Subject Matter Expert (Reinforcement & Detailing -BIM)

Job Description

We are looking for an experienced Subject Matter Expert (SME) in Reinforcement and Detailing (BIM) to support the development of AI-powered reinforcement detailing solutions. The ideal candidate will have strong hands-on experience in 3D rebar modelling, 2D reinforcement detailing, and Bar Bending Schedules (BBS) for reinforced concrete (RC) structures.

The role involves creating production-ready reinforcement models, reviewing AI-generated outputs, identifying detailing errors, and translating domain expertise into structured rules that can be implemented by the software development team.

The candidate should have practical experience working with international reinforcement detailing standards, including UK, European, and Australian codes, and exposure to both precast and in-situ construction.

Key Responsibilities

1. 3D Reinforcement Modelling

• Develop accurate, fully reinforced 3D models from unreinforced IFC models, structural drawings, and engineering design briefs.
• Model reinforcement for walls, slabs, beams, columns, foundations, and structural connection zones.
• Handle complex detailing conditions, including openings, edge reinforcement, U-bars, trimmer bars, lap zones, hooks, anchorage, starter bars, embeds, and reinforcement congestion.
• Determine appropriate reinforcement arrangements, including mesh and loose-bar configurations, based on engineering requirements and constructability.
• Ensure reinforcement models comply with project specifications, applicable codes, and engineering intent.

2. 2D Reinforcement Detailing

• Prepare reinforcement drawings, sections, callouts, dimensioned views, tags, and bar schedules.
• Generate accurate Bar Bending Schedules (BBS) and reinforcement details suitable for site execution or precast manufacturing.
• Apply BS 8666 shape codes and relevant reinforcement detailing conventions for other regions.
• Ensure consistency between 3D models, 2D drawings, and fabrication requirements.
• Review drawings for accuracy, completeness, and practical constructability.

3. AI Model Review and Validation

• Review AI-generated reinforcement models and drawings against manually detailed reference models.
• Identify incorrect bar placements, missing reinforcement, code deviations, and non-buildable detailing.
• Document discrepancies and provide clear technical explanations and corrective recommendations.
• Identify complex scenarios and edge cases that may affect automated reinforcement detailing.
• Establish quality benchmarks for AI-generated reinforcement models and detailing outputs.

4. Rule Definition and Knowledge Transfer

• Translate reinforcement detailing expertise into structured rules, conditions, exceptions, and outcomes for implementation by the software team.
• Differentiate between mandatory code requirements and decisions that depend on engineering judgement or project-specific standards.
• Define the inputs required from structural engineers, including concrete cover, bar diameters, spacing, exposure conditions, openings, and connection requirements.
• Collaborate with software engineers and AI specialists to improve the accuracy and reliability of automated detailing.
• Maintain a structured library of detailing rules, correction logs, and technical edge cases.

5. Engineering Coordination

• Interpret structural drawings and design reports to understand reinforcement requirements and engineering intent.
• Identify inconsistencies between design reports, drawings, dimensions, and model geometry.
• Work closely with structural engineers and software development teams to resolve technical queries.
• Participate in regular technical reviews and provide feedback on automation workflows.
• Support continuous improvement of AI-powered BIM and reinforcement detailing processes.

Required Skills and Qualifications

• Minimum 5+ years of hands-on experience in reinforcement detailing for reinforced concrete structures.
• Strong practical experience in 3D rebar modelling using at least one of the following tools:
  
• Autodesk Revit
• Tekla Structures
• Allplan
• CADS RC
• Experience preparing 2D reinforcement drawings, sections, and Bar Bending Schedules (BBS).

• Knowledge of international reinforcement detailing standards, including:
  
• BS 8666 and UK National Annex
• Eurocode 2 (EC2)
• Australian Standard AS 3600
• AS/NZS 4671
• Relevant DIN and SIA standards
• Understanding of both precast and in-situ reinforced concrete construction.
• Strong knowledge of concrete cover, bar spacing, lap lengths, hooks, bend radii, anchorage, starter bars, edge reinforcement, and opening reinforcement.

• Ability to read structural drawings and engineering design reports and translate design intent into practical reinforcement detailing.
• Strong understanding of reinforcement constructability, fabrication requirements, and site execution practices.
• Excellent analytical, problem-solving, and technical communication skills.
• Ability to explain technical detailing decisions clearly to non-structural engineering teams.

Preferred Qualifications

• Experience working on projects governed by UK, European, or Australian reinforcement detailing standards.
• Exposure to multiple reinforcement modelling platforms.
• Experience with IFC models and BIM model exchange workflows.
• Experience in precast factory detailing or in-situ construction projects.
• Previous experience in model checking, technical review, or quality assurance of reinforcement drawings.
• Familiarity with BIM automation, rule-based modelling, computational design, or AI-assisted engineering workflows.
• Experience identifying discrepancies in structural drawings, design reports, and model inputs.

Key Deliverables and Success Metrics

• Production-ready 3D reinforcement models and 2D detailing outputs.
• Accurate Bar Bending Schedules aligned with model geometry.
• Structured review reports documenting AI-generated detailing errors and corrections.
• A documented reinforcement detailing rules library for AI implementation.
• Identification and documentation of complex reinforcement detailing scenarios and edge cases.
• Improved accuracy, consistency, and constructability of AI-generated reinforcement models.
• Effective collaboration with structural and software engineering teams to continuously improve the automation process.