An Artificial Neural Network Model for Project Effort Estimation

Şengüneş, Burcu; Öztürk, Nursel

doi:10.3390/systems11020091

Cited by 7 publications

(1 citation statement)

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“…Other studies, such as [36], used artificial neural networks (ANNs) to address various construction topics, including the development of a mathematical model to estimate the condition of water distribution networks. The estimation of project effort was carried out by [37] using an ANN. A risk assessment model for infrastructure projects was developed by [38] using a BP-ANN algorithm.…”

Section: Rehabilitation Selection Methodsmentioning

confidence: 99%

Criticality-Based Management of Facility Assets

Salman

2024

Buildings

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Effective facility asset management requires specific skills and tools to optimize the use of limited resources, making a decision support system essential. This research introduces a comprehensive decision support system, which is a framework organized into three models: the criticality model, the rehabilitation model, and the optimum criticality model to manage the rehabilitation of facility assets. The criticality model utilizes the Analytical Hierarchy Process (AHP) to assess the group of assets. Emphasizing criticality as a central management factor, this model lays the foundation for subsequent decision-making. The rehabilitation model employs an Artificial Neural Network (ANN), integrating Customer Level of Service (CLoS), Technical Level of Service (TLoS), and asset criticality to determine appropriate rehabilitation actions. NeuralTools 7.5 is leveraged for precise predictions of rehabilitation strategies tailored to specific assets. The third model, optimum criticality, focuses on prioritizing rehabilitation activities within the constraints of limited budgets. Lingo 20.0 is utilized to optimize rehabilitation activities, considering budget limitations and other constraints, offering a strategic approach to maximize the impact of available resources. This integrated framework provides decision-makers with a systematic and data-driven approach to facility management, enhancing the efficiency and effectiveness of rehabilitation actions. An academic building was chosen as a hypothetical example to implement the three models and suggest the essential considerations for managing both the academic building itself and other infrastructure assets. The results obtained demonstrate that the principles and methodologies encapsulated in this project can be extrapolated and scaled up for application to large-scale infrastructure assets, ensuring the sustenance of the requisite level of service and the management of acceptable risk on a broader scale.

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Section: Rehabilitation Selection Methodsmentioning

confidence: 99%

Criticality-Based Management of Facility Assets

Salman

2024

Buildings

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Neural Networks‐Based Software Development Effort Estimation: A Systematic Literature Review

Boujida,

Amazal,

Idri

2024

J Software Evolu Process

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Software development effort estimation (SDEE) is a key task in managing software projects. Among the existing SDEE models, artificial neural networks (ANN) have garnered considerable attention from the software engineering community because of their ability to learn from previous data and yield acceptable estimates. However, to the best of the authors' knowledge, no systematic literature review (SLR) has been carried out with focus on the use of ANNs in SDEE. This work aims to analyze ANN‐based SDEE studies from five view‐points: estimation accuracy, accuracy comparison, estimation context, impact of combining ANN‐based SDEE models with other techniques, and ANNs parameters. To find relevant ANN‐based SDEE studies, we carried out an automated search using four electronic databases. The quality of the relevant papers was assessed to determine the set of papers to include in our review. We identified 65 papers published in the period 1993–2023 with acceptable quality score. The results of our systematic review revealed that ANN‐based SDEE models perform better than 11 machine learning (ML) and non‐ML SDEE models. Further, the estimation accuracy is improved when neural networks are used in combination with other techniques such as fuzzy clustering techniques. This study found that the use of ANN models in SDEE is promising to get accurate estimates. However, the application of ANN models in industry is still limited. Therefore, it is recommended that practitioners cooperate with researchers to encourage and facilitate the application of ANN models in industry.

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