CAADRIA 2017 | Solar & planning compliance

‘Solar and Planning Regulatory Provisions Compliance: Building Quantitative Performance Design Solutions for Residential Development’ by Suleiman Alhadidi, Paul Wintour & Heather Mitcheltree. Published in:

P. Janssen, P. Loh, A. Raonic and M. A. Schnabel (eds.), Protocols, Flows and Glitches; Proceedings of the 22nd International Conference of the Association for Computer-Aided Architectural Design Research in Asia CAADRIA 2017. © 2017, The Association for Computer-Aided Architectural Design Research in Asia (CAADRIA), Hong Kong.

Abstract

Fuelled by a recent surge in Asian investment, Australia is currently experiencing a housing development boom. The local governments have produced a series of planning guidelines, developed to improve the quality of apartments. Recently imposed guidelines, such as the State Environmental Planning Policy No. 65 (SEPP65) in New South Wales, have challenged design methods; requiring residential buildings to be designed based on more prescriptive environmental performance requirements. This paper looks at a computational design method developed using quantitative solar analysis to measure planning compliance and reduce the overshadowing impact of planning proposals on surrounding residential buildings. A generative design method has been developed based on environmental and financial metrics to satisfy both the planning controls and the client’s commercial interests so as to produce rapid design alternatives.

Introduction

Both internationally and locally within Australia, we are seeing the continual development and modification of planning legislation and guidelines in an attempt to keep pace with the rapid changes in urban development. Legislation such as the UK’s ‘Right to Light’, and state guidelines within Australia such as the State Environmental Planning Policy No. 65 (SEPP65) in New South Wales¹, have been established in an attempt to improve the quality of developments in relation to building performance and amenity. These regulations set design guidelines for performance aspects such as solar and daylight access within residential apartment developments.

Performance requirements have shaped a new generation of design practices that prioritise data-enabled design processes so as to provide quantitative solutions². As a result, planning legislation is shaping building forms. This can be seen in projects such as the Solar Carve Tower in New York by Studio Gang architects³, and BVN’s Pitt St development in Sydney⁴.

Antecedents and what is missing

The current computational analysis workflow practices utilised to design and develop compliant residential developments have been inherited from, and shaped by, generations of software tools such as Ecotect, Autodesk Vasari, Green Building Studio, and recently Sefaria and Autodesk Insight 360. This workflow, in simplified terms, follows a process of: Model, Simulate, Analyse. Within this process, designers model a building envelope, or a building, in order to get feedback on the design’s performance. Whilst these processes have enabled rapid modelling of, and feedback on, the performance of different design options, not all tools enable flexible implementation of local regulations.

Whilst Sefaria and Insight 360, for example, have added a SEPP65 component to their tool, these plugins at present have limited agility. For example, Insight 360 currently utilises 4000 lux as a minimum threshold per room when checking building compliance, however this lux level is not necessarily what the regulations require⁵. Sefaira on the other hand enables SEPP65 compliance testing and analysis through analytics tools that enable “annual daylighting, daylight factor, and time-of-day simulations”⁶.

Despite continuing development of regional compliance plugins, at present, these tools do not allow the designer to manipulate the design so as to accommodate for the full complexity of site-specific regulations and requirements. Within the current use parameters, the tools do not enable the reverse engineering of a design to produce building envelopes that are responsive to site-specific neighbourhood contexts (i.e. minimum light access requirements for surrounding buildings), nor do they enable the inclusion development feasibility data such as financial metrics and apartment mix variations within the model.

Whilst such processes enable designers to run a large number of models and iterative design variables that would traditionally not have been feasible due to their complexity and labour intensive requirements, outcomes are limited by in-built assumptions, lack of guidance and integration within the design process⁷, and parameters that are pre-set at the outset. Designers are restricted by software structuring that does not allow full control over performance parameters. Work currently underway, aims to develop a workflow process that enables upfront simulation capacity and the incorporation of a greater complexity of variables within the model. Within this process, Simulate – Generate, the simulation process becomes the feedback loop. Through this reverse engineering process, designers are able to understand upfront what is allowable, enabling the computational design process to inform their designs from the outset.

Smart design processes: Financial and performance objectives

Through the control of building design performance parameters and incorporating the generative methods from the outset, designers are able to maximise building performance objectives. Residential projects are often quite prescriptive in their constraints. Within this process, metrics need to satisfy both planning controls and the client’s commercial interests. This paper outlines two case studies in which work process and parametric design tools were developed that allow for quick feasibility assessment based on the number of hours of direct sunlight, the building mass and each individual floor received. The process also utilises an automated optimisation method to comply with planning controls, assess project financial feasibility, and allow for a better distribution of the apartment mix.

