I am familiar with various contract frameworks such as Time-Material, Lump Sum, Cost Reimbursable, and internationally recognized systems like FIDIC and NEC (UK). This understanding guides how I allocate time, people, and digital tools with a sharp eye on cost, efficiency, and deliverable quality.
In managing the Østlig Ringvej (Eastern Ring Road) Concept Design Project for COWI Denmark, I consistently applied the Activity-Time-Resource (ATR) approach to optimize effort, ensure cost-effectiveness, and deliver high-quality outputs within time schedule. I understood early that time spent on repetitive or manual validation tasks could indirectly escalate project costs, not just financially but also in terms of team productivity.
In Potomac River Tunnel, I prepared the ATR (See Exhibit 3.3)Document for the effort estimate and arrived the cost using FCR (Full cost rate) of the employees based on their Career Level. So I diligently chose the people who are cost effective and suitable for the planned roles in the project such as 2D drafting, modelling or BIM coordination.
A major cost-saving initiative I led involved automating the quality validation of IFC property sets, which are essential for asset handover and client-specific requirements defined in the project’s BIM Execution Plan (BEP). The required data spanned four primary property groups: Identity Data, Asset Referencing System, LCA (Life Cycle Assessment), and Geometry Parameters. Many of these parameters were conditional—for instance, while the Sub-material Ratio (kg/m³) applied to reinforced concrete, it was irrelevant for ballast concrete. Manually checking this across hundreds of model elements using authoring tools like Revit or Civil 3D was not only time-intensive but error-prone.
To solve this, I developed a Python-based automation tool, which parsed IFC files using IfcOpenShell, extracted parameters, and compiled them into structured CSV and Excel formats. This enabled fast, repeatable validation of element properties against regular expressions and project standards. I included conditional logic to mark missing or invalid values—turning what was once a manual and expensive task into a scalable and cost-effective solution.
To make the tool easier for non-coders to use, I developed a user-friendly interface using open-source libraries like Tkinter and Pandas. It lets users run the process without writing code or installing special software. Though designed for this specific project, the tool is flexible enough to adapt to other projects. It was also tested HS2 (High speed Rail 2) project. It’s openly licensed under Creative Commons 0 (CC0) and includes clear instructions, making it easy to reuse and share.
From a commercial standpoint, this initiative:
The memo dated 8 March 2024,(See Exhibit 3.2) which I sent as part of the weekly project status report, reflects how these automation outputs fed into weekly ACC updates, C3D modeling, and discipline coordination. These efforts not only optimized internal efficiency but also reduced the billable effort required for manual QA, aligning with the project’s commercial goals.
By combining an understanding of contract terms, structured resource planning, and cost-saving innovation, I contributed to improving the profitability and technical excellence of the project.
Exhibit 3.1: Python Script I developed to reduce time to verify the IFC outputs for Data Validation thus saving cost
Exhibit 3.2: Weekly memo I sent to Project Stakeholder to give them overview and showing accountability of our team on the progress achieved in the given time
