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1. Broad Area of Research
Computational Design, Architecture &
Sustainability
Name of Scholar: Prasoon Saxena
Name of Guide: Dr. Devendra Pratap Singh
Name of Co-Guide: __________ (Internal)
__________ (External)
Enrolment No: A1974023001
Admission Batch: Jul 2023
Semester: 1st (First)
Mode: Full Time
Amity School of Architecture and Planning

2. Brief Literature review
S No. Title & Year Author Details Abstract Main Body Types of Methodlogies & Processes Used
1 Computational Design in
Modern Architecture: Trends
and Challenges, 2017
Smith, J. & Johnson,
L.
This paper explores the emerging trends
and challenges in computational design
within modern architecture. The study
focuses on how computational tools are
revolutionizing design processes, from
conceptualization to execution, and the
challenges architects face in adapting to
these advanced technologies.
The paper identifies key trends in
computational design, such as parametric
modeling, digital fabrication, and
simulation-based design. It discusses the
challenges of integrating these
technologies into traditional architectural
practices, including the need for new
skills, the complexity of software tools,
and the balance between technological
and creative aspects.
Qualitative analysis of recent architectural
projects, interviews with practicing
architects, and a review of current
literature in the field.
2 Sustainable Architecture
through Computational
Design,
2022
Green, M. & Patel, S. Investigating the role of computational
design in promoting sustainable
architectural practices, this study
examines how computational methods
aid in achieving energy efficiency,
material optimization, and
environmental sustainability in building
designs.
The paper highlights the use of
computational tools for energy modeling,
material efficiency, and environmental
impact assessments. It showcases several
case studies where computational design
has led to sustainable architectural
solutions.
Case study analysis, simulation modeling,
and comparative assessment of pre- and
post-implementation environmental
impacts.
3 Integrating Artificial
Intelligence in Architectural
Design, 2022
Chen, Y. & Kumar, A. This paper delves into the integration of
artificial intelligence (AI) in architectural
design, focusing on how AI can enhance
creativity, efficiency, and decision-
making in the architectural process.
The study explores AI applications in
architectural design, including generative
design algorithms, AI-assisted
conceptualization, and predictive
analytics for urban planning. It discusses
the potential and limitations of AI in
architecture, including ethical
considerations and the future of AI in the
industry.
Analysis of emerging AI technologies in
architecture, review of AI-assisted design
projects, and expert interviews.
4 Quantifying Sustainability in
Architectural Design: A
Computational Approach,
2019
Rodriguez, A. & Lee,
H.
This paper presents a novel approach to
quantifying sustainability factors in
architectural design using computational
methods. It examines the challenges in
converting qualitative sustainability
criteria into quantifiable measures and
how these measures can be integrated
into design processes.
Key topics include the development of
scoring systems for sustainability, the
integration of these systems with Building
Information Modeling (BIM) tools, and
the role of machine learning in
enhancing accuracy and adaptability.
Development of quantification models for
sustainability criteria, integration with BIM,
and testing the models with real-world
design data.

