StructAssist

Elevator Pitch: StructAssist transforms the construction industry with its AI-powered platform, leveraging the cutting-edge DKNN model to predict the bearing capacity of CFSTs with unprecedented accuracy. Ensuring safer, more efficient, and cost-effective construction projects, we empower engineers to design with confidence and innovate without limits.

Concept

A machine learning-driven software platform to predict the bearing capacity of Concrete-Filled Steel Tubes (CFSTs) for the construction industry.

Objective

To optimize construction designs by accurately predicting the structural capacity of CFSTs, reducing material waste and enhancing safety.

Solution

Using the Domain Knowledge Enhanced Neural Network (DKNN) model for accurate, reliable predictions of CFST bearing capacity, incorporating advanced feature engineering techniques.

Revenue Model

Subscription-based access for construction companies, engineering firms, and educational institutions. Additional consultancy services for customized projects.

Target Market

Construction companies, civil engineering firms, structural engineers, architectural firms, and universities with civil engineering departments.

Expansion Plan

Initially target domestic markets with high construction activity, then expand globally. Introduce related services like structural health monitoring.

Potential Challenges

High initial R&D costs, ensuring constant model updates with new data, and convincing traditional industries to trust AI predictions.

Customer Problem

Inefficient and often imprecise conventional methods to predict bearing capacity of CFSTs leading to material wastage, increased costs, and potential safety issues.

Regulatory and Ethical Issues

Compliance with international construction standards and regulations. Ethical considerations in accurate and transparent model predictions to avoid structural failures.

Disruptiveness

Revolutionizes structural engineering by integrating machine learning for precise, cost-effective design and construction, reducing material waste and improving safety standards.