# Best Fit for Common Purpose: A Multi-Stakeholder Design Optimization Methodology for Construction Management

^{1}

^{2}

^{*}

## Abstract

**:**

## 1. Introduction

- How can early stage design optimization be implemented?
- How can we ensure that the design optimization properly reflects the real-world design and construction processes?
- How can we better involve stakeholders in the design optimization process?
- How should stakeholders’ preferences be measured and used within the optimization?
- How can optimization and stakeholder-driven design be integrated within a decision-making tool for construction management?

- Reflects the real-world project design and construction process where stakeholders play an essential role;
- Automates the decision-making process within building design and construction;
- Uses a mathematically correct way of preference modeling by utilizing the PFM theory;
- Is not computationally demanding, intuitive in use, and adaptive to changing stakeholders’ demands;
- Can be applied to the early stages of design and construction development.

## 2. Materials and Methods

**Step 1. Defining the design problem**

**Step 2. Finding the minima and maxima of the objective functions**

**Step 3. Defining preferences**

**Step 4. Specifying weights**

**Step 5. Setting up the optimization problem**

**Step 6. Initial population generation**

**Step 7. Preference aggregation and evaluation**

**Step 8. Next generation**

**Step 9. Selecting the best individual**

**Step 10. Results verification**

## 3. Demonstrator Case

#### 3.1. Description of the Demonstrator Case Building

^{2}) and number of floors (5). In total, three complete building models were shared with us, each characterized by different sets of materials used for the façade and the structural elements:

- A concrete frame and brick façade with Rockwool insulation;
- A structural steel profile frame and hollow concrete slabs with a steel sandwich panel façade with polyisocyanurate insulation;
- A cross-laminated timber frame with a wooden joist floor and a timber frame façade with mineral wool insulation.

- First, for each of the models, the amount of materials per floor was calculated. As each model includes two parts—a façade and a frame—separately, in total six initial calculations were made. The materials used in Autodesk Revit models were mapped to the closest possible analogues available in the LCA software OneClick LCA [56];
- The scaling of the floor area was implemented in relation to the original total gross area:
- It was assumed that the window-to-wall ratio (WWR = 0.35) and the ratio of length to width of the building (3.49) will be preserved when the floor area changes;
- The total volume of materials used in the ceiling and vertical structural elements was assumed to change proportionally to the total floor area;
- The total volume of materials used in walls was assumed to change proportionally to the perimeter length of the building whilst taking into account the scaling of the windows’ area;
- The total volume of materials used in horizontal structural elements was assumed to be proportional to the length or the width of the building (depending on the element orientation);

- To estimate the total volume of materials used in a building, first, the volume per storey was calculated based on the floor area and then the resulting value was multiplied by the number of floors;
- The resulting total volume of materials used in a building was calculated based on the floor area and the number of floors. The foundation of all building variants was assumed to be the same (500 mm thick concrete).

#### 3.2. Design Problem Specification

- Construction costs (EUR) (only includes the cost of materials, and all other costs (e.g., labor) were assumed to be constant);
- Embodied carbon emissions (kg CO
_{2}eq) (based on environmental product declarations (EPDs)); - Expected profit from renting the building out (EUR/month) (based on the total floor area of the building and the rental costs per m
^{2}); - Location (km) (characterized by the distance from the nearest railway station to a planned construction site).

#### 3.3. Optimization Problem Specification

#### 3.4. Description of the Design Process Iterations Used in the Study

**Iteration 1**

**Iteration 2**

**Iteration 3**

**Iteration 4**

**Iteration 5**

## 4. Results of the Demonstrator Case

^{2}to 1180 m

^{2}. As a consequence, the profit saw a two-fold decrease while construction costs reduced from 3.3 million EUR to 1.5 million EUR. The introduction of the third objective at Iteration 3 resulted in Distance to a railway station increasing from 0.2 km to 5 km. The floor area has seen a slight increase (by 51 m

