Time is of the essence when it comes to the role of a delay expert in a construction dispute. With cases increasingly involving vast amounts of data, AI has become a formidable tool for delay experts.
However, AI tools are not without their limitations. Human oversight is still crucial to ensure both reasonableness and accuracy in legal proceedings.
Forensic delay analysis in the construction industry involves a detailed process carried out by experts to determine the causes and extent of delays in a project schedule. This process includes the following steps:
1. Data collection and review: Gathering all relevant information related to the project.
2. Source validation: Validating the reliability and accuracy of the available schedules.
3. Delay event identification: Pinpointing specific events that caused delays.
4. Delay event analysis: Examining the nature and impact of these events.
5. Delay quantification: Measuring the duration and extent of the delays.
6. Responsibility assessment: Determining which parties are responsible for each delay.
7. Financial cost calculation: Estimating the financial impact of the delay period.
Traditional methods of forensic delay analysis involve manually reviewing extensive project documentation, a process that is time-consuming by nature. Similarly, identifying specific delay events often requires a painstaking review of schedules and logs, which can take weeks or even months.
AI and natural language processing (NLP) tools optimize this process by automating data extraction and analysis, following the processes and steps established by the expert. For instance, some AI tools can quickly parse large volumes of data using sophisticated queries to identify relevant information and anomalies. These automation tools not only speed up the research process – by streamlining data collection and review– but enhance the expert’s ability to gain a clearer understanding of project issues.
Another technology enabling experts to more effectively review large datasets is machine learning. Traditional methods reliant on static historical data and manual analysis can miss emerging trends or overlook subtle indicators of potential problems. In contrast, some AI tools can use machine learning algorithms to continuously analyze both historical and current project data. These algorithms can detect deviations from the norm and identify (based on the frequency of occurrence of certain events) common delay causes such as supply chain disruptions, weather events or resource constraints.
Additionally, machine learning models can assess the reasonableness of project schedules, comparing them against historical performance data and industry benchmarks. From a forensic delay analysis perspective, these tools can simulate various scenarios to evaluate the impact of different scheduling assumptions and constraints, assisting experts in determining whether proposed schedule updates were realistic. They also help identify potential optimism bias and check if project timelines were achievable and aligned with performance trends.
Another increasingly used technology used by delay experts is BIM modelling, particularly 4D BIM Modelling. By integrating time with 3D project models, advancements such as 4D Building Information Modelling (BIM) mark a leap forward from traditional scheduling methods. Unlike static Gantt charts, which often lack insight into how delays impact the overall project, 4D BIM and Augmented Reality software are dynamic tools that allow users to visualize as-built progress over time. This interactive capability helps identify potential scheduling conflicts and impacts of delays, supporting delay management and mitigation.
While the core methodologies of forensic delay analysis – as outlined by industry guidelines such as the Society of Construction Law, Association for the Advancement of Cost Engineering, and American Society of Civil Engineers – will continue to slowly evolve, advancements in the tools and technologies used by experts have the potential to transform the construction dispute landscape.
With AI, NLP, machine learning and 4D BIM more integrated into forensic delay analysis, experts will be able to deliver opinions and reports quicker, with more evidence and data available to support the experts’ findings. Over time, contractors and owners will find these innovations will also enhance project performance, making delays easier to predict and manage. Consequently, the construction industry can expect greater transparency, fewer disputes, and more efficient project outcomes in the long term.
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