3. Brief Literature review
S No. Title & Year Author Details Abstract Main Body Types of Methodlogies & Processes Used
5 Challenges in the Adoption of
Computational Design in
Architecture, 2015
Martinez, P. & Zhao,
Y.
This paper addresses the challenges
faced by architectural firms in adopting
computational design methods. It delves
into the technological, skill-based, and
conceptual barriers in integrating these
advanced methods with traditional
architectural practices.
The study highlights the gap in skills
required for advanced computational
tools, the resistance to change in
traditional design methods, and the
balance between creativity and
technology-driven design approaches.
Surveys and interviews with architectural
firms, analysis of project workflows, and
case studies of successful integrations of
computational design.
6 Integrating Environmental
Sustainability in
Computational Design, 2021
Green, M. & Patel, S. This paper explores how environmental
sustainability principles are integrated
into computational design processes in
architecture. It examines the use of
computational tools in achieving
environmentally sustainable designs and
the challenges faced in this integration.
The study discusses methods for
incorporating environmental sustainability
metrics into computational design
algorithms. It highlights the gap between
theoretical sustainability models and
their practical application in
computational design.
Comparative analysis of computational
design projects, sustainability metric
development, and case studies on the
integration of environmental
considerations in computational
architecture.
7 User-Centric Approaches in
Computational Design for
Architecture, 2019
Bhatia, R. & Turner,
L.
This research focuses on the
development of user-centric
computational design approaches in
architecture. It emphasizes the need for
computational design tools to not only
focus on technical efficiency but also on
user experience and human-centric
design aspects.
The paper explores the challenges in
creating computational design tools that
are adaptable to various user needs. It
discusses the potential of user-centered
design thinking in computational
architecture and the research gaps in this
area.
Qualitative research through user
experience studies, development of user-
centric design prototypes, and feedback
analysis from architectural design users.
8 Advances and Limitations in
Machine Learning for
Architectural Design, 2022
Thompson, K. &
Verma, A.
The study examines the advances in
machine learning (ML) applications in
architectural design, focusing on their
capabilities and limitations. It assesses
the role of ML in enhancing design
creativity and efficiency.
This research delves into how ML
algorithms are used in architectural design,
the types of design problems they address,
and the limitations they face in terms of
creativity, adaptability, and ethical
considerations.
Analysis of ML applications in architectural
projects, interviews with designers using
ML tools, and evaluation of ML limitations
in design creativity.
9 Lack of Standardized
Architectural Datasets for
Computational Design, 2021
Kim, J. & Singh, A. This paper addresses the critical lack of
standardized architectural datasets,
which hampers the advancement of
computational design. It explores the
challenges and implications of this gap
for the development of efficient
computational tools in architecture.
The study emphasizes the need for
comprehensive and standardized
datasets that can be utilized in
computational design for more accurate
simulations and predictions. It discusses
the barriers to data collection and
standardization in the architectural
domain.
Survey of existing architectural datasets,
analysis of data standardization
challenges, and recommendations for
dataset development and standardization
in architecture.

4. Brief Literature review
S No. Title & Year Author Details Abstract Main Body Types of Methodlogies & Processes Used
10 Data Scarcity in
Computational Architectural
Design: Implications and
Solutions, 2021
Chen, L. & Kapoor,
V.
This research investigates the scarcity of
data in computational architectural
design, examining its implications for the
field and proposing potential solutions.
It highlights how data scarcity limits the
application of advanced computational
methods.
The paper explores the types of data
necessary for effective computational
design and the reasons for their scarcity. It
suggests methods for data collection,
sharing, and utilization within the
architectural community.
Qualitative analysis of computational
design projects, interviews with architects
and data scientists, and exploration of
potential data collection and sharing
models.
11 Building a Framework for
Architectural Data Collection
for Computational Design,
2020
Martinez, P. &
Gupta, R.
This paper proposes a framework for
collecting and organizing architectural
data to support computational design. It
stresses the importance of a structured
approach to data collection to enhance
the capabilities of computational tools
in architecture.
The study outlines a structured framework
for architectural data collection, including
data types, sources, and methods of
organization. It addresses the challenges
in implementing this framework in the
architectural industry.
Development of a data collection
framework, pilot testing with architectural
firms, and analysis of the framework's
efficacy in supporting computational
design.
12 Innovative Data Collection
Methods for Computational
Design in Architecture, 2021
Fernandez, R. &
Kumar, N.
This paper explores innovative methods
for data collection in architectural
practices to facilitate computational
design. It focuses on novel techniques
that leverage technology to gather and
process data effectively for architectural
design.
The study discusses various tools and
methods like Building Information
Modeling (BIM), sensor-based data
collection, and crowd-sourced data
gathering. It examines how these
methods can be integrated into the design
process to enhance computational design
outcomes.
Analysis of different data collection tools,
case studies on their application in
architectural projects, and evaluation of
their effectiveness in enhancing
computational design.
13 Utilizing Big Data in
Architectural Design:
Challenges and Opportunities,
2022
Wilson, T. & Sharma,
P.
This research delves into the use of big
data in architectural design, particularly
for computational purposes. It assesses
the challenges in harnessing big data and
the opportunities it presents for
architecture.
Key topics include the sources of big data
relevant to architecture, methods of
processing and analyzing this data, and the
integration of these insights into
computational design processes.
Review of big data sources for
architecture, exploration of data
processing techniques, and case studies
showcasing the application of big data in
computational design.
14 Framework for Efficient Data
Management in
Computational Architectural
Design, 2023
Gonzalez, L. & Joshi,
S.
The paper proposes a framework for
efficient data management in
computational architectural design. It
outlines strategies for data collection,
storage, and utilization in a way that
enhances the computational design
process.
The framework includes guidelines for
data categorization, methods for
efficient data storage, and strategies for
integrating data with computational
design tools. The study also addresses
challenges like data privacy and
interoperability.
Development and testing of a data
management framework, analysis of its
impact on computational design efficiency,
and recommendations for its
implementation in architectural practices.

