IT INTERN

AN INTERNSHIP REPORT

Submitted by

Seju Manishkumar Govindbhai

210160111010

In partial fulfilment for the award of the degree of

BACHELOR OF ENGINEERING
In
Electronics and Communication Engineering
Government Engineering College, Modasa

Gujarat Technological University,

Ahmedabad
January 20, 2025 – April 20, 2025
 GOVERNMENT ENGINEEERING COLLEGGE MODASA
Shamlaji Road, Aravalli District, Modasa

CERTIFICATE

This is to certify that the project report submitted along with the project

entitled Internship has been carried out by SEJU MANISHKUMAR

GOVINDBHAI under my guidance in partial fulfilment for the degree of

Bachelor of Engineering in ELECTRONICS & COMMINCATION

ENGINEERING , 8th Semester of Gujarat Technological University,

Ahmadabad during the academic year 2024-25.

Internal Guide Head of the Department

Prof. Priyank V Patel Prof H.R C R Parekh

 GOVERNMENT ENGINEEERING COLLEGGE MODASA
Shamlaji Road, Aravalli District, Modasa

DECLARATION

I hereby declare that the Internship / Project report submitted along with the

Internship entitled Internship submitted in partial fulfilment for the degree of

Bachelor of Engineering in ELECTRONICS & COMMUNICATION

ENGINEERING to Gujarat Technological University, Ahmedabad, is a

bonafide record of original project work carried out by me / us at I-

SOFTRENDS SYSTEM under the supervision of Mr TILAK

MORADIYA and that no part of this report has been directly copied from

any students’ reports or taken from any other source, without providing due

reference.

Name of Student Signature of student

Seju Manishkumar Govindbhai

 Acknowledgement
I am honored to have completed my internship at I-softrends System, a forward-thinking

and innovative startup. This experience significantly expanded my technical knowledge

and provided hands-on exposure to real-world projects, contributing immensely to my

professional growth.

I am deeply grateful to my internal guide, Prof. Priyank V Patel, for her consistent

support and valuable guidance. Her mentorship helped me understand industry standards

and sharpen my problem-solving skills through practical insights and constructive

feedback.

My heartfelt thanks to Mr. Tilak Moradiya , my external mentor, for his expert guidance

and constant encouragement. I also extend my appreciation to the entire I-softrends team

for fostering a collaborative environment that made this journey enriching and

memorable.

Sincerely,

Seju Manish
 ABSTRACT

The construction industry heavily relies on architectural and structural drawings in formats
such as AutoCAD, PDFs, and images for planning and execution. Traditionally, engineers
manually analyze these drawings to ensure compliance with industry standards, such as the
Bureau of Indian Standards (BIS). However, this manual process is time-consuming and
prone to human errors, leading to inefficiencies and potential safety risks. To address these
challenges, this internship project focuses on developing an AI-powered automation system
for construction drawing analysis.

The primary objectives of this project are to automate the extraction, classification, and
validation of structural shapes from engineering drawings using computer vision and
machine learning techniques. The system will accurately identify structural components,
including reinforcement bars, beams, columns, stirrups, and slabs. Extracted shapes will be
compared against a reference library to ensure conformity and detect discrepancies such as
missing or duplicate components. Additionally, BIS compliance checks will be integrated
for automatic validation of reinforcement and concrete elements, including minimum
concrete cover, stirrup spacing, and rebar diameters. The system will flag any deviations
or missing components and provide corrective recommendations. Furthermore,
barcode/QR code scanning will be incorporated to verify drawing accuracy and ensure
alignment between the construction site and approved plans. A dashboard-based
visualization will be provided for extracted shapes, compliance reports, and detected issues,
enabling efficient review and decision-making. Reinforcement learning techniques will be
employed to continuously improve the AI model's accuracy in recognizing complex
construction patterns.

