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21 pages, 6153 KiB

Open AccessArticle

A Risk Management Database Framework Implementation for Transportation Asset Management

by Inya Nlenanya and Omar Smadi

CivilEng 2021, 2(1), 193-213; https://0-doi-org.brum.beds.ac.uk/10.3390/civileng2010011 - 02 Mar 2021

Cited by 2 | Viewed by 3408

A 2017 survey of the state of practice on how agencies are developing their risk-based asset management plan shows that state highway agencies are increasingly adapting the way they do business to include explicit considerations of risks. At the moment, this consideration of [...] Read more.

A 2017 survey of the state of practice on how agencies are developing their risk-based asset management plan shows that state highway agencies are increasingly adapting the way they do business to include explicit considerations of risks. At the moment, this consideration of risk is not linked to data. Hence, there is a lack of integration of risk management in driving strategic cross-asset programming and decision-making. This paper proposes and implements a risk management database framework as the missing piece in the full implementation of a risk-based transportation asset management program. This risk management database framework utilizes Geographic Information Systems (GIS) and Application Programming Interface (API) to implement a risk management database of all the relevant variables an agency needs for risk modeling to improve risk monitoring, risk register updates, and decision-making. This approach allows the use of existing enterprise as well as legacy data collection systems, which eliminates the need for any capital-intensive implementation cost. Furthermore, it provides transportation agencies with the ability to track risk in quantitative terms, a framework for prioritizing risk, and the development of an actionable plan for risk mitigation. In this paper, the implementation of the fully integrated GIS-enabled risk management database employs the Iowa department of transportation (DOT) data and risk register. Full article

(This article belongs to the Special Issue Addressing Risk in Engineering Asset Management)

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33 pages, 6531 KiB

Open AccessArticle

An Integrated Approach for Failure Analysis of Natural Gas Transmission Pipeline

by Sk Kafi Ahmed and Golam Kabir

CivilEng 2021, 2(1), 87-119; https://0-doi-org.brum.beds.ac.uk/10.3390/civileng2010006 - 01 Feb 2021

Cited by 6 | Viewed by 2960

Abstract

The main aim of this study is to identify the most important natural gas pipeline failure causes and interrelation analysis. In this research, the rough analytic hierarchy process (Rough-AHP) is used to identify the natural gas pipeline failure causes rank order. Then a [...] Read more.

The main aim of this study is to identify the most important natural gas pipeline failure causes and interrelation analysis. In this research, the rough analytic hierarchy process (Rough-AHP) is used to identify the natural gas pipeline failure causes rank order. Then a combination of rough decision-making trial and evaluation laboratory (DEMATEL) and interpretive structural modeling (ISM) method is applied to generate the level of importance. The comparison of traditional DEMATEL and Rough-DEMATEL are also performed to establish the cause-effect interrelation diagram. Finally, the Bayesian Belief Network (BBN) is combined with Rough DEMATEL and ISM to identify the interrelation analysis among the most crucial failure causes. As a result, the energy supply company and government policymaker can take necessary safety plan and improve the operation. The main outcome of this study is to improve the security management and reduce the potential failure risks. Full article

(This article belongs to the Special Issue Addressing Risk in Engineering Asset Management)

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22 pages, 4029 KiB

Open AccessArticle

A New Risk Management Methodology for Infrastructure Based on Real-Time Monitoring and Dynamic Interventions: An Example Application on an Air Handling Unit

by Francesco Rota, Cinzia Talamo, Giancarlo Paganin and Claudio Martani

CivilEng 2020, 1(2), 132-153; https://0-doi-org.brum.beds.ac.uk/10.3390/civileng1020009 - 25 Aug 2020

Viewed by 2929

Abstract

For an effective risk management of complex buildings it is required to dynamically estimate the risk on the service and take proper responsive measures to contrast it. This implies being able to estimate the evolving probabilities of failures over time and the way [...] Read more.

For an effective risk management of complex buildings it is required to dynamically estimate the risk on the service and take proper responsive measures to contrast it. This implies being able to estimate the evolving probabilities of failures over time and the way their occurrence is trust in affecting the service. This is now possible thanks to the advent of new sensing technologies and data-driven models to estimate failure probabilities, as well as solid risk management methodologies to estimate their effect on the service. However, it needs to be considered that the implementation of a dynamic risk management in standard building operation has to consider the reconfiguration of some processes to include the use of enabling technologies. In this paper a new dynamic risk management methodology is proposed to consistently (i) model the service, estimate the risk, first (ii) statically, using fault tree analysis, and then (iii) dynamically, using sensing technologies for data gathering and data-driven models for dynamic probability estimate, and finally (iv) implement the required intervention measures to minimize the risk. Then an application of the methodology is presented, for the risk management of an air handling unit, using a convolutional neural network, and its outcomes discussed. Conclusions are also drawn on the implications of integrating such a methodology in the current whole building risk management process and several outlooks are proposed. Full article

(This article belongs to the Special Issue Addressing Risk in Engineering Asset Management)

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26 pages, 2958 KiB

Open AccessArticle

Identifying the Input Uncertainties to Quantify When Prioritizing Railway Assets for Risk-Reducing Interventions

by Natalia Papathanasiou and Bryan T. Adey

CivilEng 2020, 1(2), 106-131; https://0-doi-org.brum.beds.ac.uk/10.3390/civileng1020008 - 19 Aug 2020

Cited by 9 | Viewed by 2706

Abstract

Railway managers identify and prioritize assets for risk-reducing interventions. This requires the estimation of risks due to failures, as well as the estimation of costs and effects due to interventions. This, in turn, requires the estimation of values of numerous input variables. As [...] Read more.

