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StructurePal

AI-powered structural concrete optimization

SaaS platform that uses machine learning to optimize reinforced concrete design, reducing concrete volume, costs, and CO2 emissions while accelerating design cycles from weeks to hours.

$1.17M raised 📈 Growth Stage

Founded 2019 • Partnerships with major construction firms and developers (exact number not publicly specified)

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Why This Tool Exists

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The Problem

Structural engineers spend 4+ weeks manually optimizing concrete designs with limited tools, resulting in over-designed structures that increase costs and carbon emissions. Concrete production represents 4-8% of global CO2 emissions, with significant waste from conservative structural designs.

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The Solution

StructurePal applies machine learning to thousands of finite element model iterations to identify optimal structural typology and cross-sections. The platform integrates with Revit workflows to determine minimum concrete volumes, reducing design time to 1-2 hours while cutting concrete usage, costs, and emissions by up to 15%.

How You Use It

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Delivery Method

SaaS

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Integrations

Autodesk Revit

Disciplines

Structural Engineering, Architecture, Construction

Project Phases

Design Development, Construction Documentation, Bidding/Procurement

Project Types

Commercial, Residential, Mixed-Use

Data Transparency

Exactly what this tool uses and how

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Input

What it needs

Required: BIM model, Project specifications

Optional: Design constraints, Client brief requirements, Construction methodology

Formats: Revit (RVT), IFC

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Output

What you get

Format: Optimized structural design with specifications

Fields: Optimized concrete column counts, Recommended slab thicknesses, Reinforced concrete cross-sections, Concrete volume calculations, Cost and carbon impact analysis, Design specifications for construction

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Algorithm

How it works

Model: Proprietary machine learning (model architecture not publicly specified)

Accuracy: Not publicly specified

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Privacy

How your data is protected

Retention: Not publicly specified

Training: Trained on structural design patterns and FEM models

Compliance: Not publicly specified

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API

Integration

Endpoint: Contact vendor for API access

Method: Not publicly documented

Use Cases

• Optimize residential tower concrete design for cost and carbon reduction
• Reduce design time for large commercial buildings from weeks to hours
• Identify and eliminate structural over-design in multi-story projects
• Align structural design with sustainability and carbon reduction goals
• Support design value engineering with data-driven concrete specifications

Pricing

Free

Not available

Pro

Per square meter of floor plan (exact pricing not publicly specified)

Enterprise

Contact for enterprise pricing and customization

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📚 Research Sources & Data Quality Last verified: 2026-02-01

Data Sources (8)

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Verified Data (16)

problem, solution, deliveryMethod, integrations, disciplines, projectPhases, projectTypes, yearFounded, maturityStage, fundingTotal, input-formats, output-fields, output-structure, algorithm-approach, useCases, pricing-model

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Not Found (7)

customerBase-exact-count, api-documentation, privacy-policy-details, compliance-certifications, algorithm-accuracy, data-retention-policy, data-training-consent

Our Commitment: We only include verified data from official sources. If information isn't publicly available, we mark it as "Not publicly specified" rather than guessing.