Fixed-bottom or floating offshore structures are the foundations, platforms, and associated infrastructure that support oil and gas production systems, offshore wind turbines, and cabling. The remote nature of these structures and the harsh environment with high variability in wind, waves, currents, and weather make construction activity very difficult and unpredictable; variation in the schedule can lead to high construction vessel and personnel costs. The adoption of artificial intelligence using trends observed in historical data can help achieve more accurate construction cost and schedule predictions, reducing the capital expenditure cost of installation.
A resource-significant activity, sometimes called a resource-critical activity or high-resource-demand activity, is an activity on a construction or project schedule that consumes a disproportionately large share of one or more resources compared with others. Plant Design Modelling (PDM) is a digital process that creates and manages a detailed 3D-model of a building's physical and functional characteristics and semantic information, such as cost and schedule. PDM serves as a single source of truth for multidisciplinary activities and, therefore, serves as a rich data source for various construction applications, including project scheduling and cost estimation. Neural networks (NNs), a subset of machine learning algorithms inspired by the human brain, excel at identifying patterns in complex datasets and making predictions, such as forecasting costs based on nonlinear relationships and historical trends.
We used data extracted from Aveva’s-EverythingPDM of an offshore structure modification project, focusing on installation activities to create a dataset for machine learning model training. The extracted information includes geometry, material types, quantities, and spatial relationships between elements. The structured data extracted exhibit nonlinear patterns; therefore, they are analysed using ordinary and regularised linear regression models, as well as neural networks (NNs). NN models show a superior ability to predict the nonlinear nature of offshore construction activities' time.
