Work Samples (2021-22)

Kritika Kharbanda

Technology/Digital

01 Assembly Performance Analyzer Tool

Understanding the carbon footprint (A1-A3)of building assemblies

02

Carbon Literacy Application

Visual-based app for raising awareness on material environmental impact among citizens

03 LearnCarbon

Rhino3D plugin for early-stage carbon footprint prediction

Simulation/Design

04

add-on, for effective decarbonization.”

Sandy Springs, Atlanta

Modeling urban energy flows to lay roadmap for a net-zero district in the heart of Atlanta, Sandy Springs

05 Resilient Hub

High-performance, affordable, resilient and lowcarbon commercial building at the Boston Seaport

Professional

06

Cockle Bay Park

A human scale sustainable destination and a true urban icon on Sydney’s skyline, Cockle Bay boasts of energy efficiency

07 Turku Museum

A music experience inspired by nature

“I aim to transform the construction sector by bridging the digital silos and infusing sustainability as a key contributor to the design process, rather than a mere

Assembly Performance Analyzer Tool

Understanding the carbon footprint (A1-A3) of building assemblies

Developed as part of the seminar SCI 6484, Harvard GSD, Spring 2022

Authors Advisor

Kritika Kharbanda

Ellie Jungmin Han

Student Grant: Harvard Office for Sustainability Nominated Project of SCI 6484 for Harvard GSD Archives

Embodied Carbon | Life Cycle Assesment Data Science | Linear Regression

Python | PyTorch | Javascript

To establish local building standards, efficient, accessible, and transparent datasets within the industry are needed. Funded by the Harvard Office for Sustainability Student Grant, this project focuses on partnering with the building industry for the collection, analysis, and augmentation of the data for external wall assemblies, focusing on embodied carbon, and potentially including cost.

The end goal is to consolidate all the current industry data into a dynamic web application format that encourages architectural literacy on the carbon footprint of each assembly to enable conscious design. The methodology entails three major steps – Data collection, analysis and digital database development. The scope of the project is cradleto-gate (A1-A3), and the construction assemblies include building envelope and flooring details.

Legend Cost/unit

Awards Tools Brief Medium Medium

High High

Representation of the Assembly Analyzer Tool

Analysis and Visualization for Embodied Carbon values in the overall dataset (with outliers)

Analysis and Visualization for Embodied Carbon values in the overall dataset (without outliers)

Finally, the assembly information page shows all the materials with thicknesses in the detail and a heatmap diagram that depicts the embodied carbon footprint of each material

Application: Unfolded

1: Landing Page

User can access the database or move directly to a ‘Predictor’ tool that has been trained on a Linear Regression model for predicting carbon footprints of new assemblies

2: Database Overview

Before accessing the database, the application shows a distibution of all the values in the database through Violin Plots

Machine Learning Predictor Application

In-depth analysis to compare the Embodied Carbon values with Thickness of insulation and their respective R-Values were performed, and a Regression model was trained to predict the embodied carbon footprint of new assemblies

3: Filters

The user also has options to select from the category for details, like window, curtain wall, roof etc. and select from the available ranges of carbon footprints

4: List of Values:

A list of values from the application backend show up, and the user ca browse through these and select

Carbon Literacy Application

Visual-based app for raising awareness on material environmental impact among citizens

Developed as part of the seminar SCI 6485, Harvard GSD, Spring 2022

Material | Embodied Carbon | Visual Machine Learning | Nueral Networks | Classification

Python | PyTorch | Tensorflow | Pix2Pix | StyleGAN

Building Industry contributes to 40% of the global CO2 Emissions. 60% is through the operational carbon, while 40% is through embodied carbon within materials, and the residential buildings play a huge role.To get the citizens on board the retrofit projects, we need to make them understand the impact of materials. Therefore, we develop a web platform that is accessible to all.

The web platform takes the users through four steps:

1. Upload an image and a predicted carbon footprint of the prominent material in the image shows up

2. Change the material by coloring over it, and understand the new predicted carbon footprint of the changed material

Concept Tools Brief Three examples highlighting the workflow (as numbered):

3. For fun: Generate new material textures by selecting a texture and a pattern

4. Finally, out of curiosity: Ask the machine to predict the carbon fooptrint of the new generated material image

User chooses an image

The ML model predicts the materials in the image by creating a segmented graphic

The user modifies the colors in the segmented graphic to change a material

The ML model generates a new image from the modified segmented graphic

Actual segmented image; B = Ground Truth (Made for testing the results)

Authors Advisor Position

Kritika Kharbanda, Harshika Bisht, Gabriel Lam Sabrina Osmany Teaching Assistant for SCI 6485

01:

Upload any image and the application predicts the prominent material, along with the carbon footprint (embodied carbon in kgCO2e/m2 or kgCO2e/unit) for the uploaded image!

