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All Journal Jurnal Rekayasa Mesin Rekayasa: Jurnal Penerapan Teknologi dan Pembelajaran
Haris Nubli
University of Surrey
Mechanical Engineering Other

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Numerical Analysis of Oblique Collision on Ship Bow Structure Using the Finite Element Method Juan Abiegnail Sianipar; Aldias Bahatmaka; Song Yeon Hee; Haris Nubli
Jurnal Rekayasa Mesin Vol. 21 No. 2 (2026): Volume 21, Nomor 2, Agustus 2026
Publisher : Mechanical Engineering Department - Semarang State Polytechnic

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Abstract

Oblique bow collision is a complex maritime accident scenario, as the angled impact produces a non-uniform distribution of force and deformation, making the resulting damage substantially harder to predict than perpendicular collisions. Although numerous finite element studies have examined ship collision behavior, most have isolated a single parameter, leaving the combined effect of collision position and angle insufficiently characterized within a single validated numerical approach. This study addresses that gap by evaluating, for the first time within one validated numerical approach, the combined influence of collision position and angle on the structural response of a ferry-to-LPG carrier collision, using Explicit Dynamic Finite Element Method analysis in ANSYS Workbench 2024 R2 (LS-DYNA solver), with two collision positions (P1: main deck; P2: mid-body/bulbous bow) and three angles (90°, 120°, 135°) at 5 m/s across six scenarios, validated against experimental data with a crushing force error of 1.07%. Results show collision position more decisively affects structural resistance than angle, with P2 producing a peak force of 31.44 MN at 90°, over 60% higher than P1. Peak force and internal energy absorption were also found decoupled, with the highest energy absorbed at P2-120° (9.85 MJ) rather than the highest-force case, indicating that the P2-90° zone is structurally critical and warrants priority reinforcement, such as additional transverse framing or localized plate thickening. These findings support treating energy absorption as equally critical to peak force in ship collision safety assessment, in line with Sustainable Development Goal 9 (Industry, Innovation and Infrastructure), and future work is recommended to develop this numerical approach further through variations in collision velocity and distance.
An IoT-Integrated Deep Learning Framework for Automated Crack Detection in Marine Concrete Structures Muhammad Shobari Romadhon; Sujantoko; Haris Nubli
Rekayasa Vol. 23 No. 2 (2025)
Publisher : Universitas Negeri Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15294/rekayasa.v23i2.59539

Abstract

Marine concrete structures such as piers, harbors, and offshore platforms are susceptible to micro-cracking and degradation due to seawater corrosion, repetitive wave action, and temperature fluctuations. Undetected structural damage can compromise load-bearing capacity, accelerate structural failure, and increase maintenance costs. This study proposes an automated real-time crack detection and monitoring system integrating Internet of Things  sensors with Machine Learning techniques for marine environments. The methodology involves deploying multi-sensor nodes comprising vibration sensors, ultrasonic pulse velocity transducers, and high-resolution optical cameras at key stress points on marine concrete surfaces. Sensor data and surface images are continuously transmitted through a low-power wide-area network to a centralized cloud server. A Convolutional Neural Network is trained to analyze structural vibrations and image features for crack detection, classification, and localisation. Experimental results demonstrate a detection accuracy exceeding 96%, with real-time alerts and minimal false positives under variable weather conditions. The system captures early-stage micro-cracks before visible structural failure, supporting proactive maintenance and reducing inspection labor costs. Future work should focus on energy-harvesting technologies for autonomous sensor operation and expanded assessment of long-term structural fatigue under extreme oceanic events.
Co-Authors Aldias Bahatmaka Juan Abiegnail Sianipar Muhammad Shobari Romadhon Song Yeon Hee Sujantoko