Ultimately, this project aims to enhance efficiency, accuracy, and compliance in

construction drawing analysis by automating repetitive tasks, accelerating decision-
making, improving project quality, and optimizing resource utilization. The goal is to
transform traditional construction workflows into a more data-driven, AI-assisted
approach.
 List of Figures
Figure 1 About Company ................................................................................................. 1

Figure 2 Vision ................................................................................................................ 2

Figure 3 Technology Skills and Tools Overview............................................................... 4

Figure 4 Challenges ......................................................................................................... 5

Figure 5 Future Aspirations .............................................................................................. 6

Figure 6 Implementation .................................................................................................. 9

Figure 7 InternShip Timeline ......................................................................................... 10

Figure 8 Project TimeLine ............................................................................................. 11

Figure 9 Problems and Weakness of the Current System ................................................ 13

Figure 10 Activities........................................................................................................ 16

Figure 11 Unified Structure............................................................................................ 17

Figure 12 Overview of System Components .................................................................. 19

Figure 13 Project and Techniques Overview .................................................................. 20

Figure 14 Flow Chart ..................................................................................................... 24

Figure 15 UseCase Diagram .......................................................................................... 26

Figure 16 Class Diagram ............................................................................................... 27

Figure 17 Sequence Diagram ......................................................................................... 28

List of Abbreviation
 Table of Contents
CHAPTER 1: INTRODUCTION ................................................................................. 1

1.1 Overview of the Company ..................................................................................... 1

1.2 About the Company ............................................................................................... 1

1.3 Company Mission and Vision ................................................................................ 2

1.3.1. Mission .......................................................................................................... 2

1.3.2. Vision ............................................................................................................ 2

1.4 Scope of Work ....................................................................................................... 2

1.5 Services Provided by BuniyadByte......................................................................... 3

1.6 Technologies Used at BuniyadByte ........................................................................ 3

1.7 My Role and Responsibilities as an IT Intern ......................................................... 4

1.8 Key Learnings and Contributions ........................................................................... 4

1.8.1. Technical Skills Gained .................................................................................. 4

1.8.2. Soft Skills Developed ..................................................................................... 5

1.8.3. Challenges Faced & Solutions ........................................................................ 5

1.9 Impact of My Internship ......................................................................................... 6

1.10 Future Aspirations ................................................................................................ 6

CHAPTER 2: INTRODUCTION TO INTERNSHIP .................................................. 7

2.1 Project / Internship Summary ................................................................................. 7

2.2 Purpose .................................................................................................................. 7

2.3 Objective ............................................................................................................... 7

2.4 Scope ..................................................................................................................... 8

 2.5 Technology and Literature Review ......................................................................... 8

2.6 Project / Internship Planning .................................................................................. 9

2.6.1 Project / Internship Development Approach and Justification........................... 9

2.6.2 Project / Internship Effort and Time, Cost Estimation ...................................... 9

2.7 Project / Internship Scheduling ............................................................................. 11

CHPATER 3: SYSTEM ANALYSIS........................................................................... 12

3.1 Study of Current System ...................................................................................... 12

3.2 Problems and Weaknesses of the Current System ................................................. 12

3.3 Requirements of the New System......................................................................... 14

3.4 System Feasibility ................................................................................................ 14

3.4.1 Does the system contribute to the overall objectives of the organization?....... 14

3.4.2 Can the system be implemented using the current technology and within the

given cost and schedule constraints? ...................................................................... 14

3.4.3 Can the system be integrated with other systems already in place? ................ 15

3.5 Activity / Process in the New System / Proposed System...................................... 15

3.6 Features of the New System / Proposed System ................................................... 16

3.7 List of Main Modules / Components / Processes / Techniques of the New System /

Proposed System ....................................................................................................... 18

3.8 Selection of Hardware / Software / Algorithms / Methodology / Techniques /

Approaches and Justification...................................................................................... 19

CHAPTER 4: SYSTEM ANALYSIS .......................................................................... 21

4.1 TECHNICAL FEASIBILITY............................................................................... 21

4.2 ECONOMICAL FEASIBILITY ........................................................................... 21

4.3 FUNCTIONS OF THE SYSTEM ........................................................................ 22

4.3.1 Flow Chart ........................................................................................................ 22

4.3.2 Use Case Diagram............................................................................................. 25

4.4 DATA MODELING ............................................................................................. 25

4.4.1 Activity Diagram............................................................................................... 25

4.4.2 Class Diagram................................................................................................... 26