For example, in this image of House Zero, wood is the prominent material with an EC factor of 0.72 and the embodied carbon value of 10kgCO2/m2

The graphs alongside reflect the training and validation percentages

02:

Now, once the image is uploaded, the user can change the material by coloring over it, or selecting an option from the drop down meny, as shown below, and understand the new predicted carbon footprint of the changed material

The sample uploaded image with white paint clad concrete facade

The drop down menu allows users to change materials, like changing the main facade to stone or increasing window area. In this case, lets select ‘Change main facade to stone’

Voila! The ML model now enhances the image to added a stone texture to the main facade, as you can see in the last image on the right, Further the app tells the user the impct they made on the embodied carbon, as in the image it says ‘Wow, you saved some carbon there!’

Classifying materials Frontend Application

Segmentation & material change

Procedural Texture Generation

ML model ResNet101 ML model

Application: Unfolded

LearnCarbon

Rhino3D plugin for early-stage carbon footprint prediction

Team project developed as part of the 36-hour AEC Hackathon 2021 in Denmark

Authors Kritika Kharbanda, Iliana Papadopoulou, Panayota Pouliou, Karim Daw, Anirudh Belwadi, Hariprasath Loganathan

Winner: ‘Best Project that solves a big AEC Problem’ at AEC Hackathon 2021 (LINK) Presentation during the eCAADe 2022 Conference (LINK)

Brief

LearnCarbon is a Rhino3D plugin that facilitates two neural networks trained with the Embodied Carbon Benchmark Study by Carbon Leadership Forum, which learns the relationship between building geometry, typology, and construction type with the Global Warming potential (GWP) in tons of C02 equivalent (tCO2e).

The first one, a regression model, can predict the GWP based on the massing model of a building, along with information about typology and location.

The second one, a classification model, predicts the construction type given a massing model and target GWP.

Embodied Carbon | Structure type Rhino3D Plugin | Machine Learning

Numpy | Pandas | Tensorflow | Keras | Grasshopper

Concept

Awards Tools

Representation of the LearnCarbon Rhino3D Plugin

The tool is designed with two ML models:

Model A, a Non-Linear Regression model, predicts the value of Global Warming Potential or GWP (kgCO2e/m2) from the trained data.

Model B, a Classification model, assigns a class label for structural typology, after describing the target GWP value attribute to input data.

Structural Typology : For Classification & Benchmarking

LearnCarbon: Plug-in Interface

Scenario 1 (using Model A):

For predicting the Global Warming Potential in kgCO2e/m2, based on the geometry attributes of floor area, height or number of floors, location, building type and structural type.

Select the 3D Model

Automated input with flexibility for manual feeding

Specify Project Location

Building Type

Automated input with flexibility for manual feeding

Finally, select the type of structure

Voila! The plug-in predicts the GWP of the current design in seconds!

Data Processing

The dataset consists of categorical and numerical features which describe building projects, and the categorical features were converted into numerical using Pandas.

From the Corelation Matrix of Features shown alongside, we observe that the features of Building Construction Type and GWP are negatively correlated, which showcases the potential of teaching this to a model. Another relevant indication to our hypothesis is between Floor Count with Location and Ground Floor Area with GWP. While the latter logically form a strong relationship, the former points out a potential bias in the dataset or even an observation of building heights per continent.

After training, the next step was connecting with Rhino3d. We used the Hops component in Rhino3d that allowed for integrating Tensorflow and Numpy into Rhino 3d CPython. The Hops infrastructure developed by McNeel is a thin wrapper around an underlying Flask server that allows running a flask server and serving data from an external python instance. Figure shown alongside depicts the final outcome for the plug-in.

Scenario 2 (using Model B):

For assigning a potential structure type for a particular geometry, based on floor area, height, building type, location and benchmark Global Warming Potential in kgCO2e/m2

Select the 3D Model

Automated input with flexibility for manual feeding

Specify Project Location

Building Type

Finally, input the target GWP for the project

Voila! The plug-in assigns a feasible structure type based on the GWP!