5. Brief Literature review
S No. Title & Year Author Details Abstract Main Body Types of Methodlogies & Processes Used
15 Integrating Computational
Design in Architectural
Education for Enhanced Data
Literacy, 2021
Morris, J. & Agarwal,
A.
This paper discusses the integration of
computational design into architectural
education, focusing on its impact on
data literacy among students. It argues
for the incorporation of computational
tools and methodologies in the
curriculum to foster a better
understanding of data collection and
analysis in architecture.
The study explores curriculum models that
include computational design, the
development of data literacy skills, and
the impact of such education on future
architectural practices. It emphasizes the
need for students to engage with real-
world data collection and analysis
scenarios.
Analysis of educational curricula, surveys
of architectural students and educators,
and case studies of educational programs
integrating computational design.
16 Preparing Future Architects:
Computational Design and
Data Competency in
Architecture Education, 2022
Baker, H. & Singh, R. This research examines the role of
architectural education in preparing
students for the increasingly data-driven
field of architecture. It specifically looks
at how computational design can be
used to teach data competency to
future architects.
Topics include teaching methods that
incorporate computational design for data
analysis, the benefits of such an approach
in understanding complex architectural
data, and the challenges in integrating
these techniques into existing curricula.
Qualitative and quantitative analysis of
architecture programs, interviews with
architecture educators, and evaluations of
student projects involving computational
design and data analysis.
17 Data-Driven Architectural
Design: Bridging the Gap in
Architectural Education,
2020
Gupta, P. &
Thompson, L.
The paper addresses the gap in
architectural education regarding data-
driven design. It proposes methods for
bridging this gap by incorporating
computational design techniques that
emphasize data understanding and
utilization.
It covers the necessity of equipping
students with the skills to navigate and
apply large data sets in architectural
design. The paper evaluates current
educational practices and suggests
modifications to include data-driven
design methodologies.
Review of architectural education trends,
development of new educational modules,
and pilot testing in architectural schools.
18 Exploring the Integration of
Cultural Context in
Computational Design, 2018
Chen, M. & Singh, R. This study explores the research gap in
integrating cultural context within
computational design in architecture. It
emphasizes the importance of cultural
sensitivity and relevance in the
computational design process.
Analyzes how computational design can
incorporate cultural factors and local
context in architectural projects. Discusses
the lack of tools and methodologies for
effectively integrating cultural aspects
into computational algorithms.
Qualitative analysis of architectural
projects, development of culturally-
informed design algorithms, and case
studies showcasing the integration of
cultural context.
19 Human-Computer Interaction
in Architectural Computational
Design, 2022
Garcia, A. & Patel, K. This paper addresses the gap in research
on human-computer interaction (HCI)
within architectural computational
design. It explores how HCI can enhance
the usability and functionality of
computational design tools.
The study examines the current state of
HCI in computational design tools,
identifying limitations in user experience
and interaction design. Proposes new
approaches for improving HCI in
architectural software.
Analysis of HCI in existing computational
tools, user experience studies, and
prototype development of improved
interaction designs.