^{2}), resulting in a minor increase in Construction costs and Profit from rent. The inclusion of Embodied carbon emissions into the model at Iteration 4 had a major effect on the optimum: the number of floors and the floor area reached their lower boundaries. In this configuration, Construction costs reached the absolute possible minimum. Embodied carbon emissions were also close to the absolute minimum. However, while the preferences related to Construction costs and Embodied carbon emissions were well-satisfied, Profit from rent decreased from the best possible value of 5.2 million EUR/month at Iteration 1 to a near-bottom value of 0.3 million EUR/month. Increasing the weight of Profit from rent at Iteration 5 resulted in it growing to 4.2 million EUR/month. The floor area increased to 2031 m

^{2}and the number of floors moved back from 2 to 10. In addition, the optimum set of the building’s structural frame materials changed from 2 (Structural steel profile frame and a hollow concrete slabs floor) to 3 (Cross-laminated timber frame with a wooden joist floor).

## 5. Discussion

#### 5.1. Limitations

#### 5.2. Steps for Further Development

- Developing the user interface and running a series of workshops with real-world stakeholders in order to obtain feedback and refine the methodology and the tool (‘Preferendus’);
- Validation of the methodology and the developed tool in design offices on more complex cases that include more objectives from different domains (expanding it to cover energy modeling, social aspects, circularity, resilience, ecosystem services provision, and more) and more complex stakeholder structures (e.g., stakeholders with overlapping preferences);
- In addition to whole-building design, implementing the methodology for more specific cases of design decision-making and specifically the selection of building materials. The selection of building materials is a challenging topic due to the overwhelming number of existing options, each promising specific advantages (e.g., traditional mass-market materials vs. circular materials vs. low-carbon materials vs. bio-based materials), and the selection process can be streamlined and simplified by implementing an automated preference-based decision-making system. In this context, the methodology will also be applied to a case of insulation material selection within the NRG-Storage Horizon 2020 project that this work is affiliated with;
- Integration of the methodology with parametric building design software (e.g., Rhino Grasshopper). Parametric design software allows us to seamlessly connect the building design variables to multiple different objectives such as energy consumption, environmental impacts, thermal comfort, and structural strength. Building a stakeholder-oriented layer on top of the existing parametric design functionality would allow us to properly account for stakeholders’ preferences and have a fully integrated design process from preferences to the final CAD model;
- Extending the methodology to include a stalemate resolver for the automated resolution of design deadlocks where the design solution space becomes over-constrained by stakeholders and no feasible solution can be found without relaxing some of the negotiable constraints (by negotiable constraints, we mean design constraints specified by stakeholders that can, in theory, be moved and modified during the design process such as the maximum budget, etc.);
- Developing the methodology to further allow for finding a compromise design solution using a preference-based goal attainment approach;
- As the core methodology presented in this study can be applied to preference-based optimization (any optimization process that should consider stakeholders’ preferences) in any context, exploring the possibility of expanding the tool to be applicable in other fields such as supply chain management, asset management, etc.

## 6. Conclusions

## Author Contributions

## Funding

## Institutional Review Board Statement

## Informed Consent Statement

## Data Availability Statement

## Acknowledgments

## Conflicts of Interest

## Appendix A. Material Composition of Frame and Façade Options

^{2}. As was mentioned above, the foundation was assumed to be the same for all design variants; so, it is provided separately in the table as well. Note that the units are different for different materials.

Material | Unit | Frame | Façade | |||||
---|---|---|---|---|---|---|---|---|

Foundation | 1—Concrete Frame | 2—Structural Steel Profile Frame and a Hollow Concrete Slabs Floor | 3—Cross-Laminated Timber Frame with a Wooden Joist Floor | 1—Brick and Rockwool Insulation | 2—Steel Sandwich Panel Façade with Polyisocyanurate Insulation | 3—Timber Frame Façade with Mineral Wool Insulation | ||

Ready-mix concrete, normal strength, generic, C 28/35 (4000/5000 PSI) with CEM I, 0% recycled binders (300 kg/m^{3}; 18.7 lbs/ft3 total cement) | m^{3} | 0 | 329.1 | 0 | 0 | 0 | 0 | 0 |

Ready-mix concrete, normal-strength, generic, C 40/50 (5800/7300 PSI), 0% recycled binders in cement (400 kg/m^{3}/24.97 lbs/ft3) | m^{3} | 500.0 | 14.7 | 0 | 0 | 0 | 0 | 0 |