4.4.4 Sequence Diagram ............................................................................................ 28

4.5 FUNCTIONAL MODELING............................................................................... 28

4.5.1 Data Dictionary ................................................................................................. 29

4.5.2 Data Flow Diagram (DFD)................................................................................ 29

 210160107061 INTRODUCTION

CHAPTER 1: INTRODUCTION

1.1 Overview of the Company

BuniyadByte is a technology-driven startup focused on developing innovative IT solutions

to streamline various industry processes. The company aims to leverage advanced
technologies, including artificial intelligence, machine learning, and automation, to
enhance efficiency and optimize workflow operations. As a growing startup, BuniyadByte
provides a dynamic work environment where professionals and interns gain hands-on
experience with real-world projects.

1.2 About the Company

 Name: BuniyadByte
 Founded Year:2023
 Founder: Mr. Himaanshu Bhavsar
 External Mentor: Mr. Himaanshu Bhavsar
 Internal Guide: Prof. Rinkal J. Prajapati
 Contact Number: +919099724363
 Email ID: buniyadbyte@gmail.com
 Website: https://www.buniyadbyte.com/
 Address: 120, Yashkunj Society, Opp. Prabhat Chawk, Ghatlodia, Ahmedabad

380061, Gujarat, India.

Figure 1 About Company

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1.3 Company Mission and Vision

1.3.1. Mission

BuniyadByte aims to bring technological innovation by developing AI-driven automation

solutions that improve efficiency across various industries, particularly in construction and
IT sectors.

1.3.2. Vision

The company envisions transforming traditional workflows into data-driven, AI-

assisted processes, enabling businesses to achieve greater accuracy, efficiency, and
scalability.

Figure 2 Vision

1.4 Scope of Work

BuniyadByte focuses on AI-powered automation, data processing, and software

development to optimize industry processes. As a startup, the company provides an ideal

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platform for innovation and research, enabling professionals and interns to work on real-
world challenges.
During my internship, I worked on a project related to AI-driven automation for
construction drawing analysis, where I contributed to:
 Developing an AI-based system to automate the extraction, classification, and
validation of structural shapes from engineering drawings.
 Enhancing accuracy by reducing manual intervention and improving compliance
with industry standards such as the Bureau of Indian Standards (BIS).
 Implementing computer vision to identify reinforcement bars, beams, columns,
stirrups, and slabs.
 Integrating a compliance framework to validate drawings and detect missing or
duplicate components.

1.5 Services Provided by BuniyadByte

BuniyadByte specializes in:

 AI-Powered Automation
 Computer Vision-Based Analysis
 Data Processing and Analytics
 Cloud-Based IT Solutions
 Software Development and Modern API Integration
 Machine Learning and Predictive Modeling

1.6 Technologies Used at BuniyadByte

 Programming Languages: Python, Java

 AI/ML Frameworks: TensorFlow, PyTorch, OpenCV
 Cloud Platforms: AWS, Google Cloud
 Software Development Tools: Django, Flask

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Figure 3 Technology Skills and Tools Overview

1.7 My Role and Responsibilities as an IT Intern

As an IT Intern at BuniyadByte, my primary responsibilities included:

 Developing AI models for construction drawing analysis.
 Implementing computer vision techniques to extract and classify shapes from
AutoCAD/PDF files.
 Testing and validating extracted shapes against reference datasets.
 Integrating compliance checks based on BIS standards.
 Collaborating with mentors and team members to refine the AI system and
improve its accuracy.

1.8 Key Learnings and Contributions

1.8.1. Technical Skills Gained

 AI and Machine Learning

 Computer Vision (Shape Detection, Image Processing)
 Python Programming and Automation

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 Database Management and API Development

1.8.2. Soft Skills Developed

 Problem-Solving and Critical Thinking

 Team Collaboration and Communication
 Time Management and Project Planning

1.8.3. Challenges Faced & Solutions

 Challenge: Low accuracy in shape extraction from complex engineering

drawings.
 Solution: Fine-tuned the AI model using reinforcement learning and
enhanced image pre-processing techniques.
 Challenge: Ensuring compliance with BIS standards dynamically.
 Solution: Integrated a rule-based validation system with AI-assisted
recommendations.

