuring Performance in Transportation Companies in Developing Countries: A Novel Rough ARAS Model , 2018, Symmetry.

[56]  Abdullah Cemil Ilce,et al.  AN INTEGRATED INTELLIGENT SYSTEM FOR CONSTRUCTION INDUSTRY: A CASE STUDY OF RAISED FLOOR MATERIAL , 2018, Technological and Economic Development of Economy.

[57]  T. N. Goh,et al.  Some practical considerations in the design of manufacturing process experiments , 1989 .

[58]  Aliakbar Kamari,et al.  Constraint-based renovation design support through the renovation domain model , 2019, Automation in Construction.

[59]  Frank Schultmann,et al.  Building Information Modeling (BIM) for existing buildings — Literature review and future needs , 2014 .

[60]  Benachir Medjdoub,et al.  Big Data to support sustainable urban energy planning: The EvoEnergy project , 2020, Frontiers of Engineering Management.

[61]  Cory Searcy,et al.  Measuring social issues in sustainable supply chains , 2015 .

[62]  Jurgita Antucheviciene,et al.  HYBRID MULTIPLE CRITERIA DECISION MAKING METHODS: A REVIEW OF APPLICATIONS IN ENGINEERING , 2016 .

[63]  Raphaël Couturier,et al.  Health risk assessment and decision-making for patient monitoring and decision-support using Wireless Body Sensor Networks , 2019, Inf. Fusion.