Rock wool insulation, unfaced, L = 0.035–0.04 W/mK, 40 mm, 1.2 kg/m^{2}, 30 kg/m^{3}, Lambda = 0.035 W/(m.K), ProRox LF 970 (ROCKWOOL) | m^{3} | 0 | 0 | 0 | 0 | 58.2 | 0 | 0 |

Wall bricks, 575 kg/m^{3} (Ziegel) | m^{3} | 0 | 0 | 0 | 0 | 36.4 | 0 | 0 |

Hot-dip galvanised structural steel, 0.45–1.5 mm, 7850 kg/m^{3} (Ruukki) | m^{3} | 0 | 0 | 1.2 | 0 | 0 | 0 | 0 |

Structural steel profiles, generic, 90% recycled content (typical), I, H, U, L, and T sections, S235, S275 and S355 | m^{3} | 0 | 0 | 6.0 | 3.6 | 0 | 0 | 0 |

Hollow core concrete slab, HDF27, C 40/50, 270 mm, 344 kg/m^{2}, reinforcing 12.7 kg/ton (Strängbetong) | m^{2} | 0 | 0 | 945.0 | 0 | 0 | 0 | 0 |

Aluminium profile, 2700.0 kg/m^{3} | m^{3} | 0 | 0 | 0 | 0 | 0 | 0.2 | 0 |

Sandwich panel with polyisocyanurate (PIR) foam and double steel siding, U = 0.21 W/(m2K), 100.9 mm (Total), 0.5 mm (Outer sheet), 0.4 mm (Liner sheet), 100 mm (Insulation), 12.11 kg/m^{2}, 120.02 kg/m^{3}, SAB WB 100.1000 (SAB-profile, Tata Steel Europe) | m^{2} | 0 | 0 | 0 | 0 | 0 | 363.9 | 0 |

Aluminium profiled sheets, thickness 0.7 mm, height of profile 65 mm, 2.9 kg/m^{2} (European Association for Panels and Profiles) | m^{2} | 0 | 0 | 0 | 0 | 0 | 363.9 | 0 |

Cross-laminated timber, 491.65 kg/m^{3}, T:51–500 mm, max width: 2.95–4.80 m, max length: 16–20 m, X-Lam (Studiengemeinschaft Holzleimbau e.V.) | m^{3} | 0 | 0 | 0 | 88.9 | 0 | 0 | 3.1 |

Wooden joist floor assembly, 278 mm, incl. 225 mm mineral wool insulation | m^{2} | 0 | 0 | 0 | 688.1 | 0 | 0 | 0 |

Timber frame external wall assembly, incl. mineral wool insulation | m^{2} | 0 | 0 | 0 | 0 | 0 | 0 | 363.9 |

## Appendix B

#### Appendix B.1. Equations for Calculating the Volume of Materials Used in the Model

^{3}of “Ready-mix concrete, normal strength, generic, C 28/35 (4000/5000 PSI) with CEM I, 0% recycled binders (300 kg/m3; 18.7 lbs/ft3 total cement)” and 1 m

^{2}of “Timber frame external wall assembly, incl. mineral wool insulation”. Note that since the data provided in EPDs can be measured using different units, the vector contains both values measured in m

^{2}and m

^{3}.

^{2}. In further equations, it is denoted as ${a}_{0}$. The proportions of the floor area were assumed to be constant (the length-to-width ratio of the building is equal to 3.49). The equations provided below are sometimes defined through the length of the building that—given the fixed proportions—can be defined as:

#### Appendix B.2. Calculation of the Objectives

#### Appendix B.2.1. Embodied Carbon Emissions

#### Appendix B.2.2. Construction Costs

#### Appendix B.2.3. Profit from Rent

#### Appendix B.2.4. Location Objective

## References

- Kubba, S. Handbook of Green Building Design and Construction: LEED, BREEAM, and Green Globes; Butterworth-Heinemann: Oxford, UK, 2012; ISBN 978-0-12-385129-1. [Google Scholar]
- Kibert, C.J. Sustainable Construction: Green Building Design and Delivery; John Wiley & Sons: Hoboken, NJ, USA, 2016; ISBN 978-1-119-05517-4. [Google Scholar]
- Xijiang, T. Green Building Design Method Based on System Ecology. Energy Syst.
**2020**, 1–18. [Google Scholar] [CrossRef] - Mok, K.Y.; Shen, G.Q.; Yang, R. Stakeholder Complexity in Large Scale Green Building Projects: A Holistic Analysis towards a Better Understanding. Eng. Constr. Archit. Manag.
**2018**, 25, 1454–1474. [Google Scholar] [CrossRef] - Tresidder, E. Accelerated Optimisation Methods for Low-Carbon Building Design. Ph.D. Dissertation, De Montfort University, Leicester, UK, 2014. [Google Scholar]
- Barbhuiya, S.; Barbhuiya, S.; Nikraz, H. Adaptation to the Future Climate: A Low Carbon Building Design Challenge. Procedia Eng.
**2013**, 51, 194–199. [Google Scholar] [CrossRef] [Green Version] - Jankovic, L. Designing Zero Carbon Buildings: Using Dynamic Simulation Methods, 2nd ed.; Routledge: London, UK, 2017; ISBN 978-1-315-62090-9. [Google Scholar]
- Jones, P.; Hou, S.S.; Li, X. Towards Zero Carbon Design in Offices: Integrating Smart Facades, Ventilation, and Surface Heating and Cooling. Renew. Energy
**2015**, 73, 69–76. [Google Scholar] [CrossRef] [Green Version] - Sodha, M.S.; Bansal, N.K.; Bansal, P.K.; Kumar, A.; Malik, M.A.S. Solar Passive Building: Science and Design; Pergamon Press: Oxford, UK, 1986. [Google Scholar]
- Tian, Z.; Zhang, X.; Jin, X.; Zhou, X.; Si, B.; Shi, X. Towards Adoption of Building Energy Simulation and Optimization for Passive Building Design: A Survey and a Review. Energy Build.
**2018**, 158, 1306–1316. [Google Scholar] [CrossRef] - Keeler, M.; Vaidya, P. Fundamentals of Integrated Design for Sustainable Building; John Wiley & Sons: Hoboken, NJ, USA, 2016; ISBN 978-1-118-88191-0. [Google Scholar]
- Bunz, K.R.; Henze, G.P.; Tiller, D.K. Survey of Sustainable Building Design Practices in North America, Europe, and Asia. J. Archit. Eng.
**2006**, 12, 33–62. [Google Scholar] [CrossRef] - Ghaffar, S.H.; Burman, M.; Braimah, N. Pathways to Circular Construction: An Integrated Management of Construction and Demolition Waste for Resource Recovery. J. Clean. Prod.
**2020**, 244, 118710. [Google Scholar] [CrossRef] - Kanters, J. Circular Building Design: An Analysis of Barriers and Drivers for a Circular Building Sector. Buildings
**2020**, 10, 77. [Google Scholar] [CrossRef] - Alfraidi, Y.; Boussabaine, A.H. Design Resilient Building Strategies in Face of Climate Change. Int. J. Archit. Civ. Constr. Sci.
**2015**, 9, 23–28. [Google Scholar] [CrossRef] - Hosseini, M. Toward Resilient Building Design in Energy Performance under Climate Change. Ph.D. Thesis, Concordia University, Montreal, QC, Canada, 2021. [Google Scholar]
- Buckman, A.H.; Mayfield, M.; Beck, S.B.M. What Is a Smart Building? Smart Sustain. Built Environ.
**2014**, 3, 92–109. [Google Scholar] [CrossRef] - Eini, R.; Linkous, L.; Zohrabi, N.; Abdelwahed, S. A Testbed for a Smart Building: Design and Implementation. In Proceedings of the Fourth Workshop on International Science of Smart City Operations and Platforms Engineering, Montreal, QC, Canada, 15 April 2019; Association for Computing Machinery: New York, NY, USA, 2019; pp. 1–6. [Google Scholar]
- Loftness, V.; Hakkinen, B.; Adan, O.; Nevalainen, A. Elements That Contribute to Healthy Building Design. Environ. Health Perspect.