6. Brief Literature review
S No. Title & Year Author Details Abstract Main Body Types of Methodlogies & Processes Used
20 Addressing Interface
Complexity in Computational
Architectural Tools, 2022
Miller, R. & Joshi, D. The paper focuses on the challenge of
interface complexity in computational
tools used in architecture. It explores
how this complexity affects the
adaptability and practical application of
these tools in architectural design.
Examines the current state of user
interfaces in computational design
software, identifying key areas where
complexity hinders usability. Suggests
design improvements to make these tools
more accessible and adaptable for
architects.
Evaluation of user interface design in
computational tools, user experience
studies with architects, and development
of guidelines for interface design
improvements.
21 Computational Design's Role
in Mitigating Climate Change
in Architecture, 2021
Chen, M. & Singh, R. This study delves into how
computational design in architecture can
contribute to climate action, particularly
in mitigating the effects of climate
change. It explores innovative
approaches in architectural design that
address climate change challenges.
Discusses the application of
computational methods in creating
energy-efficient and low-impact building
designs. The paper also evaluates the
effectiveness of these methods in
reducing the carbon footprint of
architectural projects.
Analysis of computational design projects
aimed at climate mitigation, evaluation of
energy efficiency and carbon reduction
metrics.
22 Adapting Architectural Design
for Climate Resilience Through
Computational Methods, 2022
Green, S. & Patel, H. This research focuses on adapting
architectural design for climate
resilience, using computational methods.
It examines how computational tools
can be used to design buildings that are
resilient to climate change impacts.
Highlights the role of computational
design in assessing and responding to
climate risks, such as extreme weather
events. Discusses strategies for designing
climate-resilient structures using
computational simulations and
modeling.
Case studies of climate-resilient
architectural projects, development and
application of computational models for
climate risk assessment.
Research Isuue:
The existing process for sustainability compliance checking in Indian architecture is hindered by its lack
of efficiency, inability to adapt to diverse climatic needs, and the growing demand for sustainable
buildings in the face of rapid urbanization. The manual nature of current processes leads to high error
rates, significant time and cost implications, and challenges in consistently applying multiple
sustainability standards. An automated system tailored to India’s climatic diversity could address these
issues, offering a more efficient, accurate, and cost-effective solution for sustainability compliance in
architecture.

7. Research Enquiry/ Tentative Thesis Title
“Development and Validation of a Climatically Adaptable Automated
Sustainability Compliance System for Indian Architecture”
Research Enquiries:
• How can computational algorithms be customized for effective sustainability compliance across different climatic zones
of India?
• What are the specific climatic challenges and requirements for automated sustainability compliance in Indian
architecture?
• How can regional architectural expertise and climatic-specific case studies enhance the algorithm's effectiveness in
diverse Indian climatic contexts?
Computational
Design
Architectural
Design
Sustainability

8. Mixed Method Approach
Quantitative Phase:
1.Enhanced Data Collection:
1. Collect a broad range of building designs from various climatic zones in India, including
tropical, arid, temperate etc. (e.g. criterias: Energy Efficiency, Water Usage, Material
Sustainability).
2. Focus on obtaining designs that span a spectrum of sustainability standards and regional
architectural features.
2.Algorithm Refinement:
1. Develop algorithms that consider regional climatic adaptations, such as passive cooling in hot
climates and insulation in cold regions.
2. Translate sustainability standards into quantifiable metrics, incorporating region-specific PDT
Schemas and adaptations.
3.Advanced Model Training and Testing:
1. Utilize machine learning techniques to analyze and predict sustainability compliance, ensuring
that models are trained on diverse data reflecting India’s climatic zones.
2. Test the algorithm’s accuracy in identifying compliance and specific areas of non-compliance.
4.Comprehensive Statistical Analysis:
1. Employ statistical tools to evaluate system performance, focusing on accuracy, precision,
recall, F1-score, and other relevant metrics within different climatic contexts.

