An expert system for assessing the technical and economic risk of pipe rehabilitation options

Marlow, David; Gould, Scott; Lane, Bradley W.

doi:10.1016/j.eswa.2015.07.020

Cited by 13 publications

(5 citation statements)

References 34 publications

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“…Regarding urban water networks, Marlow et al (2015) noted that water distribution infrastructure is undervalued because it is buried and out of sight. This poses an additional challenge on managing the pipe networks, and Marlow et al proposed a rule-based expert system to provide suggestions about the technical and economic risks related to pipeline repair and maintenance.…”

Section: Heuristics and Metaheuristicsmentioning

confidence: 99%

Using Optimisation and Machine Learning to Validate the Value of Infrastructure Investments

Flores¹,

Banerjee²,

Mathews³

2019

Preprint

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When stakeholders commit to building infrastructure as part of strategic, long-term planning, the final facilities are not normally amenable to modification after completion. A consequence of this is that users are forced to operatewithin the original specifications for, at least, as long as it takes to carry out major refurbishments or retrofitting, and even then, the constraints imposed by the original layout may be inescapable.On one hand, the original infrastructure plans enhance (or limit) the users' ability to operate efficiently for years to come. As time passes and the payback period approaches, changing operating conditions and unforeseen bottlenecks in the original blueprint can, at best, affect the economic returns and, at worst, defeat the purpose of the whole project (see, for example, Castellon airport in Spain, which was built but is grossly underutilised), producing unanticipated economical, social and political repercussions. On the other hand, managers and operators (that is, those living with the consequences of the strategic planning) have some leeway to compensate for miscalculations by means of their tacticaland operational planning.In this chapter, we explore the use of quantitative techniques to, first, amend bottlenecks and uncertain market and operating conditions that affect the performance of infrastructure investments (the tactic and operational levels), and second, validate the effectiveness of the original infrastructure design (the strategic level) under these changing conditions.More specifically, we present a rail scheduling case study where we combine demand forecasting using Machine Learning techniques and formal Operations Research methods to assess and maximise the value of already-existing infrastructure.Rail scheduling is a typical optimisation problem popular in the literature, but its potential value is bounded not only by its technical properties and specifications (how good the algorithm is) but also by the accuracy of data feeding the algorithm. Such data is critical in specifying the demand thata facility will experience in the future, and the costs that will be incurred to operate it. The use of intensive data analytics and appropriate Machine Learning techniques can resolve this and provide a substantial competitive edge forinvestors and operators of rail inter-modal terminals.We anticipate that Machine Learning algorithms that predict future demand, coupled with optimisation techniques that streamline operations of facilities, can be integrated to create tools that help policy makers and terminal operators maximise the value of their current infrastructure, while meeting ever-changing demand.

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Section: Heuristics and Metaheuristicsmentioning

confidence: 99%

Using Optimisation and Machine Learning to Validate the Value of Infrastructure Investments

Flores¹,

Banerjee²,

Mathews³

2019

Preprint

View full text Add to dashboard Cite

show abstract

“…The choice and further implementation of a particular method require a detailed preliminary inspection to investigate the condition of the pipeline, its dimensions, the type of transported medium, the surrounding underground infrastructure, the type of soil, the presence of groundwater, etc. [14,15]. Among the well-known methods of rehabilitation, cement spraying can be considered as one of the oldest with an experience of implementation since the early 1900s.…”

Section: Introductionmentioning

confidence: 99%

The research of reinforced cement liner for reconstruction of water supply and sewer pipes

Makisha¹

2022

J Appl Eng Science

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The research is focused on the investigation of the features of mortar liner reinforced with composite mesh, which may be one of the possible options in case of one possible method of trenchless reconstruction of water and sewer pipes. The research had two goals: to study the strength under static loading of a mortar liner reinforced with a composite mesh frame and to define the maximum depth of underground location of a coating element, which is of significant importance if a host pipe cannot bear external loads any longer. Within the research, two specimens of coating with the length of 1000 mm and diameter of 800 mm were tested. The compression results showed that for both specimens the loss of bearing capacity occurred to the fluidity of composite reinforcement under the load of approximately 30 kN. After the strength test, a calculation was made to evaluate the maximum depth of liner location in case if a host pipe fully exhaustits bearing capacity. The results of the calculation showed that the maximum height of the soil layer above the crown of a pipe is 2.8 to 3.2 m depending on the type and features of the soil.

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“…The problem with these models is their reliance on historical failure data, which may not be available every time. On the contrary, Marlow et al (2015) used expert elicitation methods that helped the decision-maker to select the most effective rehabilitation strategy for the pavements and pipelines. Similarly, probabilistic models such as Markov models (Butt et al, 1994;Mishalani & Madanat, 2002;Thompson & Johnson, 2005), method of moments (Damnjanovic, 2006), survival model (Gharaibeh & Darter, 2003), and Bayesian analysis (Liu & Gharaibeh, 2014) have been used by the researchers in forecasting performance transportation assets over time.…”

Section: Introductionmentioning

confidence: 99%

Risk-Based Multi-Objective Cross-Asset Budget Planning and Allocation Framework for the City of Sugar Land Integrated Asset Management System (IAMS)

Poudel

Damnjanović

Tang³

et al. 2021

Public Works Management & Policy

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An Integrated Asset Management System (IAMS) is a strategic asset management system that combines the assets of different categories into a system to prioritize their maintenance. This paper proposes a risk-based reliability-centric IAMS framework which works on the principle that the risk associated with the failure of an asset is a function of its direct and indirect costs. The direct costs are the costs associated with the replacement of the assets, whereas the indirect costs are the risks that the city faces following the failure. The framework is applied to the asset database of the City of Sugar Land, TX. The results of the model are analyzed in terms of a reduction in the number of assets in a very high-risk category in the risk matrix, the network level expected annual failure rate, and an increase in the network level average reliability.

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An expert system for assessing the technical and economic risk of pipe rehabilitation options

Cited by 13 publications

References 34 publications

Using Optimisation and Machine Learning to Validate the Value of Infrastructure Investments

Using Optimisation and Machine Learning to Validate the Value of Infrastructure Investments

The research of reinforced cement liner for reconstruction of water supply and sewer pipes

Risk-Based Multi-Objective Cross-Asset Budget Planning and Allocation Framework for the City of Sugar Land Integrated Asset Management System (IAMS)

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