**2007**, 115, 965–970. [Google Scholar] [CrossRef] [PubMed] [Green Version] - Spengler, J.D.; Chen, Q. Indoor Air Quality Factors in Designing a Healthy Building. Annu. Rev. Energy Environ.
**2000**, 25, 567–600. [Google Scholar] [CrossRef] [Green Version] - Aapaoja, A.; Haapasalo, H.; Söderström, P. Early Stakeholder Involvement in the Project Definition Phase: Case Renovation. ISRN Ind. Eng.
**2013**, 2013, e953915. [Google Scholar] [CrossRef] [Green Version] - Huang, D.; Liu, J.; Tang, C. Stakeholders Management in Construction Project: A Case of Hydropower Station Project. In Proceedings of the 17th International Symposium on Advancement of Construction Management and Real Estate; Wang, J., Ding, Z., Zou, L., Zuo, J., Eds.; Springer: Berlin/Heidelberg, Germany, 2014; pp. 605–613. [Google Scholar]
- Chan, A.P.C.; Chan, D.W.M.; Chiang, Y.H.; Tang, B.S.; Chan, E.H.W.; Ho, K.S.K. Exploring Critical Success Factors for Partnering in Construction Projects. J. Constr. Eng. Manag.
**2004**, 130, 188–198. [Google Scholar] [CrossRef] - Nguyen, L.D.; Le-Hoai, L.; Tran, D.Q.; Dang, C.N.; Nguyen, C.V. Effect of Project Complexity on Cost and Schedule Performance in Transportation Projects. Constr. Manag. Econ.
**2019**, 37, 384–399. [Google Scholar] [CrossRef] - El Touny, A.S.; Ibrahim, A.H.; Mohamed, H.H. An Integrated Sustainable Construction Project’s Critical Success Factors (ISCSFs). Sustainability
**2021**, 13, 8629. [Google Scholar] [CrossRef] - Durdyev, S.; Hosseini, M.R. Causes of Delays on Construction Projects: A Comprehensive List. Int. J. Manag. Proj. Bus.
**2019**, 13, 20–46. [Google Scholar] [CrossRef] - Wu, G. A Multi-Objective Trade-Off Model in Sustainable Construction Projects. Sustainability
**2017**, 9, 1929. [Google Scholar] [CrossRef] [Green Version] - Ng, S.T.; Rose, T.M.; Mak, M.; Chen, S.E. Problematic Issues Associated with Project Partnering—The Contractor Perspective. Int. J. Proj. Manag.
**2002**, 20, 437–449. [Google Scholar] [CrossRef] - Eurima. Life Cycle Assessment of Buildings—A Future-Proofed Solution in the Digitalised World of Tomorrow; White paper; Eurima: Brussels, Belgium, 2017; Available online: https://www.eurima.org/uploads/files/modules/articles/1584367144_SC%20Eurima_LCA_WhitePaper_Final_20170915.pdf (accessed on 14 April 2022).
- Meex, E.; Hollberg, A.; Knapen, E.; Hildebrand, L.; Verbeeck, G. Requirements for Applying LCA-Based Environmental Impact Assessment Tools in the Early Stages of Building Design. Build. Environ.
**2018**, 133, 228–236. [Google Scholar] [CrossRef] - Machairas, V.; Tsangrassoulis, A.; Axarli, K. Algorithms for Optimization of Building Design: A Review. Renew. Sustain. Energy Rev.
**2014**, 31, 101–112. [Google Scholar] [CrossRef] - Evins, R. A Review of Computational Optimisation Methods Applied to Sustainable Building Design. Renew. Sustain. Energy Rev.
**2013**, 22, 230–245. [Google Scholar] [CrossRef] - Nguyen, A.-T.; Reiter, S.; Rigo, P. A Review on Simulation-Based Optimization Methods Applied to Building Performance Analysis. Appl. Energy
**2014**, 113, 1043–1058. [Google Scholar] [CrossRef] - Shi, X.; Tian, Z.; Chen, W.; Si, B.; Jin, X. A Review on Building Energy Efficient Design Optimization Rom the Perspective of Architects. Renew. Sustain. Energy Rev.
**2016**, 65, 872–884. [Google Scholar] [CrossRef] - Zhai, Y.; Wang, Y.; Huang, Y.; Meng, X. A Multi-Objective Optimization Methodology for Window Design Considering Energy Consumption, Thermal Environment and Visual Performance. Renew. Energy
**2019**, 134, 1190–1199. [Google Scholar] [CrossRef] - Shi, X. Design Optimization of Insulation Usage and Space Conditioning Load Using Energy Simulation and Genetic Algorithm. Energy
**2011**, 36, 1659–1667. [Google Scholar] [CrossRef] - Dino, I.G.; Üçoluk, G. Multiobjective Design Optimization of Building Space Layout, Energy, and Daylighting Performance. J. Comput. Civ. Eng.
**2017**, 31, 04017025. [Google Scholar] [CrossRef] - Zhang, L.; Zhang, L.; Wang, Y. Shape Optimization of Free-Form Buildings Based on Solar Radiation Gain and Space Efficiency Using a Multi-Objective Genetic Algorithm in the Severe Cold Zones of China. Sol. Energy
**2016**, 132, 38–50. [Google Scholar] [CrossRef] - Rachmawati, L.; Srinivasan, D. Preference Incorporation in Multi-Objective Evolutionary Algorithms: A Survey. In Proceedings of the 2006 IEEE International Conference on Evolutionary Computation, Vancouver, BC, Canada, 16–21 July 2006; pp. 962–968. [Google Scholar]
- Harkouss, F.; Fardoun, F.; Biwole, P.H. Optimal Design of Renewable Energy Solution Sets for Net Zero Energy Buildings. Energy
