Delay Analysis
Delay Analysis
Construction delays are an inherent risk in projects of all scales and sectors—arising from factors such as unforeseen site conditions, weather disruptions, material shortages, late design changes, regulatory hold-ups, labour issues, and external events. If not effectively managed, these delays can escalate into costly claims and disputes, disrupting progress and damaging stakeholder relationships.
At Strata, we offer comprehensive forensic delay analysis services designed to investigate, resolve, and even prevent delay claims. Our team combines engineering expertise, advanced planning techniques, and cutting-edge technology—including AI-driven analytics, machine learning, and 4D visualisations—to deliver insight, clarity, and defensible outcomes.
Our delay analysis process goes beyond traditional methods by integrating historical and live project data. With machine learning models, we identify delay patterns across similar project types and flag high-risk activities early. Our 4D visualisations allow stakeholders to see the actual and planned construction sequence in motion—making the causes and impacts of delays easier to communicate and understand.
We apply a full suite of forensic analysis techniques tailored to each project:
As-Planned vs. As-Built Analysis
A baseline comparison between the original programme and the actual construction sequence. AI-powered timeline comparisons help quickly detect deviations and quantify impacts.
Windows Analysis
The schedule is segmented into defined “windows,” with progress and delays analysed within each period. This technique offers precise attribution and is ideal for projects with multiple critical paths.
Time Impact Analysis (TIA)
Evaluates the cause and effect of delay events on the critical path. Widely accepted in legal and contractual contexts, TIA is especially useful for assessing concurrent delays and changes. AI-assisted logic modelling enhances the accuracy of path disruption analysis.
Collapsed As-Built (But-For Analysis)
Simulates what the programme would have looked like had specific delays not occurred—providing clarity around responsibility and concurrency. This is further enhanced by machine learning algorithms that analyse delay impact trends over time.