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All Journal Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control Civil Engineering Journal
Aminaton Marto
Civil Engineering Department, Universiti Teknologi Malaysia
Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering Energy Engineering

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Evaluation of the Response of Buried Steel Pipelines Subjected to the Strike-slip Fault Displacement Oghabi, Mohsen; Khoshvatan, Mehdi; Marto, Aminaton
Civil Engineering Journal Vol 3, No 9 (2017): September
Publisher : Salehan Institute of Higher Education

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (1409.649 KB) | DOI: 10.21859/cej-03093

Abstract

In this paper, the response of buried steel pipeline subjected to the strike–slip fault displacement is studied. This study aimed to identify the seismic fault under the pipe at the intersection of large displacement (up to 3 meter fault displacement) and identify failure modes in the pipe. Innovation studies the effect of thickness ratio of the diameter of the pipe failure modes of the fault displacement. The nonlinear finite element method analysis was conducted. By using ABAQUS software, nonlinear finite element analysis was carried out on the pipeline under fault displacement. Numerical modelling aimed at obtaining the amount of displacement corresponding to the nonlinear behaviour in the pipeline, as well as identifying failure modes pipes in displacement from 0.2 to 3 meter in diameter to thickness ratio, taking into account the impact of the pipeline. The results showed the nonlinear behaviour of the displacement 57.5 cm pipeline damage starts and the displacement of 1 meter buckling occurs in pipes. The displacement of 1 meter fault, failure mode is local buckling pipe, and displacement and deformation of the pipe is 1 meter looks like the letter S. The displacement of 1.5 meter high (3 meter) failure mode tube is wrinkling. And deformation of the pipe in the fault displacement of 1.5 meter, like the letter Z. With the increase in displacement from 1.5 meter to high wrinkling occurs in pipes and up to 3 meter displacement continues. Plastic strain in the fault displacement of 80 cm in diameter to thickness ratio of 112 and 96 occurs, Plastic strain ratio of diameter to thickness of 86 does not occur. Reduction in the diameter of the thickness has a positive impact on reducing plastic strain in the pipe.
Flood Disaster and Early Warning: Application of ANFIS for River Water Level Forecasting Faruq, Amrul; Marto, Aminaton; Izzaty, Nadia Karima; Kuye, Abidemi Tolulope; Mohd Hussein, Shamsul Faisal; Abdullah, Shahrum Shah
Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control Vol. 6, No. 1, February 2021
Publisher : Universitas Muhammadiyah Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22219/kinetik.v6i1.1156

Abstract

Intensively monitoring river water level and flows in both upstream and downstream catchments are essential for flood forecasting in disaster risk reduction. This paper presents a developed flood river water level forecasting utilizing a hybrid technique called adaptive neuro-fuzzy inference system (ANFIS) model, employed for Kelantan river basin, Kelantan state, Malaysia. The ANFIS model is designed to forecast river water levels at the downstream area in hourly lead times. River water level, rainfall, and river flows were considered as input variables located in upstream stations, and one river water level in the downstream station is chosen as flood forecasting point (FFP) target. Particularly, each of these input-output configurations consists of four stations located in different areas. About twenty-seven data with fifteen minutes basis recorded in January 2013 to March 2015 were used in training and testing the ANFIS network. Data preprocessing is done with feature reduction by principal component analysis and normalization as well. With more attributes in input configurations, the ANFIS model shows better result in term of coefficient correlation ( ) against artificial neural network (ANN)-based models and support vector machine (SVM) model. In general, it is proven that the presented ANFIS model is a capable machine learning approach for accurate forecasting of river water levels to predict floods for disaster risk reduction and early warning.
Co-Authors Amrul Faruq Izzaty, Nadia Karima Khoshvatan, Mehdi Kuye, Abidemi Tolulope MOHD HUSSEIN, SHAMSUL FAISAL Oghabi, Mohsen Shahrum Shah Abdullah