**2019**, 179, 1155–1175. [Google Scholar] [CrossRef] - Shao, Y.; Geyer, P.; Lang, W. Integrating Requirement Analysis and Multi-Objective Optimization for Office Building Energy Retrofit Strategies. Energy Build.
**2014**, 82, 356–368. [Google Scholar] [CrossRef] - Chaturvedi, S.; Rajasekar, E.; Natarajan, S. Multi-Objective Building Design Optimization under Operational Uncertainties Using the NSGA II Algorithm. Buildings
**2020**, 10, 88. [Google Scholar] [CrossRef] - Ascione, F.; Bianco, N.; De Stasio, C.; Mauro, G.M.; Vanoli, G.P. Simulation-Based Model Predictive Control by the Multi-Objective Optimization of Building Energy Performance and Thermal Comfort. Energy Build.
**2016**, 111, 131–144. [Google Scholar] [CrossRef] - Senel Solmaz, A.; Halicioglu, F.H.; Gunhan, S. An Approach for Making Optimal Decisions in Building Energy Efficiency Retrofit Projects. Indoor Built Environ.
**2018**, 27, 348–368. [Google Scholar] [CrossRef] - Islam, H.; Jollands, M.; Setunge, S.; Bhuiyan, M.A. Optimization Approach of Balancing Life Cycle Cost and Environmental Impacts on Residential Building Design. Energy Build.
**2015**, 87, 282–292. [Google Scholar] [CrossRef] - Bre, F.; Silva, A.S.; Ghisi, E.; Fachinotti, V.D. Residential Building Design Optimisation Using Sensitivity Analysis and Genetic Algorithm. Energy Build.
**2016**, 133, 853–866. [Google Scholar] [CrossRef] - Geyer, P. Component-Oriented Decomposition for Multidisciplinary Design Optimization in Building Design. Adv. Eng. Inform.
**2009**, 23, 12–31. [Google Scholar] [CrossRef] - Geyer, P. Multidisciplinary Grammars Supporting Design Optimization of Buildings. Res. Eng. Des.
**2008**, 18, 197–216. [Google Scholar] [CrossRef] - Geyer, P. Embedding Optimization in the Design Process of Buildings—A Hall Example. In Proceedings of the 24th W78 Conference, Maribor, Slovenia, 26–29 June 2007; pp. 689–698. [Google Scholar]
- Nadkarni, R.R.; Puthuvayi, B. A Comprehensive Literature Review of Multi-Criteria Decision Making Methods in Heritage Buildings. J. Build. Eng.
**2020**, 32, 101814. [Google Scholar] [CrossRef] - Tan, T.; Mills, G.; Papadonikolaki, E.; Liu, Z. Combining Multi-Criteria Decision Making (MCDM) Methods with Building Information Modelling (BIM): A Review. Autom. Constr.
**2021**, 121, 103451. [Google Scholar] [CrossRef] - Barzilai, J. Preference Function Modelling: The Mathematical Foundations of Decision Theory. In Trends in Multiple Criteria Decision Analysis; Ehrgott, M., Figueira, J.R., Greco, S., Eds.; International Series in Operations Research & Management Science; Springer: Boston, MA, USA, 2010; pp. 57–86. ISBN 978-1-4419-5904-1. [Google Scholar]
- Binnekamp, R. Preference-Based Design in Architecture; IOS Press: Amsterdam, The Netherlands, 2010. [Google Scholar]
- Scientific Metrics: Home. Available online: https://scientificmetrics.com/ (accessed on 10 February 2022).
- Brand, S. How Buildings Learn: What Happens after They’re Built; Viking: New York, NY, USA, 1994; ISBN 978-0-670-83515-7. [Google Scholar]
- World’s Fastest Building Life Cycle Assessment Software—One Click LCA. Available online: https://www.oneclicklca.com/ (accessed on 25 October 2021).
- EnergyPlus. Available online: https://energyplus.net/ (accessed on 23 February 2022).
- OpenLCA.Org. OpenLCA Is a Free, Professional Life Cycle Assessment (LCA) and Footprint Software with a Broad Range of Features and Many Available Databases, Created by GreenDelta since 2006. Available online: https://www.openlca.org/ (accessed on 23 February 2022).
- Brightway2 LCA Framework. Available online: https://brightway.dev/ (accessed on 23 February 2022).
- NVM Business. Kantoren in Cijfers 2021 Statistiek van de Nederlandse Kantorenmarkt; NVM Bussiness: Utrecht, The Netherlands, 2021. [Google Scholar]
- Debrezion, G.; Pels, E.; Rietveld, P. The Impact of Rail Transport on Real Estate Prices: An Empirical Analysis of the Dutch Housing Market; Tinbergen Institute Discussion Papers; Tinbergen Institute: Amsterdam, The Netherlands, 2006. [Google Scholar]

