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Wu, B.; Maalek, R. Smart Decision-Support in Aging Buildings. Encyclopedia. Available online: https://encyclopedia.pub/entry/48469 (accessed on 27 July 2024).
Wu B, Maalek R. Smart Decision-Support in Aging Buildings. Encyclopedia. Available at: https://encyclopedia.pub/entry/48469. Accessed July 27, 2024.
Wu, Bin, Reza Maalek. "Smart Decision-Support in Aging Buildings" Encyclopedia, https://encyclopedia.pub/entry/48469 (accessed July 27, 2024).
Wu, B., & Maalek, R. (2023, August 24). Smart Decision-Support in Aging Buildings. In Encyclopedia. https://encyclopedia.pub/entry/48469
Wu, Bin and Reza Maalek. "Smart Decision-Support in Aging Buildings." Encyclopedia. Web. 24 August, 2023.
Smart Decision-Support in Aging Buildings
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This entry is adapted from the peer-reviewed paper 10.3390/smartcities6040089

The framework integrated digital technological advancements, such as building information modeling (BIM), point clouds processing with field information modeling (FIM)®, and structural optimization, together with lifecycle assessment to evaluate and rate the environmental impact of different solutions in aging buildings. Three sustainability aspects, namely, cost, energy consumption, and carbon emissions, were quantitatively evaluated and compared in two scenarios, namely, renovation, and demolition or deconstruction combined with redevelopment. Through a case study in Germany, it was demonstrated that the proposed framework can support decision-makers in selecting the optimal rehabilitation strategy in aging buildings.

sustainability building information modeling (BIM) artificial intelligence

1. Introduction

The preservation of the structural integrity of aging and historic buildings is a crucial step towards ensuring their lasting conservation [1]. To this end, temporal destructive and non-destructive testing is performed to predict the material properties of structural components, and determine the structural response and consequentially their effective lifecycle [2][3]. In the extreme cases, a complete demolition of the structural systems, followed by a preservation of the envelop (and interior architectural components) may be the only possible option. The environmental impact of construction and demolition waste (CDW) is, however, significant. In the European Union, the construction sector produces 839 million tons of waste annually, with 281 million tons of Construction and Demolition waste (CDW), contributing to 33% of the total waste from all sectors [4]. In addition, CDW contributes to 10–30% of all landfilled waste [5]. As a result, in recent years, more attention has been focused on reusing and recycling strategies to manage CDW and reduce machine demolition and landfill. A study conducted by Marzouk and Azab [6] shows that recycling CDW reduces emissions, energy use, and global warming potential (GWP) significantly, while preserving landfill space. In addition, the increasing prices and shortages of building materials compel the construction industry to find new, affordable, and sustainable material sources. In this case, the considerable amount of CDW allows the industry to repurpose this waste into usable construction materials.

2. Challenges in BIM-Based Sustainability Assessment of Existing Buildings

Typical metrics to quantify sustainability, such as embodied energy and carbon, report the cradle-to-gate or cradle-to-grave values in unit weight of material consumed (e.g., the Inventory of Carbon and Energy (ICE) [7][8]). As such, once the inventory of material consumed in a building is determined (through quantity-surveying), the embodied energy and carbon of the whole building can be estimated. Given that BIM is the process of modeling (and linking) intelligent graphical and non-graphical data related to building construction projects within a unified model [9][10][11], the quantity of material can be automatically extracted from the BIM. In fact, most BIM software platforms, such as Revit [12], readily provide this opportunity. The BIM, however, is not always available, especially for historic and aging buildings, and hence, strategies must be devised to generate the BIM model (ideally automatically) in such projects.
The geometry of the visible structural elements of a building can be digitally documented in 3D using optical metrology tools, such as laser scanners and cameras. To generate a semantic BIM model from the acquired point clouds, the Field Information Modeling (FIM)® framework [13] can be utilized, which has been shown effective in automatic semantic BIM model generation of oil and gas pipes [14], mechanical residential pipes [15][16], reinforced concrete structures [17], and cultural heritage domes [1]. The BIM model generated using only visual digital information, such as point clouds, will contain information about building construction, boundaries and relationship between elements (e.g., partition walls, beams and building envelopes), and the quantities and types of building materials. This is a precondition for producing a material inventory.
However, in order to create a detailed material inventory for lifecycle analysis, the FIM® framework requires additional information regarding the inherent and hidden material within the visible elements of the building. These include concrete reinforcement steel, exact concrete composition, types and thickness of insulations, and many more factors. Some inherent mechanical and material properties can be identified automatically using non-destructive testing methods, such as ultrasonic pulse velocity test [18], concrete tomography [19] and ferro-scanning [20]. Others can be determined through available textual documents, such as building permits and technical specifications, and automatically determined using natural language processing [21][22]. Despite such advancements and developments, many old buildings lack up-to-date and accurate information. As such, in this study an informed assumption about the unknown internal characteristics of the building, such as the ratio of the weight of steel and concrete volume, was made (with raw data adopted from [23]).
Finally, it is worth noting that many factors influence the embodied energy and carbon depending on the type of analysis (e.g., 50-50 [7][8]), even for the same material type. For example, the embodied energy and carbon of sand imported from abroad greatly differ from these values in sand obtained through domestic sources. This study refers to the ICE database [7][8], which adopts the EU-wide standard 15,804 EPDs (i.e., Environmental Product Declarations), for embodied energy calculations, and the possible effects of idealizations have been disregarded.