Railway managers identify and prioritize assets for risk-reducing interventions. This requires the estimation of risks due to failures, as well as the estimation of costs and effects due to interventions. This, in turn, requires the estimation of values of numerous input variables. As there is uncertainty related to the initial input estimates, there is uncertainty in the output, i.e., assets to be prioritized for risk-reducing interventions. Consequently, managers are confronted with two questions: Do the uncertainties in inputs cause significant uncertainty in the output? If so, where should efforts be concentrated to quantify them? This paper discusses the identification of input uncertainties that are likely to affect railway asset prioritization for risk-reducing interventions. Once the track sections, switches and bridges of a part of the Irish railway network were prioritized using best estimates of inputs, they were again prioritized using: (1) reasonably low and high estimates, and (2) Monte Carlo sampling from skewed normal distributions, where the low and high estimates encompass the 95% confidence interval. The results show that only uncertainty in a few inputs influences the prioritization of the assets for risk-reducing interventions. Reliable prioritization of assets can be achieved by quantifying the uncertainties in these particular inputs. Full article

(This article belongs to the Special Issue Addressing Risk in Engineering Asset Management)

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23 pages, 5998 KiB

Open AccessArticle

Comparison of Multi-Criteria Group Decision-Making Methods for Urban Sewer Network Plan Selection

by Zhen Wu and Georges Abdul-Nour

CivilEng 2020, 1(1), 26-48; https://0-doi-org.brum.beds.ac.uk/10.3390/civileng1010003 - 16 Jun 2020

Cited by 44 | Viewed by 4478

Abstract

Selecting a suitable sewer network plan for a city is a complex and challenging task that requires discussion among a group of experts and the consideration of multiple conflicting criteria with different measurement units. A number of multi-criteria decision-making (MCDM) methods have been [...] Read more.

Selecting a suitable sewer network plan for a city is a complex and challenging task that requires discussion among a group of experts and the consideration of multiple conflicting criteria with different measurement units. A number of multi-criteria decision-making (MCDM) methods have been proposed for analyzing sewer network selection problems, each having their own distinct advantages and limitations. Although many decision-making techniques are available, decision-makers are confronted with the difficult task of selecting the appropriate MCDM method, as each method can lead to different results when applied to an identical problem. This paper evaluates four different multi-criteria decision-making methods, which are the Analytic Hierarchy Process (AHP), the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS), Elimination Et Choix Traduisant la REalité (ELECTRE III) and the Preference Ranking Organization METHods for Enrichment Evaluations II (PROMETHEE II), for one sewer network group decision problem in the early stage of sewer water infrastructure asset management. Moreover, during the implementation of different MCDM methods, the Delphi technique is introduced to organize and structure the discussions among all the decision-makers. The results of the study are examined based on each method’s ability to provide accurate representations of the decision-makers’ preferences and their experience implementing each method. As a conclusion, decision-makers identify PROMETHEE II as their favorite method, AHP is more time and energy consuming and results in a number of inconsistencies, while TOPSIS loses information during vector normalization for multi-dimension criteria, and ELECTRE III’s results are inconclusive. Full article

(This article belongs to the Special Issue Addressing Risk in Engineering Asset Management)

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16 pages, 2108 KiB

Open AccessArticle

Managing the Performance of Asset Acquisition and Operation with Decision Support Tools

by Susan Lattanzio, Linda Newnes, Marcelle McManus and Derrick Dunkley

CivilEng 2020, 1(1), 10-25; https://0-doi-org.brum.beds.ac.uk/10.3390/civileng1010002 - 09 Jun 2020

Viewed by 3095

Abstract

Decision support tools (DSTs) are increasingly being used to assist with asset acquisition and management decisions. Whether these tools are “fit for purpose” will have both economic and non-economic implications. Despite this, the on-going governance of DST performance receives only limited attention within [...] Read more.