**Figure 3.**Material options for the façade and structural frame of the demonstrator case provided by Heijmans.

**Figure 4.**(

**a**) Preference curves and weights for all iterations; (

**b**) The results of optimization runs for each iteration showing the optimal values of design variables and objectives.

Material (the Full Name as it Appears in OneClick LCA) | Unit | Embodied Carbon, kg CO_{2}eq/Unit | Total Emissions from Transport, kg CO_{2}eq/Unit | Price, EUR/Unit |
---|---|---|---|---|

Ready-mix concrete, normal strength, generic, C 28/35 (4000/5000 PSI) with CEM I, 0% recycled binders (300 kg/m^{3}; 18.7 lbs/ft3 total cement) | m^{3} | 292.13 | 18.72 | 344.87 |

Ready-mix concrete, normal-strength, generic, C 40/50 (5800/7300 PSI), 0% recycled binders in cement (400 kg/m^{3}/24.97 lbs/ft3) | m^{3} | 386.96 | 18.72 | 348 |

Rock wool insulation, unfaced, L = 0.035–0.04 W/mK, 40 mm, 1.2 kg/m^{2}, 30 kg/m^{3}, Lambda = 0.035 W/(m.K), ProRox LF 970 (ROCKWOOL) | m^{3} | 35.25 | 0.07 | 57 |

Wall bricks, 575 kg/m^{3} (Ziegel) | m^{3} | 138.29 | 1.32 | 115 |

Hot-dip galvanised structural steel, 0.45–1.5 mm, 7850 kg/m^{3} (Ruukki) | m^{3} | 20,794.65 | 111.24 | 5976.96 |

