Economic-based approach for predicting optimal water pipe renewal period based on risk and failure rate

Kim, Ki-Bum; Seo, Jeewon; Hyung, Jinseok; Kim, Taehyeon; Kim, Jaehag; Koo, Ja−Yong

doi:10.4491/eer.2017.188

Cited by 11 publications

(3 citation statements)

References 19 publications

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“…In the teaching process, it is necessary to shape the creative teaching situation. Simultaneously, it is necessary to design teaching objectives with high-level thinking, deliver instruction around complex, cooperative, and challenging deep learning tasks, and emphasize objective, comprehensive evaluation methods that point to the process and developmental high-level for indepth learning evaluation [17]. According to the five teaching links of the traditional scaffolding teaching model, and based on the analysis of the characteristics of deep learning, guided by the deep learning theory, combined with the abovementioned scaffolding teaching principles for deep learning and the types and functions of learning scaffolding, this study systematizes and deepens the core elements of scaffolding teaching model [18].…”

Section: Realization Of Teaching Evaluation Of Project Costmentioning

confidence: 99%

[Retracted] Data Analysis and Processing Application of Deep Learning in Engineering Cost Teaching Evaluation

2022

Journal of Mathematics

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Based on the poor quality of engineering cost teaching, this paper puts forward the application method of in-depth learning in engineering cost teaching evaluation. This study aims to construct the engineering cost teaching evaluation system, optimize the engineering cost teaching evaluation algorithm combined with the in-depth learning method, standardize the evaluation index of the engineering cost teaching system, and realize the research goal of engineering cost teaching evaluation. Finally, the experiment proves that the application effect of in-depth learning in engineering cost teaching evaluation is obviously better, which can better guide the quality of engineering cost teaching.

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Section: Realization Of Teaching Evaluation Of Project Costmentioning

confidence: 99%

[Retracted] Data Analysis and Processing Application of Deep Learning in Engineering Cost Teaching Evaluation

2022

Journal of Mathematics

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“…Focused on the water supply suspension risk, Kim et al [32] proposed a framework for estimating the benefits of water pipe renewal. Some value assessment approaches were less direct and descriptive than the suggested approach based on five benefit items.…”

Section: Literature Reviewmentioning

confidence: 99%

Teaching-Learning-Based Optimization of Neural Networks for Water Supply Pipe Condition Prediction

Elshaboury

Abdelkader

Al-Sakkaf

et al. 2021

Water

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The bulk of water pipes experience major degradation and deterioration problems. This research aims at estimating the condition of water pipes in Shattora and Shaker Al-Bahery’s water distribution networks, in Egypt. The developed models involve training the Elman neural network (ENN) and feed-forward neural network (FFNN) coupled with particle swarm optimization (PSO), genetic algorithms (GA), the sine cosine algorithm (SCA), and the teaching-learning-based optimization (TLBO) algorithm. For the Shattora network, the inputs to these models are pipe characteristics such as length, wall thickness, diameter, material, lining and coating, surface type, traffic distribution, cathodic protection, flow velocity, and c-factor. For the Shaker Al-Bahery network, the data gathered include length, material, age, diameter, depth, and wall thickness. Three assessment criteria are used to evaluate the suggested machine learning models, namely index of agreement (IOA), correlation coefficient (R), and root mean squared error (RMSE). The results reveal that coupling FFNN with the TLBO algorithm outperforms other prediction models. Therefore, the FFNN-TLBO model can be a valuable tool for simulating the water network pipe condition. This study could help the water municipality allocate the available budget effectively and plan the required maintenance and rehabilitation actions.

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“…The problem of water pipe renewal planning based on a cost-benefit approach is addressed in Ref. [9]. Authors define five items of benefit.…”

Section: Introductionmentioning

confidence: 99%

Cost–Benefit Prediction of Asset Management Actions on Water Distribution Networks

Nafi

Brans

2019

Water

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The potential costs and benefits of a combination of asset management actions on the water distribution network are predicted. Two types of actions are considered: maintenance actions and renewal actions. Leak detection and reparation of failures on connections and pipes define the set of potential maintenance actions to be carried out. Renewal actions concern connections, pipes, and meters. All these actions represent the model’s decision variables in order to determine a trade-off between two objectives: (i) the maximization of the water efficiency rate and (ii) the minimization of the total cost of actions to be carried out on the water system. The assessment of objective functions is ensured by an artificial neural network (ANN) trained on a French mandatory database «SISPEA». A non-dominated sorting genetic algorithm (NSGA-II) is coupled to the ANN to reach the set of compromised solutions representing potential actions to achieve. Applied to a real water distribution system in the southeast of France, the proposed decision model indicates that the improvement of water efficiency rate (WER) in the short term requires increasing operation expenditures (OPEX), which represent 99% of the total cost. Results show the existence of a threshold effect that implies to use the budget in a certain way to improve performance. A potential solution can be chosen by the decision maker among the generated Pareto front with regard to the constraint on the budget and the targeted WER.

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Economic-based approach for predicting optimal water pipe renewal period based on risk and failure rate

Cited by 11 publications

References 19 publications

[Retracted] Data Analysis and Processing Application of Deep Learning in Engineering Cost Teaching Evaluation

[Retracted] Data Analysis and Processing Application of Deep Learning in Engineering Cost Teaching Evaluation

Teaching-Learning-Based Optimization of Neural Networks for Water Supply Pipe Condition Prediction

Cost–Benefit Prediction of Asset Management Actions on Water Distribution Networks

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