Timber Reinforced Concrete Steel Concrete Timber Concrete

A Pilot study for Modeling Urban Energy Flows

Sandy Springs as a self sustainable heart of Atlanta

Developed as part of the course 4.433 at MIT, Spring 2022

Authors Advisor

Kritika Kharbanda, Yiwei Lyu, Sihui Chen, Katie Pelton

Christoph Reinhart

Building Simulation 2023 (Abstract under review)

Urban Energy Modeling | Net Zero Districts Climate Change | GHG Emissions

MIT’s Urban Modeling Interface | Urban Network Analysis toolbox for Rhino3D

Working with the municipality of Atlanta, this project aims to propose a holistic development scenario for Sandy Springs, that is tested through environmental performance simulations and scores high on the parameters of:

1. Neighborhood Walkability

2. Outdoor Thermal Comfort 3. Access of Daylight 4. Operational and Embodied Building Energy Use 5. Economic viabaility and Cash Flow 6. Resouce Efficient District Energy system and electicifying the city

7. Climate Change and Urban Heat Island

8. Resiliency and long-term building energy use

Initial studies inclue experimenting with trade-offs between daylight and massing with different building typologies for maximum economic profits for the ‘heart of the city,’ followed by several supporting simulations that guided the final proposal for the neighbourhood.

59% Average sDA

Stepped Courtyard Linear

68% Average sDA 52% Average sDA

Awards Tools Brief Legend Spatial Daylight Autonomy(sDA)

65% Commercial 35% Residential 72% Commercial 28% Residential 59% Commercial 41% Residential Retail Restaurant Residential School Office Warehouse

Legend Proposed Building Typology 1980 2004

Massing studies for trade-offs between access to sufficient Daylight (sDA), vs. Economic returns based on typology and built-up area.

Neighbourhood Concept Urban Simulations

The identified parcel for development includes pre-existing residential, commercial and reatil with large parking areas

Two corridors, Primary NorthSouth, and Secondary East-West were proposed for encouraging pedestrianized paths and reducing personal vehicles

N-S Primary Green Corridor

Proposed Civic Centre

E-W Secondary Green Corridor

Proposed new Retail

Visualizations from the Primary corridor, that focuses on a central green park along the roadway for promoting pedestrians, and with retail structures lining the street.

Trade-off between Operational Energy, Carbon Fooprint and Economic Impacts were studied for both existing and new proposed buildings, and an optimized solution was selected

Total Energy Consumed (kWh/m2) Carbon Comparison Cost Comparison

Energy consumption in a baseline case, with added dimming and LED lighting, using Energy Star appliances for reducing peak heating and cooling, and better natural ventilation with efficient glass type

Total Footprint 493,544 m2

Total Occupancy 84,717 per

Proposed Green

Current

Built-Up Proposed Typology

Added Greenroofs 9925 m2

Added Rooftop PV 5910 m2

On-Site Power Generation 3,555,252 kWh

Average sDA 64%

Spatial Daylight Autonomy Embodied Emissions (kgCO2e/m2)

Legend Spatial Daylight Autonomy(sDA) 100 175 250 Legend Utility Legend Utility Legend Utility

Normalised Footprint smaller buildings with RCC structure contribute the most

Legend Embodied Emissions (kgCO2e/m2)

Carbon and cost comparison for baseline electricity use, with unrestricted and restricted PV energy generation and installing a Heat Pump

Resilient Hub

High-performance, affordable, resilient and low-carbon commercial project at the Boston Seaport

Developed as part of the seminar SCI 6374, Harvard GSD, Spring 2022

Authors Advisor

Awards

Kritika Kharbanda, Lara Tomholt, Kuan-Ting Chen, Sihui Chen, Andrew Gibbs

Prof. Holly Samuelson

US DOE Solar Decathlon Third Prize: Commercial category Built Environment Plus 2021, Student Prize

ISI/APH Conference: Selected for Project Showcase

Concept

Tools

Energy Efficiency | Commercial Benchmark

Adaptable envelope | Resiliency

Climate Studio | Ladybug Tools | Cardinal LCA

Brief

ResilientHub, situated in the Seaport District of Boston, is a future-ready building that maintains the highest energy efficiency and occupant comfort level possible throughout its lifetime.

Adaptable ETFE pillow façades optimize solar heat gain and daylight access in response to daily and seasonal weather changes, and future global warming and urbanization. A solar chimney, placed prominently at its most optimal position for solar heat gain, provides buoyancy-driven natural ventilation and significantly lowers the building’s cooling loads with future rising temperatures.

The project’s maximum annual site EUI goal for the year 2025 was set to 28.6 kWh/m2, and for financial feasibility, the project’s Internal Rate of Return (IRR) was required to be above 20%

Visualization of ResilientHub, while approaching from the Summer St.