Structural steel profiles, generic, 90% recycled content (typical), I, H, U, L, and T sections, S235, S275 and S355 | m^{3} | 6033.17 | 111.24 | 5976.96 |

Hollow core concrete slab, HDF27, C 40/50, 270 mm, 344 kg/m^{2}, reinforcing 12.7 kg/ton (Strängbetong) | m^{2} | 46.78 | 0.79 | 60.64 |

Aluminium profile, 2700.0 kg/m^{3} | m^{3} | 29,289 | 48.60 | 8920 |

Sandwich panel with polyisocyanurate (PIR) foam and double steel siding, U = 0.21 W/(m2K), 100.9 mm (Total), 0.5 mm (Outer sheet), 0.4 mm (Liner sheet), 100 mm (Insulation), 12.11 kg/m^{2}, 120.02 kg/m^{3}, SAB WB 100.1000 (SAB-profile, Tata Steel Europe) | m^{2} | 37.2 | 0.20 | 49.92 |

Aluminium profiled sheets, thickness 0.7 mm, height of profile 65 mm, 2.9 kg/m^{2} (European Association for Panels and Profiles) | m^{2} | 27 | 0.03 | 6.24 |

Cross-laminated timber, 491.65 kg/m^{3}, T: 51–500 mm, max width: 2.95–4.80 m, max length: 16–20 m, X-Lam (Studiengemeinschaft Holzleimbau e.V.) | m^{3} | 187.23 | 4.15 | 544.93 |

Wooden joist floor assembly, 278 mm, incl. 225 mm mineral wool insulation | m^{2} | 36 | 0.45 | 132 |

Timber frame external wall assembly, incl. mineral wool insulation | m^{2} | 65 | 0.73 | 110 |

Name | Bounds | Type |
---|---|---|

Floor area | 700–2500 m^{2} | Continuous |

Number of floors | 2–10 | Integer |

Set of the building’s facade materials | 1—Brick and Rockwool insulation 2—Steel sandwich panel façade with polyisocyanurate insulation 3—Timber frame façade with mineral wool insulation | Categorical |

Set of the building’s structural frame materials | 1—Concrete frame 2—Structural steel profile frame and a hollow concrete slabs floor 3—Cross-laminated timber frame with a wooden joist floor | Categorical |

Distance from a railway station | 0.2–15 km | Continuous |

Parameter | Value |
---|---|

Population size (‘PopulationSize’) | 250 |

Max. generations (‘MaxGenerations’) | 400 |

Max. stall generations (‘MaxStallGenerations’) | 20 |

Function Tolerance (‘FunctionTolerance’) | 1E-9 |

Vectorized form (‘UseVectorized’) | true |

Elite population size (‘EliteCount’) | 30 |

Crossover fraction (‘CrossoverFraction’) | 0.5 |

Name | Minimum Value Given the Design Variables’ Bounds | Maximum Value Given the Design Variables’ Bounds |
---|---|---|

Embodied carbon, kg CO_{2}eq | 241,950 | 3,992,042 |

Construction costs, EUR | 272,675 | 5,636,095 |

Profit from rent, EUR | 288,978 | 5,162,471 |

Location, km | 0.2 | 15 |

Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations. |

© 2022 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).

## Share and Cite

**MDPI and ACS Style**

Zhilyaev, D.; Binnekamp, R.; Wolfert, A.M.R.
Best Fit for Common Purpose: A Multi-Stakeholder Design Optimization Methodology for Construction Management. *Buildings* **2022**, *12*, 527.
https://doi.org/10.3390/buildings12050527

**AMA Style**

Zhilyaev D, Binnekamp R, Wolfert AMR.
Best Fit for Common Purpose: A Multi-Stakeholder Design Optimization Methodology for Construction Management. *Buildings*. 2022; 12(5):527.
https://doi.org/10.3390/buildings12050527

**Chicago/Turabian Style**

Zhilyaev, Dmitry, Ruud Binnekamp, and A. M. Rogier Wolfert.
2022. "Best Fit for Common Purpose: A Multi-Stakeholder Design Optimization Methodology for Construction Management" *Buildings* 12, no. 5: 527.
https://doi.org/10.3390/buildings12050527