Figure 4 Challenges

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1.9 Impact of My Internship

 Contributed to developing an AI-powered system for construction drawing

validation.
 Helped improve automation in detecting reinforcement bars, reducing manual
efforts.
 Enhanced compliance framework by integrating BIS validation checks.
 Optimized project workflow by implementing an AI-driven decision-making
system.

1.10 Future Aspirations

This internship has provided me with valuable experience in AI, automation, and
construction technology. Moving forward, I aim to:
 Expand my expertise in AI and machine learning for real-world applications.
 Contribute to AI-driven projects that optimize industry workflows.
 Continue collaborating with professionals to build data-driven, scalable
solutions.

Figure 5 Future Aspirations

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 210160107061 COMPANY OVERVIEW

CHAPTER 2: INTRODUCTION TO INTERNSHIP

The construction industry heavily relies on architectural and structural drawings for
planning and execution. Traditionally, engineers manually analyze these drawings to
ensure compliance with industry standards, such as the Bureau of Indian Standards
(BIS). However, this manual process is time-consuming and prone to human errors,
leading to inefficiencies and potential safety risks.
To address these challenges, my internship at BuniyadByte focused on developing an AI-
powered automation system for construction drawing analysis. This system leverages
computer vision, machine learning, and automation techniques to analyze engineering
drawings, extract structural elements, validate compliance, and provide actionable insights
for engineers.

2.1 Project / Internship Summary

The internship at BuniyadByte focused on AI-powered automation for construction

drawing analysis. The primary objective was to develop an intelligent system that
automates the extraction, classification, and validation of structural shapes from AutoCAD,
PDF, JPG, and PNG drawings. This project aimed to enhance accuracy, efficiency, and
compliance with industry standards like BIS.

2.2 Purpose

The purpose of this project was to reduce the manual effort involved in analysing
construction drawings by leveraging artificial intelligence, computer vision, and machine
learning techniques. This automation aimed to improve decision-making, minimize human
errors, and accelerate the construction workflow.

2.3 Objective

 Automate the extraction, classification, and validation of structural components.

 Utilize AI to identify and compare structural elements like reinforcement bars, beams,
columns, stirrups, and slabs.
 Ensure compliance with BIS standards by integrating validation checks.

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 Implement QR barcode scanning to verify drawing accuracy.

 Develop a dashboard for graphical reports, compliance analysis, and AI-based
recommendations.

2.4 Scope

Capabilities:
 AI-based shape detection and classification from multiple file formats.
 Automated compliance checks against industry standards.
 Dashboard visualization and real-time reporting.
Limitations:
 Requires pre-trained AI models, which may need regular updates for better accuracy.
 Accuracy depends on the quality of input drawings.
 Not all non-standard construction elements may be recognized automatically.

2.5 Technology and Literature Review

The project was implemented using:

 Programming Languages: Python
 Libraries & Frameworks: OpenCV, TensorFlow, PyTorch, NumPy, Pandas
 Database Management: SQLite, PostgreSQL
 Tools: AutoCAD, OCR APIs, Machine Learning models
 Reference Standards: BIS codes for construction compliance

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Figure 6 Implementation

2.6 Project / Internship Planning

2.6.1 Project / Internship Development Approach and Justification

The development followed an Agile methodology, allowing iterative improvements based

on testing and feedback. A combination of supervised and unsupervised learning models
was used for shape detection and classification.

2.6.2 Project / Internship Effort and Time, Cost Estimation

Effort estimation was based on:

 Data collection and preprocessing (2 weeks)

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 Model training and fine-tuning (4 weeks)

 System integration and dashboard development (3 weeks)
 Testing and validation (3 weeks)
 Total estimated duration: 12 weeks
2.6.3 Roles and Responsibilities

 IT Intern (Azaz Shaikh): Responsible for AI model development, shape extraction,

compliance validation, and dashboard implementation.
 Mentor (Himaanshu Bhavsar): Provided guidance on AI strategies and implementation.
 Internal Guide (Prof. Rinkal J. Prajapati): Supported academic research and
development.
2.6.4 Group Dependencies

 Dependency on AutoCAD files for accurate shape extraction.

