learning curve models and applications literature review and research directions

Comparative Analysis of Learning Curve Models on Construction Productivity of Diaphragm Wall and Pile

G SaravanaPrabhu 1 and R Vidjeapriya 1

Published under licence by IOP Publishing Ltd IOP Conference Series: Materials Science and Engineering , Volume 1197 , International Conference on Advances in Civil Engineering (ICACE 2021) 25th-26th June 2021, Guntur, India Citation G SaravanaPrabhu and R Vidjeapriya 2021 IOP Conf. Ser.: Mater. Sci. Eng. 1197 012004 DOI 10.1088/1757-899X/1197/1/012004

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1 Division of Structural Engineering, Department of Civil Engineering, College of Engineering Guindy, Anna University Chennai, India

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In the analysis of construction operations, Learning Curves (LC) are considered one of the most important factors that determines the on-site variation in the productivity, which is usually considered in the construction projects during the estimation and planning stage. This research attempts to assess the suitability of LC models for the analysis of the learning phenomenon using productivity data for fairly complicated construction operations concerning the Diaphragm Wall and Pile Construction process from large-scale construction projects. In this study, the role of different LC models (i.e., Wright or Straight Line, Quadratic, Cubic, Knecht or Combined Exponential Log-linear, Stanford B) is investigated by the comparison of their outcomes through the utilization of cumulative productivity data of the activities involved in the Diaphragm Wall and Pile Construction process. The two main research objectives are (i) the investigation of the model which is the best bit for the historical productivity data of the completed construction activities (ii) an endeavour is formed to work out which model predicts the future performance better. The best suited LC model is predicted based on the least deviation from the yielded results of each model with respect to the actual construction data. Analysis of the cumulative average productivity data predicted that the Knecht or Combined Exponential Log-linear Model best fits both the complete Diaphragm Wall and Pile Construction Process in both the cases of fitting Historical data and in predicting future performance.

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Recent advances in deep learning models: a systematic literature review

• Published: 25 April 2023
• Volume 82 , pages 44977–45060, ( 2023 )

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• Ruchika Malhotra 1 &
• Priya Singh   ORCID: orcid.org/0000-0001-7656-7108 1  

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In recent years, deep learning has evolved as a rapidly growing and stimulating field of machine learning and has redefined state-of-the-art performances in a variety of applications. There are multiple deep learning models that have distinct architectures and capabilities. Up to the present, a large number of novel variants of these baseline deep learning models is proposed to address the shortcomings of the existing baseline models. This paper provides a comprehensive review of one hundred seven novel variants of six baseline deep learning models viz. Convolutional Neural Network, Recurrent Neural Network, Long Short Term Memory, Generative Adversarial Network, Autoencoder and Transformer Neural Network. The current review thoroughly examines the novel variants of each of the six baseline models to identify the advancements adopted by them to address one or more limitations of the respective baseline model. It is achieved by critically reviewing the novel variants based on their improved approach. It further provides the merits and demerits of incorporating the advancements in novel variants compared to the baseline deep learning model. Additionally, it reports the domain, datasets and performance measures exploited by the novel variants to make an overall judgment in terms of the improvements. This is because the performance of the deep learning models are subject to the application domain, type of datasets and may also vary on different performance measures. The critical findings of the review would facilitate the researchers and practitioners with the most recent progressions and advancements in the baseline deep learning models and guide them in selecting an appropriate novel variant of the baseline to solve deep learning based tasks in a similar setting.

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• Deep Learning

Various Frameworks and Libraries of Machine Learning and Deep Learning: A Survey

Zhaobin Wang, Ke Liu, … Yaonan Zhang

An Overview of Deep Learning

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Data sharing is not applicable to this article as this is a review article. The detail of the selected primary studies is presented in Table 3 .

Abbreviations

Deep Leering

• Autoencoder
• Convolutional Neural Network
• Recurrent Neural Network
• Generative Adversarial Network
• Long Short-Term Memory
• Transformer Neural Network

Deep Learning Models

Systematic Literature Review

Novel Variant

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Ruchika Malhotra & Priya Singh

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For the Systematic Literature Review-

1. Ruchika Malhotra proposed the idea for the article.

2. Primary Study selection was done by Priya Singh followed by a review by Dr. Ruchika Malhotra.

3. Data Extraction was done by Ruchika Malhotra and Priya Singh both separately, resolving differences where applicable at the time of merging.

4. Result Reporting was done by Priya Singh and reviewed by Ruchika Malhotra.

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1.1 Quality assessment results

We provide the quality scores to 166 studies selected after Inclusion–Exclusion criteria according to 16 quality assessment questions stated in Table 2 . Table 10 reports the percentage of candidate studies that answered a given quality question as “Yes”, “Partly” or “No”.

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Malhotra, R., Singh, P. Recent advances in deep learning models: a systematic literature review. Multimed Tools Appl 82 , 44977–45060 (2023). https://doi.org/10.1007/s11042-023-15295-z

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Received : 08 September 2022

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DOI : https://doi.org/10.1007/s11042-023-15295-z

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