9. Mixed Method Approach
Qualitative Phase:
1.Targeted Expert Interviews:
1. Interview architects, sustainability experts, and engineers who have specific knowledge and
experience in designing for different climatic zones of India.
2.Focused Focus Groups:
1. Organize focus groups with stakeholders from various regions to understand their
perspectives on regional sustainability challenges and the practical application of
sustainability standards.
3.Localized Case Studies:
1. Conduct in-depth case studies of buildings across different climatic zones, assessing how the
system’s findings correlate with real-world sustainability compliance.
Integrating Quantitative and Qualitative Findings:
•Use insights from qualitative research to refine the algorithm, ensuring it aligns with climatic-specific
architectural practices and sustainability needs in India.
•Validate and enhance the system's accuracy and regional applicability by correlating quantitative
results with qualitative findings.

10. Potential PDT Schemas
Climatic Zone Design Strategy Algorithmic Parameters Example Parameters
Tropical Wet (e.g.,
Kerala) Natural Ventilation
Wind pattern analysis, building
orientation optimization
Wind rose data integration,
orientation angle calculations
Rainfall Adaptation
Sloping roof design, drainage
system layout
Roof slope angle calculation,
drainage capacity assessment
Material Selection
Moisture resistance, local
sourcing
Material database querying,
moisture tolerance levels
Solar Exposure
Solar panel placement, shading
optimization
Solar path analysis, shading
device placement algorithm
Tropical Dry (e.g.,
Rajasthan) Heat Gain Reduction
Insulation quality, reflective
materials
Insulation R-value assessment,
reflectivity analysis
Passive Cooling
Courtyard design, natural airflow
optimization
Courtyard dimension calculations,
airflow simulation
Water Efficiency
Landscaping, irrigation system
design
Drought-resistant plant selection,
irrigation efficiency analysis
Thermal Mass
Material selection for
temperature regulation
Heat capacity database query,
material selection algorithm
Subtropical Humid
(e.g., Uttar
Pradesh) Humidity Control Airflow and ventilation design
Ventilation efficiency analysis,
airflow pattern simulation
Shading Solutions Strategic placement of shades
Solar exposure calculation,
dynamic shading placement
Elevated Design Raised structure parameters
Flood risk analysis, structural
elevation design
Energy-Efficient HVAC HVAC system optimization
Humidity control analysis, energy
consumption modeling

11. Potential PDT Schemas
Climatic Zone Design Strategy Algorithmic Parameters Example Parameters
Mountain (e.g.,
Himachal Pradesh) Thermal Insulation Insulation for walls and roofs
Insulation material selection,
thermal resistance calculation
Snow and Rain Management Roof design for snow/rain
Slope calculation for snow/rain
management, drainage design
Seismic Resistance Structural stability features
Earthquake simulation, structural
reinforcement analysis
Sustainable Heating Integration of heating solutions
Geothermal potential
assessment, heating efficiency
calculation
General
Parameters Sustainability Metrics Energy, water, carbon footprint
Energy simulation, water usage
calculation, carbon footprint
analysis
Local Material Usage
Material sourcing and
sustainability
Local material database search,
sustainability scoring
Cultural/Contextual
Relevance
Design adaptation to local
culture/context
Cultural motifs integration,
context analysis algorithms
Flexibility/Adaptability Design modification capabilities
Modular design algorithms,
adaptive reuse strategies
Cost-Efficiency
Economic analysis of design
choices
Cost-benefit analysis algorithm,
life-cycle cost assessment

12. 12
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