 AI model requires continuous training with diverse datasets.
 Integration with industry compliance standards for validation.

Figure 7 InternShip Timeline

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2.7 Project / Internship Scheduling

The project followed a structured timeline with key activities:

Figure 8 Project TimeLine

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CHPATER 3: SYSTEM ANALYSIS

3.1 Study of Current System

The current system for analyzing construction drawings relies heavily on manual
inspection and verification. Engineers and architects review AutoCAD, PDF, JPG, and
PNG drawings to ensure compliance with industry standards such as BIS. The manual
process involves identifying structural components, checking dimensions, verifying
reinforcement placement, and ensuring regulatory compliance. This approach is time-
consuming, prone to human error, and lacks real-time validation.

3.2 Problems and Weaknesses of the Current System

 High Time Consumption: Manual analysis takes significant time, leading to

project delays.
 Human Errors: Prone to miscalculations, omissions, and misinterpretations.
 Lack of Standardization: Different engineers may interpret designs differently,
leading to inconsistencies.
 Limited Scalability: As project complexity increases, manual verification
becomes impractical.
 Compliance Risks: Missing or incorrect structural elements may violate BIS
codes, leading to rework and penalties.

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Figure 9 Problems and Weakness of the Current System

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3.3 Requirements of the New System

 Automated Extraction & Classification: AI-powered detection of structural

shapes from drawings.
 Real-time Compliance Checks: Integration with BIS codes for validation.
 Dashboard for Visualization: Interactive reports on compliance, detected issues,
and corrective actions.
 Barcode/QR Code Integration: Verification of drawing authenticity using
scanning technology.
 AI Copilot for Reporting: Automated generation of graphical reports for
decision-making.
 Predictive Analysis: AI-based predictions for concrete cover, reinforcement
placements, and structural integrity.

3.4 System Feasibility

3.4.1 Does the system contribute to the overall objectives of the

organization?

Yes, the system enhances efficiency, accuracy, and compliance in construction projects. It
reduces manual effort, minimizes errors, and speeds up the verification process.

3.4.2 Can the system be implemented using the current technology and

within the given cost and schedule constraints?

Yes, the system is designed using readily available technologies such as Python, OpenCV,

TensorFlow, and database management systems like SQLite/PostgreSQL. The project

timeline ensures phased development and integration, keeping costs manageable.

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3.4.3 Can the system be integrated with other systems already in place?

Yes, the system is designed for seamless integration with existing CAD tools, compliance
databases, and project management dashboards. APIs allow interoperability with third-
party construction management software.

3.5 Activity / Process in the New System / Proposed System

 Step 1: Extract shapes from AutoCAD, PDF, JPG, and PNG files.

 Step 2: Compare extracted shapes with an existing library for duplicates.

 Step 3: Apply AI-based classification and compliance checks.

 Step 4: Generate graphical reports and validation summaries.

 Step 5: Use QR code/barcode scanning to verify drawing accuracy.

 Step 6: Predict concrete cover requirements based on structural data.

 Step 7: Display analysis results on an interactive dashboard.

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Figure 10 Activities

3.6 Features of the New System / Proposed System

 Automated Shape Detection: Identifies reinforcement bars, beams, stirrups,

slabs, etc.
 Real-time BIS Compliance Validation: Checks for missing components and
structural issues.
 AI-powered Copilot: Provides recommendations for corrections and
improvements.
 Interactive Dashboard: Displays detected shapes, validation results, and
compliance issues.

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 Predictive Analytics: AI-based forecasting of structural integrity and required
cover.
 Barcode/QR Code Integration: Enhances accuracy in drawing verification.

