From Static Rates to Live Models: How a Directed Acyclic Graph (DAG)-Based Estimating Engine with AST Formula Parsing Optimises Quarry Sourcing, Haulage, and Production Cost

How Methvin’s Estimating Engine Optimises Construction Methodology, Resources, Logistics, and Project Cost

Technical White Paper

Industry: Heavy Civil | Infrastructure | Transport | Construction Cost Engineering

Executive Summary

The success or failure of major infrastructure projects is usually determined before construction begins. A tender estimate is not simply a financial document—it is a mathematical representation of a proposed construction methodology. The quality of that model directly shapes bid competitiveness, construction margins, resource planning, risk exposure, and commercial outcomes.

For decades, most estimating tools have been built around one limited concept: a construction item has a quantity multiplied by a rate. That approach can produce a tender price, but it does not represent the physical reality of construction, where cost is driven by interconnected variables—material sources, haul distances, equipment cycles, production rates, labour productivity, plant utilisation, site constraints, and market fluctuations.

A capable estimating platform must move beyond answering ‘what does this item cost?’ and answer instead ‘what is the optimum way to construct this project at the lowest commercial risk?’ That requires a first-principles estimating engine—one capable of modelling the relationships between resources, production, logistics, and cost, not just storing historical prices.

This paper sets out the computational architecture of Methvin’s Estimating module. It is a dependency-graph-driven calculation engine, where rates are executable expressions parsed into Abstract Syntax Trees (ASTs), variables are resolved through a strict scope hierarchy, and recalculation is performed via topological traversal of the graph. The two case studies that follow—one at project-planning scale (quarry sourcing strategy), one at work-item scale (pavement installation)—demonstrate the engine operating at opposite ends of the estimate. Crucially, this paper introduces the mechanism by which tender progression and design finalisation (e.g., locking in actual quarry GPS coordinates) automatically cascade through the entire haulage model, updating cycle times, fleet requirements, and unit rates without manual intervention.