The adaptable ETFE pillow façade system with integrated gold-coated PDMS film. The strain of the internal PDMS layer can be modulated automatically by an array of sensors placed on the exterior of the façade that measure outdoor temperatures and irradiation and in the indoor space that measure indoor temperatures and occupancy.

Future Adaptability - Climate Change

01

Mapping the changes in solar irradation levels over the years, and integrating shading in the Southern facing facades

02

Environmental development scenario 2025-2080 for ResilientHub: An increase in temperatures by 4.1ºC and rise in sea water level by 2080. The project’s maximum annual site EUI goal for the year 2025 was set to 28.6 kWh/m2 (28.6 kBtu/ft2) and will equal an annual source EUI goal of 80 kBtu/ft2 (252 kWh/m2).

Visualization of atrium - materiality palette of recycled steel, glass, timber and interior plants, highlighting flexible furniture in the multi0functional space

Cockle Bay Park

A human scale sustainable destination and a true urban icon on Sydney’s skyline, Cockle Bay boasts of energy efficiency

Sustainability Engineer on project involved from Competition till Detailed Design phases

Concept Tools Brief

Sustainability strategy | Microclimate simulations

Facade Optimization | Energy Efficiency

Ladybug Tools | OpenFoam | ENVI-Met

Thoughtful design of the public realm can create thermally comfortable, attractive and more healthy urban environments. Integrating architecture, planning and landscape design enhances the positive natural and man-made features in this commercial high rise with public podium.

An increase in the comfort quality allows for a diverse array of activities at the podium level. Spaces of varied scales are obtained by experimenting with volumes that facilitate effective seasonal shading and controlled wind influx. Great flexibility in the time and activity pattern at the podium can hence be exercised that also helps in maximising commerce in all times of the year. We were able to decrease the solar exposure at the North facade by upto 80%, reduce heat stress at plaza level by 40%, and lower high wind speeds by 25%.

As a sustainability intern, I was responsible for developing the sustainability concept, strategy and action plan for this key project located in the prime of Sydney. As a Sustainability Engineer, I worked with the consultants to prepare Material specifications based on embodied carbon footprint, Benchmarking & ensuring the Sustainability standards across all domains to satisfy the client portfolio.

Visualization of Cockle Bay Park as viewed from Darling Harbour (Developed by MIR)

Form Development Envelope Design

Irradiation Analysis

Plaza Design

The north-west wind is blocked by the mass of Podium. The UTCI chart shows that 67% of the time in the year ‘comfortable’ outdoor conditions are experienced

Incoming wind is funelled by fragmenting the Podium that reduces heat stress. Thischanges the UTCI chart to increase the ‘comfortable’ conditions to 78% in a year

The downdraft wind is diverted away from the podium by further fragmentation. This helps increase the time of comfortable conditions to 80%

Strategic landscaping optimises the radiation and wind to improve the microclimate. Therefore, 90% of the time, it feels comfortable at the plaza level

Materiality Study

Stack Joint Details

Computational

Turku Concert Hall

A music experience inspired by nature

Sustainability Engineer on project for Competition phase

Microclimate simulations | Urban Comfort Energy Strategy | Life Cycle Assesment

Ladybug Tools | OpenFoam | Cardinal LCA

Located in Turku, the venue is a representation of the culture, a ‘house’ for music experiences. The museum building is inspired by nature resembling a Finnish forest that arises from the rock. The materiality inspiration comes from nature’s renewable resources of biomass and geothermal energy, The design boasts of a thermodynamic spatial experience with an embodied-operational energy balance.

The sustainability goals include resource return by minimizing embodied carbon, lowering peak energy demand by switching to green energy, and maximize outdoor comfort. The carbon footprint is very low due to a hybrid wood-concrete structure, with timber columns and slabs. A thermally activated building system is proposed for the concrete clad core walls, that generates a thermodynamic stimuli in the visitors and allows them to experience a difference in temperatures across the museum zones.

To maximize the outdoor spaces, the functions are stacked. The outdoor space has been strategically planned to minimize the cold winds, optimized sunlight and perceived temperatures. The timber facade also break down the winds and deflects the streams away. The trees further act as wind barrier.

Concept Tools Brief

Visualization of Turku Museum from the Theatre Bridge (Courtesy: Henning Larsen Architects)

Having a shared and scenic outdoor meeting venue that acts as a hub for non-visitors at the museum was an important part of the strategy.

This space is designed to be multi-functional and allow a variety of activities based on the seasons. For example, in warmer season, the space can be used as a fair space for families, for concerts in the night time; as an iceskating space in the winters and so on.