Figure 11 Unified Structure

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3.7 List of Main Modules / Components / Processes / Techniques of the New

System / Proposed System

 Shape Extraction Module: Extracts structural components from images and

drawings.
 AI-based Classification Module: Uses machine learning models to identify
construction elements.
 Compliance Validation Module: Cross-checks extracted elements against BIS
standards.
 Visualization Dashboard: Displays analytical reports and flagged issues.
 AI Copilot: Generates graphical reports and suggests corrective actions.
 QR Code Scanner Module: Ensures accurate drawing authentication.
 Predictive Analysis Module: Forecasts required concrete cover and
reinforcement parameters.

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Figure 12 Overview of System Components

3.8 Selection of Hardware / Software / Algorithms / Methodology /

Techniques / Approaches and Justification

 Hardware: High-performance computing system with GPU acceleration for AI

processing.
 Software: Python, OpenCV, TensorFlow, PyTorch, PostgreSQL, SQLite.
 Algorithms:
o Optical Character Recognition (OCR) for text extraction.
o Machine Learning classifiers for shape identification.
 Methodology: Agile development methodology for iterative improvements.
 Approach: AI-driven automation with manual validation for accuracy
enhancement.

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 Justification: The selected tools and techniques ensure scalability, efficiency, and
accuracy while maintaining cost-effectiveness.


Figure 13 Project and Techniques Overview

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CHAPTER 4: SYSTEM ANALYSIS

System Analysis is a crucial phase in the development of AI-driven automation for civil
engineering. It involves evaluating the feasibility of the system, defining its functions, and
developing various diagrams to model its structure, behavior, and data flow.

4.1 TECHNICAL FEASIBILITY

Technical feasibility assesses whether the system can be successfully developed using the
available technology, tools, and expertise.
Key Factors for Technical Feasibility:
1. Software Readiness – The system uses Python, OpenCV, AutoCAD APIs, and
TensorFlow/PyTorch for AI-based shape detection and validation.
2. Hardware Requirements – The system runs on high-performance machines with
GPUs, cloud servers, and high-speed processors for efficient AI computations.
3. Integration Possibilities – The system can seamlessly integrate with AutoCAD,
cloud storage, mobile apps, and reporting dashboards.
4. Scalability – The modular architecture allows easy enhancements, including
additional AI models, compliance checks, and real-time site monitoring.
Given these factors, the system is technically feasible for deployment in civil engineering
applications.

4.2 ECONOMICAL FEASIBILITY

Economic feasibility evaluates the cost-effectiveness of the proposed system, considering

the financial investment, operational costs, and long-term benefits.
Cost Analysis:
1. Initial Investment – Costs include software licensing, AI model development,
and cloud-based infrastructure.
2. Operational Costs – Includes server maintenance, data storage, and periodic
AI model training/upgrades.
3. Cost Savings – Reduces the need for manual labor in shape extraction,
compliance validation, and report generation, saving significant costs.

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4. Time Efficiency – Faster processing reduces project timelines, leading to reduced
project delays and cost overruns.
5. Return on Investment (ROI) – The automation ensures higher accuracy,
regulatory compliance, and better project monitoring, improving efficiency and
cost-effectiveness.
The long-term cost savings outweigh the initial investment, making the system
economically feasible.
4.3 FUNCTIONS OF THE SYSTEM

The system performs multiple functions in the automation of engineering drawing analysis:
1. AutoCAD/PDF Shape Extraction – AI algorithms detect and categorize shapes
from engineering drawings.
2. AI-based Compliance Verification – The system validates extracted shapes
against predefined standards (BIS codes).
3. Barcode/QR Code Validation – Ensures that extracted shapes match construction
drawings and field implementations.
4. Graphical Reports and Dashboard Visualization – Provides real-time insights,
analytics, and compliance status to engineers and auditors.

4.3 FUNCTIONS OF THE SYSTEM

The system performs multiple functions in the automation of engineering drawing analysis:
1. AutoCAD/PDF Shape Extraction – AI algorithms detect and categorize shapes
from engineering drawings.
2. AI-based Compliance Verification – The system validates extracted shapes
against predefined standards (BIS codes).
3. Barcode/QR Code Validation – Ensures that extracted shapes match construction
drawings and field implementations.
4. Graphical Reports and Dashboard Visualization – Provides real-time insights,
analytics, and compliance status to engineers and auditors.

