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13 pages, 1468 KiB

Open AccessArticle

Safety Evaluation Method for Submarine Pipelines Based on a Radial Basis Neural Network

by Weidong Sun, Jialu Zhang, Yasir Mukhtar, Lili Zuo and Shaohua Dong

Sustainability 2023, 15(17), 12724; https://doi.org/10.3390/su151712724 - 23 Aug 2023

Cited by 1 | Viewed by 829

Abstract

As the lifeline of offshore oil and gas production, a submarine pipeline requires regular safety evaluations with proper maintenance according to the evaluation results. At present, the safety factors based on regional-level commonly used factors in engineering are too many, and this leads [...] Read more.

As the lifeline of offshore oil and gas production, a submarine pipeline requires regular safety evaluations with proper maintenance according to the evaluation results. At present, the safety factors based on regional-level commonly used factors in engineering are too many, and this leads to conservative evaluation results with a low acceptance of defects. In this paper, a risk factor evaluation index system for submarine pipeline defects is constructed through an analytic hierarchy process (AHP), and the original safety factors are corrected to achieve accurate evaluations for submarine pipeline safety. By constructing a radial basis neural network (RBFNN), the fast calculation of safety factors for other pipeline defects can be realized. Through comparison, it was found that the values obtained by the machine training were in good agreement with the real values, which reflects the accuracy of the model and provides a basis for the repair of a defective pipeline. Full article

(This article belongs to the Special Issue Advances in Structural Reliability Analysis and Maintenance Management)

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21 pages, 3685 KiB

Open AccessArticle

Comparative Study on the Efficiency of Simulation and Meta-Model-Based Monte Carlo Techniques for Accurate Reliability Analysis of Corroded Pipelines

by Mohamed El Amine Ben Seghier, Panagiotis Spyridis, Jafar Jafari-Asl, Sima Ohadi and Xinhong Li

Sustainability 2022, 14(10), 5830; https://doi.org/10.3390/su14105830 - 11 May 2022

Cited by 2 | Viewed by 1579

Abstract

Estimation of the failure probability for corroded oil and gas pipelines using the appropriate reliability analysis method is a task with high importance. The accurate prediction of failure probability can contribute to the better integrity management of corroded pipelines. In this paper, the [...] Read more.

Estimation of the failure probability for corroded oil and gas pipelines using the appropriate reliability analysis method is a task with high importance. The accurate prediction of failure probability can contribute to the better integrity management of corroded pipelines. In this paper, the reliability analysis of corroded pipelines is investigated using different simulation and meta-model methods. This includes five simulation approaches, i.e., Monte Carlo Simulation (MCS), Directional Simulation (DS), Line Sampling (LS), Subset Simulation (SS), and Importance Sampling (IS), and two meta-models based on MCS as Kriging-MCS and Artificial Neural Network based on MCS (ANN-MCS). To implement the proposed approaches, three limit state functions (LSFs) using probabilistic burst pressure models are established. These LSFs are designed for describing the collapse failure mode for pipelines constructed of low, mid, and high strength steels and are subjected to corrosion degradation. Illustrative examples that comprise three candidate pipelines made of X52, X65, and X100 steel grade are employed. The performance and efficiency of the proposed techniques for the estimation of the failure probability are compared from different aspects, which can be a useful implementation to indicate the complexity of handling the uncertainties provided by corroded pipelines. Full article

(This article belongs to the Special Issue Advances in Structural Reliability Analysis and Maintenance Management)

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17 pages, 4070 KiB

Open AccessArticle

Improvement in Durability and Service of Asphalt Pavements through Regionalization Methods: A Case Study in Baja California, Mexico

by José Cota, Cynthia Martínez-Lazcano, Marco Montoya-Alcaraz, Leonel García, Alejandro Mungaray-Moctezuma and Alejandro Sánchez-Atondo

Sustainability 2022, 14(9), 5123; https://doi.org/10.3390/su14095123 - 24 Apr 2022

Cited by 4 | Viewed by 2059

Abstract

The objective of this research is to develop a pavement design procedure that allows calibrating the design variables of asphalt pavements using regionalized conditions, to obtain efficient pavement performance for developing countries with limited resources and data. This study analyzes the roads of [...] Read more.

The objective of this research is to develop a pavement design procedure that allows calibrating the design variables of asphalt pavements using regionalized conditions, to obtain efficient pavement performance for developing countries with limited resources and data. This study analyzes the roads of the state of Baja California, Mexico; where type structures are determined and the performance grade of the binder used in the manufacture of asphalt concrete is regionalized according to the weather conditions altitude, traffic, and quality of the available materials. In a complementary way, the economic incidence of pavements during its service life is analyzed, projecting the analysis with different pavement structures and damage coefficients. The results show that this approach favors the asphalt pavements that comply with the projected in its service life, reducing maintenance interventions and costs. Full article

(This article belongs to the Special Issue Advances in Structural Reliability Analysis and Maintenance Management)

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27 pages, 9088 KiB

Open AccessArticle

Multi-Asset Defect Hotspot Prediction for Highway Maintenance Management: A Risk-Based Machine Learning Approach

by Arash Karimzadeh, Omidreza Shoghli, Sepehr Sabeti and Hamed Tabkhi

Sustainability 2022, 14(9), 4979; https://doi.org/10.3390/su14094979 - 21 Apr 2022

Cited by 4 | Viewed by 1685

Abstract

Transportation agencies constantly strive to tackle the challenge of limited budgets and continuously deteriorating highway infrastructure. They look for optimal solutions to make intelligent maintenance and repair investments. Condition prediction of highway assets and, in turn, prediction of their maintenance needs are key [...] Read more.