Decision support tools (DSTs) are increasingly being used to assist with asset acquisition and management decisions. Whether these tools are “fit for purpose” will have both economic and non-economic implications. Despite this, the on-going governance of DST performance receives only limited attention within both the academic and industry literature. This work addresses that research gap. Within this paper a conceptual process for managing the operational performance of decision support tools is presented. The novelty of the approach is that it aligns with the ISO 5500x:2014 Asset Management Standard, therefore introducing consistency in the governance of DSTs with physical engineered assets. A case study of the UK’s National Grid Electricity Transmission (NGET) is used to inform the approach design. The evaluation demonstrates it to be both logical and useable within the context of NGET and they have expressed an intention to implement the approach within their business. A key finding of the research was that DSTs transcend functions and organisations. This is significant and can lead to a situation whereby performance and criticality are interpreted and measured differently. The introduction of a common approach for managing DST performance works towards improving consistency and creating a shared understanding. Full article

(This article belongs to the Special Issue Addressing Risk in Engineering Asset Management)

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9 pages, 834 KiB

Open AccessArticle

Methodological Proposal for the Development of Insurance Policies for Building Components

by Miguel Macedo, Jorge de Brito, Carlos Oliveira Cruz and Ana Silva

CivilEng 2020, 1(1), 1-9; https://0-doi-org.brum.beds.ac.uk/10.3390/civileng1010001 - 19 May 2020

Cited by 1 | Viewed by 2265

Abstract

Insurance is a growing economic activity within the construction sector. Homes and buildings are perhaps the most important investment an individual makes in his/her lifetime. Nevertheless, the market for insurance coverage policies applied to the building envelope is in an embryonic stage, mainly [...] Read more.

Insurance is a growing economic activity within the construction sector. Homes and buildings are perhaps the most important investment an individual makes in his/her lifetime. Nevertheless, the market for insurance coverage policies applied to the building envelope is in an embryonic stage, mainly due to the lack of knowledge in terms of risk and costs associated to the failure of these elements. This study provides an innovative and methodological approach to the development of an insurance product that targets the obsolescence of building components. In defining a structured approach to the design of insurance policies for buildings, the use of the service life prediction models proposed in this study allows establishing different types of insurance policies with different risk premiums and evaluating different losses and risks accepted by the owners, thus promoting the increase of the patrimonial value of the asset and reducing the risk of premature failure and the uncertainty of the costs of maintenance during its life cycle. Full article

(This article belongs to the Special Issue Addressing Risk in Engineering Asset Management)

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Review

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14 pages, 753 KiB

Open AccessReview

Earthquake Early Warning Systems as an Asset Risk Management Tool

by Luisa Beltramone and Rui Carrilho Gomes

CivilEng 2021, 2(1), 120-133; https://0-doi-org.brum.beds.ac.uk/10.3390/civileng2010007 - 02 Feb 2021

Cited by 5 | Viewed by 4837

Abstract

Losses due to strong seismic events can amount to millions or billions of US dollars and can affect regions for large periods of time, even severely undermining the economy. Earthquake early warning systems have proven to be helpful tools to mitigate the social [...] Read more.

Losses due to strong seismic events can amount to millions or billions of US dollars and can affect regions for large periods of time, even severely undermining the economy. Earthquake early warning systems have proven to be helpful tools to mitigate the social and economic impact on communities and businesses. Recent case studies are briefly described, followed by examples of proactive measures for assets, infrastructure, citizens education and empowerment, complementary to earthquake early warning systems. Full article

(This article belongs to the Special Issue Addressing Risk in Engineering Asset Management)

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13 pages, 884 KiB

Open AccessReview

Framework for Validation of Permanently Installed MEMS-Based Acquisition Devices Using Soft Sensor Models

by Alain Bartels, Edward Cripps, Adrian Keating, Ian Milne, Ben Travaglione and Melinda Hodkiewicz

CivilEng 2020, 1(2), 93-105; https://0-doi-org.brum.beds.ac.uk/10.3390/civileng1020007 - 28 Jul 2020

Viewed by 2606

Abstract

Asset integrity and predictive maintenance models require field data for an accurate assessment of an asset’s condition. Historically these data collected periodically in the field by technicians using portable units. The significant investment in inexpensive microelectromechanical (MEMS) sensors mounted on untethered (energy-harvesting or [...] Read more.

Asset integrity and predictive maintenance models require field data for an accurate assessment of an asset’s condition. Historically these data collected periodically in the field by technicians using portable units. The significant investment in inexpensive microelectromechanical (MEMS) sensors mounted on untethered (energy-harvesting or battery-powered) microprocessors communicating wirelessly to the cloud is expected to change the way we collect asset health data. Permanently installed MEMS-based sensing units will enable near-real time data collection and reduce the safety exposure of technicians by eliminating the need to manually collect field data. With hundreds of MEMS-based sensing units expected to be installed at a single site it is vital to assure the data they produce and maintain them cost effectively. An asset management framework for validation of MEMS-based sensing units for condition monitoring and structural integrity (CM&SI) applications is proposed. An integral part of this framework is the proposed use of soft sensor models to replace technician inspections in the field. Soft sensor models are used in the process industry to stabilize product quality and process operations but there are few examples in asset management applications. The contributions of this paper are twofold. Firstly, we use an interdisciplinary approach drawing on electronics, process control, statistics, machine learning, and asset management fields to describe the emerging field of permanently installed MEMS-based sensing units for CM&SI. Secondly, we development a framework for assuring validation of the data these sensing units generate. Full article

(This article belongs to the Special Issue Addressing Risk in Engineering Asset Management)

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