3. Design Optimization for Sustainability

Generative design (GD) [24][25][26][27] is the process of utilizing artificial intelligence (AI) to generate meaningful heuristic results when either traditional methods fail, or a single solution cannot be obtained (e.g., no single solution exists that satisfies all objectives simultaneously). In such cases, many good solutions (in the case of multi-objective optimization, Pareto front) are generated to solve the optimization problem [28][29]. In terms of building design, the integration of visual programming [30], BIM and GD has been shown effective, particularly to enhance lifecycle sustainability. Some examples of successful BIM and GD integration with sustainability considerations include the construction feasibility of underground infrastructures [31]; drywall installation planning in prefabricated construction [32]; the design of connections to code regulatory standards [33]; cloud-based solutions for energy performance simulation, such as daylight analysis [34]; lay-out of steel frame components [35][36]; spatial planning for residential blocks [37] together with integration of dynamic and complex policy frameworks for obsolescence buildings [38]; and topology optimization [39][40].
Despite considerable efforts made by the academic community in the area of GD for building and process optimization, a considerable gap between academic research and industry in structural optimization exists. This discrepancy is attributed to the lack of robust intermediary and interoperable frameworks to effectively transfer the necessary information from the BIM software for generative optimization. To address this issue, a workflow for automated structural optimization using architectural designs generated through BIM-based software was proposed [41]. This approach used a BIM software package, including Revit, the Dynamo plugin [30], and Robot Structural Analysis (RSA), to merge the architectural design and structural design phases.