Transportation agencies constantly strive to tackle the challenge of limited budgets and continuously deteriorating highway infrastructure. They look for optimal solutions to make intelligent maintenance and repair investments. Condition prediction of highway assets and, in turn, prediction of their maintenance needs are key elements of effective maintenance optimization and prioritization. This paper proposes a novel risk-based framework that expands the potential of available data by considering the probabilistic susceptibility of assets in the prediction process. It combines a risk score generator with machine learning to forecast the hotspots of multiple defects while considering the interrelations between defects. With this, we developed a scalable algorithm, Multi-asset Defect Hotspot Predictor (MDHP), and then demonstrated its performance in a real-world case. In the case study, MDHP predicted the hotspots of three defects on paved ditches, considering the interrelation between paved ditches and five nearby assets. The results demonstrate an acceptable accuracy in predicting hotspots while highlighting the interrelation between adjacent assets and their contribution to future defects. Overall, this study offers a scalable approach with contribution in data-driven multi-asset maintenance planning with potential benefits to a broader range of linear infrastructures such as sewers, water networks, and railroads. Full article

(This article belongs to the Special Issue Advances in Structural Reliability Analysis and Maintenance Management)

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25 pages, 36382 KiB

Open AccessArticle

Prediction of FRCM–Concrete Bond Strength with Machine Learning Approach

by Aman Kumar, Harish Chandra Arora, Krishna Kumar, Mazin Abed Mohammed, Arnab Majumdar, Achara Khamaksorn and Orawit Thinnukool

Sustainability 2022, 14(2), 845; https://doi.org/10.3390/su14020845 - 12 Jan 2022

Cited by 26 | Viewed by 3633

Abstract

Fibre-reinforced cement mortar (FRCM) has been widely utilised for the repair and restoration of building structures. The bond strength between FRCM and concrete typically takes precedence over the mechanical parameters. However, the bond behaviour of the FRCM–concrete interface is complex. Due to several [...] Read more.

Fibre-reinforced cement mortar (FRCM) has been widely utilised for the repair and restoration of building structures. The bond strength between FRCM and concrete typically takes precedence over the mechanical parameters. However, the bond behaviour of the FRCM–concrete interface is complex. Due to several failure modes, the prediction of bond strength is difficult to forecast. In this paper, effective machine learning models were employed in order to accurately predict the FRCM–concrete bond strength. This article employed a database of 382 test results available in the literature on single-lap and double-lap shear experiments on FRCM–concrete interfacial bonding. The compressive strength of concrete, width of concrete block, FRCM elastic modulus, thickness of textile layer, textile width, textile bond length, and bond strength of FRCM–concrete interface have been taken into consideration with popular machine learning models. The paper estimates the predictive accuracy of different machine learning models for estimating the FRCM–concrete bond strength and found that the GPR model has the highest accuracy with an R-value of 0.9336 for interfacial bond strength prediction. This study can be utilising in the estimation of bond strength to minimise the experimentation cost in minimum time. Full article

(This article belongs to the Special Issue Advances in Structural Reliability Analysis and Maintenance Management)

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16 pages, 1794 KiB

Open AccessArticle

Risk-Cost Optimized Maintenance Strategy for Steel Bridge Subjected to Deterioration

by Le Li, Mojtaba Mahmoodian, Alireza Khaloo and Zhiyan Sun

Sustainability 2022, 14(1), 436; https://doi.org/10.3390/su14010436 - 31 Dec 2021

Cited by 3 | Viewed by 2197

Abstract

This paper aims to develop a deteriorated bridge maintenance strategy that ensures the safe operation of steel structures and minimizes the total risk. Five common failure modes are considered for the deteriorated bridge: flexure, shear, deflection, fatigue failure for girder, and chloride attack [...] Read more.

This paper aims to develop a deteriorated bridge maintenance strategy that ensures the safe operation of steel structures and minimizes the total risk. Five common failure modes are considered for the deteriorated bridge: flexure, shear, deflection, fatigue failure for girder, and chloride attack for the concrete deck. Time-dependent and system reliability analyses are carried out to find the probability of failure under these failure modes. Risk-cost optimization is then used to determine the maintenance strategy. This method was applied to a working example. It was found that the developed maintenance strategy can predict when, where, and what to maintain for a bridge to ensure its safe and serviceable operation during its lifespan. The proposed methodology can help structural engineers and asset managers repair and maintain bridges under deterioration. Full article

(This article belongs to the Special Issue Advances in Structural Reliability Analysis and Maintenance Management)

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10 pages, 1069 KiB

Open AccessEditor’s ChoiceArticle

Estimating the Long-Term Reliability of Steel and Cast Iron Pipelines Subject to Pitting Corrosion

by Robert E. Melchers and Mukshed Ahammed

Sustainability 2021, 13(23), 13235; https://doi.org/10.3390/su132313235 - 29 Nov 2021

Cited by 2 | Viewed by 1431

Abstract

Water-injection, oil production and water-supply pipelines are prone to pitting corrosion that may have a serious effect on their longer-term serviceability and sustainability. Typically, observed pit-depth data are handled for a reliability analysis using an extreme value distribution such as Gumbel. Available data [...] Read more.

