OFFERINGS
Services

Software Development

Enterprise Software

ECommerce Application

Content Management System

UI/UX Design

Web Support & Maintenance

Artificial Intelligence

Chatbots & Virtual Assistants

Machine Learning

Generative AI

Custom Solutions

Consulting & Integration

Cloud Solutions

Cloud Application Development

Cloud Consulting & Strategy

Migration & Modernization

Cloud Security Services

Mobile Development

Custom App Development

Cross-Platform Development

Wearable App Development

App Optimization & Integration

Testing & QA

Software Prototyping

Proof of Concept Prototypes

User Interface Prototypes

Interactive Prototype

Business Analysis

Requirement Definition & Analysis

Requirement Documentation

High Level User Interface Design
Expertise

Web

.NET

Python

Node Js

JavaScript

Angular, React, Vue

Mobile

iOS

Android

React Native

Flutter

Xamarin

Cloud

Azure

AWS

Google Cloud
Engagement Models

Hire resources

Fixed Cost Model

Time and Material Contract

Hybrid Engagement

Dedicated Team

Staff Augmentation

Our expert team is committed to turning your innovative ideas into powerful, scalable solutions that drive success into digital age.
WORK
COMPANY
- About
- Contact
- Careers
INSIGHTS
Let's talk?

AI-Driven Material Optimization in BIM (Building Information Modeling)

Optimizing Materials for Cost, Sustainability, and Structural Integrity

Goal

Optimize material selection in BIM, balancing cost, sustainability, and structural integrity for improved project efficiency and outcomes.

Solution

By integrating AI, BIM delivers data-driven material recommendations, balancing cost, environmental impact, and structural needs, leading to faster, smarter construction decisions.

Result

The BIM system optimized material choices, leading to a 40% time reduction, 20% cost savings, and better sustainability metrics.

Technologies Used : Neo4j, BIMserver, TensorFlow, AWS Sagemaker

Background

The client is a mid-sized architecture and construction firm known for delivering complex building projects across various sectors. With a strong focus on quality and design innovation, the firm aims to embrace sustainable building practices while maintaining cost efficiency.
Focused on delivering high-quality, sustainable buildings, the firm faced increasing challenges in selecting appropriate materials that met design, cost, and environmental goals. Their current manual process was time-intensive and could not quickly adapt to changing sustainability standards and cost pressures.

Challenge

The client faced challenges in selecting materials that would align with their design vision while meeting modern requirements for cost-efficiency, sustainability, and structural integrity. Their existing Building Information Modeling (BIM) system could not provide intelligent material recommendations, forcing manual, time-consuming decisions that impacted deadlines. The need for an innovative solution to optimize material choices while maintaining project quality was crucial for achieving their goals.

Our Approach

To address the challenges, we implemented an AI-powered enhancement to the client's existing BIM system. This approach enabled the use of advanced algorithms to recommend materials that optimized cost, sustainability, and structural performance. We streamlined the material selection process by integrating real-time data analytics, allowing for faster decision-making. The system provided tailored suggestions based on design requirements and environmental goals. We also incorporated machine learning to continuously improve material choices over time, ensuring both short- and long-term project benefits.

Data Collection and Preparation

The system collects data from multiple sources including material suppliers, sustainability indexes, and past project data stored in the BIM environment. The data is cleaned, processed, and tagged to train the AI models.

AI Model Development

A supervised learning model is employed to recommend materials based on the design specifications provided by the architects and engineers. The model uses the following key features:
Cost Parameters: Historical cost data along with current market trends.
Sustainability Scores: Data on material impact, carbon footprint, and lifecycle assessment.
Structural Integrity: Data from building codes and regulations to ensure that the chosen materials meet required standards.

Pilot Testing

The system was deployed in a pilot phase for a small-scale project to validate its performance. During the pilot, architects and engineers provided feedback on material recommendations, which allowed further model refinement.

BIM System Integration

The AI-powered recommendation engine is integrated into the BIM environment via APIs. As the design evolves within BIM, the AI system continuously updates material recommendations in real-time.

Continuous Learning Loop

Feedback loops are created to continuously update the AI system based on material performance post-project completion. This makes the AI system smarter over time, ensuring that the recommendations evolve with real-world usage data.

Data Extraction for Building Information Modeling(BIM)

Acquiring detailed building data, such as 3D models, performance requirements, and environmental factors, establishes a robust foundation for the AI system.

We aggregated comprehensive building information and parameters the AI system needs to analyze. These include architectural layouts, structural data, energy consumption targets, material preferences, and environmental factors.

3D Model Input: Detailed 3D models of the building, created using CAD or BIM tools are provided. These models include geometry, building components, and any existing material specifications.
Performance Requirements: Information on required performance standards such as sustainability goals (e.g., energy efficiency ratings) and durability constraints.
Environmental Context: Site-specific data including climate, terrain, and local material availability is included to enable material recommendations that are climate-adapted and cost-effective.

AI Model Development

A supervised learning model is employed to recommend materials based on the design specifications provided by the architects and engineers. The model uses the following key features:

Data preprocessing includes feature engineering (tagging material properties with structural integrity values and environmental impact scores).
Random Forests Algorithms are used to build predictive models while Genetic Algorithms (GA) optimization techniques helped to optimize multiple factors.
K-means Clustering techniques helped group similar materials to provide a range of options within the constraints.
Cost Parameters: Historical cost data along with current market trends.
Sustainability Scores: Data on material impact, carbon footprint, and lifecycle assessment.
Structural Integrity: Data from building codes and regulations to ensure that the chosen materials meet required standards.

The system applies multi-objective optimization to find materials that strike a balance between these parameters and uses supervised learning to predict the best materials based on past project data, including material costs, sustainability scores, and structural performance.

Multi-objective optimization ensures that material recommendations are not based solely on one criterion but balance multiple factors like cost, sustainability, and structural integrity. For example, a material with a slightly higher cost but a much better sustainability score might be prioritized.

Reinforcement Learning is employed in advanced scenarios to enable continuous improvement in the decision-making process, where the model "learns" from the real-world outcomes of material selections over time.

The AI material recommendation system was integrated into BIM platform through APIs. As architects or engineers update their designs in the BIM environment, the system continuously pulls in design parameters such as building dimensions, load-bearing requirements, or environmental constraints. It then uses these parameters to update material recommendations in real-time.