References

  1. Maalek, R.; Maalek, S. Automatic Recognition and Digital Documentation of Cultural Heritage Hemispherical Domes Using Images. J. Comput. Cult. Herit. 2023, 16, 1–21.
  2. Akbari, R.; Maalek, S.; Ashayeri, H. Modal Analysis and Step-by-Step Repair Operation of a Two Span Concrete Skew Bridge to Replacement of Its Elastomeric Bearings. In Bridge Maintenance, Safety, Management, Health Monitoring and Informatics, Proceedings of the 4th International Conference on Bridge Maintenance, Safety and Management, Seoul, Republic of Korea, 13–17 July 2008; CRC Press: London, UK, 2008.
  3. Maalek, S.; Akbari, R. Distribution of Demand in Single-Column-Bent Viaducts with Irregular Configuration in Longitudinal Direction. In Bridge Maintenance, Safety, Management, Health Monitoring and Informatics, Proceedings of the 4th International Conference on Bridge Maintenance, Safety and Management, Seoul, Republic of Korea, 13–17 July 2008; CRC Press: London, UK, 2008.
  4. Eurostat. Generation of Waste by Waste Category, Hazardousness and NACE Rev. 2 Activity; Eurostat: Luxembourg, 2023.
  5. Agamuthu, P. Challenges in Sustainable Management of Construction and Demolition Waste. Waste Manag. Res. 2008, 26, 491–492.
  6. Marzouk, M.; Azab, S. Environmental and Economic Impact Assessment of Construction and Demolition Waste Disposal Using System Dynamics. Resour. Conserv. Recycl. 2014, 82, 41–49.
  7. Hammond, G.; Jones, C. Embodied Carbon: The Inventory of Carbon and Energy (ICE); BSRIA: Bracknell, UK, 2011.
  8. Jones, C.; Hammond, G. Inventory of Carbon and Energy (ICE Database); University of Bath: Bath, UK, 2019.
  9. Jung, Y.; Joo, M. Building Information Modelling (BIM) Framework for Practical Implementation. Autom. Constr. 2011, 20, 126–133.
  10. Eastman, C.; Teicholz, P.; Sacks, R.; Liston, K. BIM Handbook—A Guide to Building Information Modelling for Owners, Designers, Engineers, Contractors, and Facility Managers; Jonh Wiley & Sons, Inc.: Hoboken, NJ, USA, 2018.
  11. AGC. The Contractors’ Guide to BIM; Associated General Contractors of America: Arlington, VA, USA, 2013.
  12. Autodesk. Revit IFC Manual: Detailed Instructions for Handling IFC Files; Autodesk: San Francisco, CA, USA, 2018.
  13. Maalek, R. Field Information Modeling (FIM)TM: Best Practices Using Point Clouds. Remote Sens. 2021, 13, 967.
  14. Maalek, R.; Lichti, D.D.; Walker, R.; Bhavnani, A.; Ruwanpura, J.Y. Extraction of Pipes and Flanges from Point Clouds for Automated Verification of Pre-Fabricated Modules in Oil and Gas Refinery Projects. Autom. Constr. 2019, 103, 150–167.
  15. Maalek, R.; Lichti, D.D.; Maalek, S. Towards Automatic Digital Documentation and Progress Reporting of Mechanical Construction Pipes Using Smartphones. Autom. Constr. 2021, 127, 103735.
  16. Maalek, R.; Lichti, D.D. Robust Detection of Non-Overlapping Ellipses from Points with Applications to Circular Target Extraction in Images and Cylinder Detection in Point Clouds. ISPRS J. Photogramm. Remote Sens. 2021, 176, 83–108.
  17. Maalek, R.; Lichti, D.D.; Ruwanpura, J.Y. Automatic Recognition of Common Structural Elements from Point Clouds for Automated Progress Monitoring and Dimensional Quality Control in Reinforced Concrete Construction. Remote Sens. 2019, 11, 1102.
  18. Hong, S.; Yoon, S.; Kim, J.; Lee, C.; Kim, S.; Lee, Y. Evaluation of Condition of Concrete Structures Using Ultrasonic Pulse Velocity Method. Appl. Sci. 2020, 10, 706.
  19. Słoński, M.; Schabowicz, K.; Krawczyk, E. Detection of Flaws in Concrete Using Ultrasonic Tomography and Convolutional Neural Networks. Materials 2020, 13, 1557.
  20. Korl, S.; Wuersch, C.; Zanona, J. Innovative Sensor Technologies for Nondestructive Imaging of Concrete Structures: Novel Tools Utilising Radar and Induction Technologies. In Nondestructive Testing of Materials and Structures; RILEM Bookseries; Springer: Dordrecht, The Netherlands, 2012; Volume 6, pp. 131–136.
  21. Amer, F.; Golparvar-Fard, M. Modeling Dynamic Construction Work Template from Existing Scheduling Records via Sequential Machine Learning. Adv. Eng. Inform. 2021, 47, 101198.