Water-injection, oil production and water-supply pipelines are prone to pitting corrosion that may have a serious effect on their longer-term serviceability and sustainability. Typically, observed pit-depth data are handled for a reliability analysis using an extreme value distribution such as Gumbel. Available data do not always fit such monomodal probability distributions well, particularly in the most extreme pit-depth region, irrespective of the type of pipeline. Examples of this are presented, the reasons for this phenomenon are discussed and a rationale is presented for the otherwise entirely empirical use of the ‘domain of attraction’ in extreme value applications. This permits a more rational estimation of the probability of pipe-wall perforation, which is necessary for asset management and for system-sustainability decisions. Full article

(This article belongs to the Special Issue Advances in Structural Reliability Analysis and Maintenance Management)

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15 pages, 475 KiB

Open AccessArticle

On the Remuneration to Electrical Utilities and Budgetary Allocation for Substation Maintenance Management

by Pedro J. Zarco-Periñán, José L. Martínez-Ramos and Fco. Javier Zarco-Soto

Sustainability 2021, 13(18), 10125; https://doi.org/10.3390/su131810125 - 10 Sep 2021

Cited by 1 | Viewed by 1479

Abstract

The liberalization of electricity markets has produced a great change in electrical utilities. One of these changes has affected the methodology for setting their remuneration. Depending on the country, these are different. Despite the wide range of remuneration methodologies for the electricity market [...] Read more.

The liberalization of electricity markets has produced a great change in electrical utilities. One of these changes has affected the methodology for setting their remuneration. Depending on the country, these are different. Despite the wide range of remuneration methodologies for the electricity market of each country, they all feature one common element: the remuneration of operation and maintenance. One of the messages that this remuneration transmits is the need to extend the useful life of the facilities to allow sustainable development. This article focuses on the remuneration schemes of electrical utilities, the classification of substations for the definition of their maintenance programs, and the budget allocation for the execution of maintenance in these critical infrastructures. The particularity of these facilities, in which it is generally necessary to de-energize some of their parts for maintenance, has also been taken into account. To this end, a simple methodology currently used is presented based on the standardization of the bays of the substations and their classification into levels of importance. This classification into levels enables the facilities to be grouped according to similarities in their maintenance plans, although they differ from each other in terms of the periodicity of the application of maintenance procedures. This methodology guarantees a similar distribution of maintenance activities and financial needs over the years. In addition, the methodology allows one to know the importance of each substation (since the greater the equivalent weight, the greater the importance). Finally, the application of the proposed methodology in a real case is presented. It shows the simplicity, effectiveness, and lamination of the budgetary allocation of the proposed methodology, this being the main contribution of the formulation. Full article

(This article belongs to the Special Issue Advances in Structural Reliability Analysis and Maintenance Management)

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Other

Jump to: Research

12 pages, 2973 KiB

Open AccessEssay

Detecting Cable Force Anomalies on Cable-Stayed Bridges Using the STA/LTA Method

by Yanwei Wang, Qingxu Zhao, Yuandi Li, Min Zhang and Wanxu Zhu

Sustainability 2022, 14(18), 11373; https://doi.org/10.3390/su141811373 - 10 Sep 2022

Cited by 1 | Viewed by 1364

Abstract

The cable force of cable-stayed bridges may become abnormal during operation, so cable force anomaly detection is essential for evaluating the health of cables. The current methods for detecting cable force anomalies have poor resistance to temperature disturbances and are insensitive to abnormal [...] Read more.

The cable force of cable-stayed bridges may become abnormal during operation, so cable force anomaly detection is essential for evaluating the health of cables. The current methods for detecting cable force anomalies have poor resistance to temperature disturbances and are insensitive to abnormal cable force, making it challenging to detect minor cable force anomalies. Therefore, this work employs the short-time-average over long-time-average (STA/LTA) method to detect cable force anomalies. The characteristic function and key parameters of the STA/LTA method are optimized and tested by combining measured cable force data with cable force anomaly simulation. The results show that the STA/LTA method can effectively mitigate the interference of temperature in the detection of cable force anomalies and that it has good sensitivity to minor cable force anomalies. By examining detecting the measured cable force data of the Xiangjiang Bridge in Dongzhou, Hengyang, China, it is further verified that the STA/LTA method could effectively detect a cable force anomaly with a cable force deviation rate of higher than 1%. Full article

(This article belongs to the Special Issue Advances in Structural Reliability Analysis and Maintenance Management)

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