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Data-Driven Approaches in Offshore Infrastructure Inspection: From Manual Logs to AI Models

by Dulo Chukwuemeka Wegner, Adumike Kenechukwu Nicholas, Ojoh Odoh
journal cover thumbnail

International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 187 - Number 50
Year of Publication: 2025
Authors: Dulo Chukwuemeka Wegner, Adumike Kenechukwu Nicholas, Ojoh Odoh
10.5120/ijca2025925866

Dulo Chukwuemeka Wegner, Adumike Kenechukwu Nicholas, Ojoh Odoh . Data-Driven Approaches in Offshore Infrastructure Inspection: From Manual Logs to AI Models. International Journal of Computer Applications. 187, 50 ( Oct 2025), 37-48. DOI=10.5120/ijca2025925866

@article{ 10.5120/ijca2025925866,
author = { Dulo Chukwuemeka Wegner, Adumike Kenechukwu Nicholas, Ojoh Odoh },
title = { Data-Driven Approaches in Offshore Infrastructure Inspection: From Manual Logs to AI Models },
journal = { International Journal of Computer Applications },
issue_date = { Oct 2025 },
volume = { 187 },
number = { 50 },
month = { Oct },
year = { 2025 },
issn = { 0975-8887 },
pages = { 37-48 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume187/number50/data-driven-approaches-in-offshore-infrastructure-inspection-from-manual-logs-to-ai-models/ },
doi = { 10.5120/ijca2025925866 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2025-10-23T00:18:35+05:30
%A Dulo Chukwuemeka Wegner
%A Adumike Kenechukwu Nicholas
%A Ojoh Odoh
%T Data-Driven Approaches in Offshore Infrastructure Inspection: From Manual Logs to AI Models
%J International Journal of Computer Applications
%@ 0975-8887
%V 187
%N 50
%P 37-48
%D 2025
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Field verification and maintenance of offshore structures, including subsea pipelines, risers, and wind turbine foundations, are required to keep the operations safe, protect the environment, and prolong the life of the assets. Historically, inspections concentrated on diver commentaries and physical documents and photographs, which, although rudimentary, suffered from subjectivity, inconsistency, and long-range data storage. Software for inspections was a positive development, which, for the first time, enabled standardized performance for data capture, systematization of data and metadata, and integration of ROV and sensor data. Systems like FDVR, COABIS, and SENSE enhanced the ability of data capture and retention and, thereby, improved the decision-making for operators and regulators. In recent years, the offshore sector has been going through a new phase because of the influence of artificial intelligence and machine learning. These models are capable of assisting with anomaly detection, corrosion assessment, and fatigue prediction with abundant multiple untidy data from video, ultrasonic, acoustic, and various environmental sensors. More of these machine-learning systems are used within the digital twin technology and framework, which helps with real-time observation and monitoring, and proactive warning systems. More advanced subsea analytical systems, combined with conventional inspection data, allow operators to transition from unplanned maintenance to proactive predictive maintenance, which optimizes asset management, minimizes unplanned outages, and significantly increases safety. This analyzes the evolution of offshore inspection practices from manual to AI-driven approaches and the continuities between traditional inspection records and modern inspection predictive data systems. While challenges related to data quality and standardization, workforce training, and data security protection are recognized, the potential gains in resilience, cost effectiveness, and regulatory compliance are compelling. Integration of legacy data with digital inspection systems and AI-driven analytics evokes a paradigm shift in offshore infrastructure management by utilizing data as the principal element for safe, economical, and environmentally responsible operations.

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Index Terms

Computer Science
Information Sciences

Keywords

Data-Driven Approaches Offshore Infrastructure Inspection Manual Logs AI Models Digital Transformation Predictive Analytics Machine Learning Deep Learning Computer Vision Sensor Data Big Data Integration Cloud Computing Digital Twin Structural Health Monitoring

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