  22. Khurana, D.; Koli, A.; Khatter, K.; Singh, S. Natural Language Processing: State of the Art, Current Trends and Challenges. Multimed. Tools Appl. 2023, 82, 3713–3744.
  23. Hofstadler, C. Bauablaufplanung Und Logistik Im Baubetrieb; Springer: Berlin/Heidelberg, Germany, 2007.
  24. Ramu, P.; Thananjayan, P.; Acar, E.; Bayrak, G.; Park, J.W.; Lee, I. A Survey of Machine Learning Techniques in Structural and Multidisciplinary Optimization; Springer: Berlin/Heidelberg, Germany, 2022; Volume 65, ISBN 0123456789.
  25. Watson, M.; Leary, M.; Brandt, M. Generative Design of Truss Systems by the Integration of Topology and Shape Optimisation. Int. J. Adv. Manuf. Technol. 2022, 118, 1165–1182.
  26. Du, W.F.; Wang, Y.Q.; Wang, H.; Zhao, Y.N. Intelligent Generation Method for Innovative Structures of the Main Truss in a Steel Bridge. Soft Comput. 2023, 27, 5587–5601.
  27. Peng, J.; Feng, Y.; Zhang, Q.; Liu, X. Multi-Objective Integrated Optimization Study of Prefabricated Building Projects Introducing Sustainable Levels. Sci. Rep. 2023, 13, 2821.
  28. Mccormack, J.; Dorin, A.; Innocent, T. Generative Design: A Paradigm for Design Research. In Proceedings of the Futureground—DRS International Conference, Melbourne, Australia, 17–21 November 2004.
  29. Oxman, R. Thinking Difference: Theories and Models of Parametric Design Thinking. Des. Stud. 2017, 52, 4–39.
  30. Ravshanovich, K.S. REVIT+DYNAMO. Pindus J. Cult. Lit. ELT 2022, 2, 57–63. Available online: https://literature.academicjournal.io/index.php/literature/article/view/215 (accessed on 9 July 2023).
  31. Huang, M.Q.; Chen, X.L.; Ninić, J.; Bai, Y.; Zhang, Q.B. A Framework for Integrating Embodied Carbon Assessment and Construction Feasibility in Prefabricated Stations. Tunn. Undergr. Sp. Technol. 2023, 132, 104920.
  32. Cuellar Lobo, J.D.; Lei, Z.; Liu, H.; Li, H.X.; Han, S. Building Information Modelling- (BIM-) Based Generative Design for Drywall Installation Planning in Prefabricated Construction. Adv. Civ. Eng. 2021, 2021, 6638236.
  33. Henríquez, D.; Herrera, R.F.; Vielma, J.C. Method for Designing Prequalified Connections Using Generative Design. Buildings 2022, 12, 1579.
  34. Asl, M.R.; Bergin, M.; Menter, A.; Yan, W. BIM-Based Parametric Building Energy Performance Multi-Objective Optimization. In Proceedings of the International Conference on Education and Research in Computer Aided Architectural Design in Europe, Newcastle, UK, 10–12 September 2014; Volume 2.
  35. Fu, B.; Gao, Y.; Wang, W. Dual Generative Adversarial Networks for Automated Component Layout Design of Steel Frame-Brace Structures. Autom. Constr. 2023, 146, 104661.
  36. Elsayed, A.M.; Elanwar, H.H.; Marzouk, M.; Safar, S.S. Sustainable Layout Design of Steel Buildings through Embodied Energy and Costs Optimization. Clean. Eng. Technol. 2021, 5, 100308.
  37. Mukkavaara, J.; Sandberg, M. Architectural Design Exploration Using Generative Design: Framework Development and Case Study of a Residential Block. Buildings 2020, 10, 201.
  38. Buitelaar, E.; Moroni, S.; De Franco, A. Building Obsolescence in the Evolving City. Reframing Property Vacancy and Abandonment in the Light of Urban Dynamics and Complexity. Cities 2021, 108, 102964.
  39. Rong, Y.; Zhao, Z.L.; Feng, X.Q.; Xie, Y.M. Structural Topology Optimization with an Adaptive Design Domain. Comput. Methods Appl. Mech. Eng. 2022, 389, 114382.
  40. Ching, E.; Carstensen, J.V. Truss Topology Optimization of Timber–Steel Structures for Reduced Embodied Carbon Design. Eng. Struct. 2022, 252, 113540.
  41. Hamidavi, T.; Abrishami, S.; Hosseini, M.R. Towards Intelligent Structural Design of Buildings: A BIM-Based Solution. J. Build. Eng. 2020, 32, 101685.
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Subjects: Construction & Building Technology; Engineering, Civil
Contributors MDPI registered users' name will be linked to their SciProfiles pages. To register with us, please refer to https://encyclopedia.pub/register :
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