4.3.1 Flow Chart

The Flow Chart represents the step-by-step workflow of the system:

1. User uploads engineering drawings (AutoCAD/PDF).

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2. System extracts shapes using AI-based detection.
3. Compliance check is performed based on BIS standards.
4. Errors and discrepancies are flagged for correction.
5. Final report is generated and stored in the database.

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Figure 14 Flow Chart

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4.3.2 Use Case Diagram

The Use Case Diagram shows interactions between system components and users
(engineers, auditors, site supervisors). It highlights primary use cases like uploading
drawings, analyzing shapes, validating standards, and generating reports.

4.4 DATA MODELING

Data modeling defines how system data is structured, stored, and processed. It ensures
that the system can efficiently manage engineering drawings, extracted shapes,
compliance results, and user interactions.

4.4.1 Activity Diagram

The Activity Diagram represents the sequence of operations in the system,

including:

1. User uploads an engineering drawing.

2. AI extracts shapes and analyzes them.

3. System checks compliance with predefined standards.

4. Results are stored, and reports are generated.

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Figure 15 UseCase Diagram

4.4.2 Class Diagram

The Class Diagram defines system objects and their relationships. Key
classes include:

 DrawingFile (stores uploaded drawings)

 ShapeExtractor (AI-based shape detection)

 ComplianceChecker (validates shapes)

 ReportGenerator (generates compliance reports)

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 User (engineers, auditors, site supervisors)

Figure 16 Class Diagram

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4.4.4 Sequence Diagram

The Sequence Diagram represents interactions between system components

over time:

1. User uploads a drawing.

2. System extracts shapes.

3. AI validates compliance.

4. Results are stored and displayed.

Figure 17 Sequence Diagram

4.5 FUNCTIONAL MODELING

Functional modeling focuses on how the system processes data and

performs its core functions.

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4.5.1 Data Dictionary

The Data Dictionary defines key variables and data fields:

Data
Field Name Description
Type

File_ID Unique identifier for uploaded drawing Integer

Type of shape detected (circle, beam,

Shape_Type String
reinforcement)

Compliance_Status Pass/Fail result of shape validation Boolean

User_ID ID of the engineer or auditor Integer

Timestamp Date and time of data processing DateTime

4.5.2 Data Flow Diagram (DFD)

The Data Flow Diagram (DFD) illustrates the flow of information within the
system and how data is processed at different stages. It focuses on the
transformation of input data into meaningful output through various system
components.
Level 0 DFD (Context Level)
At this highest level, the system is depicted as a single process, showing how
users interact with it.
 External Entities:
o User (Engineer, Auditor, Supervisor)
 Process:
o Upload and Analyze Drawing
 Data Flows:
o Engineering Drawing (Input from User)

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o Processed Report and Compliance Status (Output to User)

Level 1 DFD (Decomposition Level)

This level provides more detail about the internal processes of the system.
1. Process 1.0 – Upload Drawing
o Input: AutoCAD/PDF drawing files
o Output: Stored drawing file in system database
o Data Store: Drawing Repository
2. Process 2.0 – Shape Extraction
o Input: Drawing file from repository
o Output: Identified shapes
o Data Store: Extracted Shapes Database
3. Process 3.0 – Compliance Validation
o Input: Extracted shapes
o Output: Compliance results (flags, errors, passes)

Gujarat Technological University 30 Government Engineering College, Modasa

 210160107061 CONCLUSION

o Data Store: Compliance Results

4. Process 4.0 – Report Generation
o Input: Compliance data
o Output: Graphical and tabular reports
o Data Store: Generated Reports

Gujarat Technological University 31 Government Engineering College, Modasa

 210160107061 CONCLUSION

Data Stores:
 Drawing Repository: Stores uploaded drawing files
 Extracted Shapes Database: Stores AI-detected shapes
 Compliance Results: Contains validation outcomes
 Generated Reports: Stores final compliance reports and visuals

Gujarat Technological University 32 Government Engineering College, Modasa

 210160107061 CONCLUSION

Gujarat Technological University 33 Government Engineering College, Modasa