Project Introduction
The construction industry faces numerous challenges related to project management, quality control, and communication among stakeholders. To address these issues, our client, ZIEL Construction, embarked on a project to develop an AI-based Construction Project and Quality Management System. This system aims to streamline project management, enhance real-time tracking of project progress, and automate quality control processes using advanced AI and machine learning technologies.
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Discovery Phase Details and Process
The discovery phase was instrumental in understanding the project’s requirements, defining the scope, and ensuring alignment among all stakeholders. Here’s an overview of the process we followed:
Stakeholder Meetings:
- Conducted initial meetings with key stakeholders, including project managers, developers, QA teams, and client representatives, to gather high-level requirements and understand their expectations.
- Documented the objectives, pain points, and desired outcomes from these meetings.
Requirement Gathering Workshops:
- Organized workshops with different user groups to dive deeper into specific requirements. Separate sessions for project developers, managers, QA teams, and end-users helped to gather detailed insights.
- Used techniques like brainstorming, mind mapping, and role-playing to uncover hidden needs and potential challenges.
Market and Competitor Analysis:
- Performed an analysis of existing project management and quality control systems in the market. Identified their strengths and weaknesses to inform the development of a superior solution.
Technology Assessment:
- Evaluated various technologies, tools, and libraries that could be used in the project. Considered factors such as scalability, integration capabilities, and support for AI and machine learning functionalities.
Define Use Cases and User Stories:
- Created detailed use cases and user stories based on the requirements gathered. This helped in visualizing how different user roles would interact with the system and ensured comprehensive coverage of all functionalities.
Prototype Development:
- Developed a prototype or wireframe of the system to provide a visual representation of the proposed solution. This helped in validating the requirements and receiving early feedback from stakeholders.
Project Plan and Timeline:
- Created a detailed project plan outlining the phases of development, key milestones, resource allocation, and timelines. e plan included regular checkpoints and review sessions.
Approval and Sign-Off:
- Presented the findings, prototype, and project plan to the stakeholders for approval. Addressed any concerns or suggestions before proceeding to the development phase.
Libraries Used
The following libraries were utilized to implement various functionalities of the AI-based Construction Project and Quality Management System:
Frontend Libraries:
- React.js: For building a responsive and interactive user interface.
- Bootstrap: To ensuring a consistent and visually appealing design across different devices.
- Axios: For making API requests from the frontend to the backend.
Backend Libraries:
- Nest.js: A progressive Node.js framework for building efficient and scalable server-side applications.
- Express.js: To handle routing and middleware functionalities.
- Mongoose: For object data modeling (ODM) with MongoDB.
AI and Machine Learning Libraries:
- TensorFlow: For implementing machine learning models and training them with project data.
- OpenAI API: To integrate advanced AI capabilities for chat and predictive analytics.
Authentication and Authorization Libraries:
- Passport.js: For handling user authentication and authorization.
- JWT (JSON Web Tokens): For secure token-based authentication.
Other Utility Libraries:
- Lodash: For utility functions and data manipulation.
- Moment.js: For handling date and time operations.
Databases Used
The project utilized two primary databases to ensure efficient data management and security
MongoDB:
- Purpose: Used as the primary database for storing project-related data, user information, tasks, forms, and audit logs.
- Features: Provides scalability, flexibility, and support for complex queries, making it suitable for handling diverse data types and large datasets.
MongoDB Atlas:
- Purpose: A cloud-based database solution that provides automated backups, scalability, and security features.
- Features: Ensures high availability and disaster recovery, reducing the risk of data loss and downtime.
Redis:
- Purpose: Used for caching and real-time data processing to enhance system performance.
- Features: In-memory data structure store, supporting various data types and providing fast access to frequently used data.