The Apartment Configuration Generator Tools were based on the following parameters:

• Living rooms and private open spaces for at least 70% of apartments in a development should receive a minimum of three hours of direct sunlight between 9am and 3 pm in mid-winter (in dense urban areas a minimum of two hours may be acceptable).
• Limit the number of single-aspect apartments with a southerly aspect (SW-SE) to a maximum of 10% of the total units proposed.
• 60% of residential units should be naturally cross ventilated.

Generative performance driven computation

Impact assessment and reduction 

45 Murray Street, Pyrmont, is located in a prominent position to the west of the Sydney CBD. The existing site was underdeveloped, relative to its surrounding context. To examine the potential for increased site yield, a comprehensive urban design and computational analysis were prepared that evaluated the site and surrounding context. Solar access impacts of the proposed maximum building parametric envelope and an automated shadow assessment tool were developed that enabled a ‘simulation first’ process of neighbouring sites and the proposed site. This information then fed into the design generation process. The chart demonstrates the existing amount of solar access to each nominated window and the affect (if any) of the proposal on solar access based on the solar access requirements defined by City of Sydney.

The computation process generated using Grasshopper and C# to calculate sun vector access to liveable spaces can be summarised as the following:

1. Simulate the solar intake for all surrounding sites and get existing performance data for all spaces.
2. Maximum building envelope.
3. Maximum building envelope carved to maintain 2 hours solar access to neighbouring 32 Harwood Street.
4. Repeat the process to generate the compliant envelope.

From the simulation, it was found that less than 70% of surrounding apartments currently receive at least 2 hours of direct sunlight between 9 am and 3 pm at mid-winter. It indicated that six apartments at a neighbouring site would lose between 8 and 28 minutes of sunlight to habitable spaces during mid-winter.

Apartment combination generator and mix matrix 

A selection of pre-defined apartments is chosen as the starting point. These apartments are evaluated based on various criteria including: SEPP65 compliance; single or double aspect; accessibility; etc. Since the orientation of the apartment is not yet known, preliminary results can be ascertained and embedded into the Rhino apartment block using Elefront to establish desired performance requirements. The next step is to deploy these apartments within the building massing. The presented script evaluates the problem mathematically using the Brute Force component within the TT Toolbox plug-in.

Four sliders are established which represent the number of apartments per floor for that particular apartment type. These parameters are known as the ‘genomes’. Assuming there will be a maximum of 10 apartment types per floor, the slider can be set to have a minimum value of 0, and a maximum value of 10 (11 possible genome states). Since there are four sliders, one for each apartment type, this equates to 14,641 possible outcomes.

As the Brute Force component runs, the script records the genome mix. This list is then exported to Excel. The spreadsheet is then populated with formulas to calculate the total area each genome mix would produce in terms of Net Saleable Area (NSA). This area value is used later to determine if that genome mix is suitable. A simple calculation then takes the NSA generated from the massing, and combined with the apartment mix ratio and average area per apartment type, generates the target number of apartments. It is important to note that when calculating these figures, the ratios are based on the total number of apartments, not the total NSA. Therefore, the formula is:

Once all possible permutations and number of apartments are known, it is possible to filter out configurations that are less desirable. By importing from Excel the total NSA per floor for each genome mix, a comparison can be made between the genome mix NSA and the actual NSA required. The delta between these two values is used as the fitness value. The previous step evaluated apartment mixes on a level by level basis, based on NSA. However, other parameters must also be addressed, such as overall apartment mix.

In order to do this, every level must be taken into consideration. Since there are 20 stories in the presented building with 20 possible apartment mixes per floor, this would equate to 100 quintillion possible solutions if the brute force methodology is adopted. This is obviously not viable due to the time it would take to compute all possibilities. The script therefore utilises Galapagos, an evolutionary solver within Grasshopper to define optimal options.

A simple way of taking a holistic approach to apartment distribution is to calculate the delta number of apartments compared to the brief requirements. By mixing and matching one of the top 20 genome/apartment mixes per floor, it is possible to get close to the brief’s requirements. Fitness, therefore, can be defined by the delta number of total apartments compared to the brief. However, this is not always adequate. For example, in certain situations such as a tower, stacking of apartment layouts is desirable. In this scenario, limiting the variations in apartment distribution might be desirable. To cater for this, the script ‘penalizes’ an overall arrangement based on the number of different mixes it has, essentially ensuring that an apartment mix is repeated throughout the tier/building. By adjusting the various penalty inputs, the user is able to prioritise and customise the fitness criteria.

Summary

The paper outlined the workflows of two selected residential planning proposals where solar based performance scripts were developed and utilised planning controls and financial metrics, one of the limitations of the scripts is that no apartment geometry is generated.

References