Basic Steps in Machine Learning-Based Modeling

Bhattacharya, Shuvajit

doi:10.1007/978-3-030-71768-1_3

Cited by 4 publications

(1 citation statement)

References 41 publications

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“…A huge amount of data has been produced by scientific methods either on the laboratory scale or at the full scale (Buyya et al, 2013) and these historical data are of great asset to be used to the maximum advantage using ML to produce platforms for judging the concrete properties (Kamath et al, 2022). The main process of data analysis in machine learning involves different steps the first is data ingestion where data are collected and imported into the assigned analysis platform, while the second step is processing these data by filtering cleaning the data, or even data manipulation, the third is data analysis by choosing the best-fit model either optimization or prediction, train the model, evaluate the model and the last step is data visualization or making the prediction (Bhattacharya, 2021).…”

Section: Introductionmentioning

confidence: 99%

Machine learning in concrete technology: A review of current researches, trends, and applications

Gamil

2023

Front. Built Environ.

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Machine learning techniques have been used in different fields of concrete technology to characterize the materials based on image processing techniques, develop the concrete mix design based on historical data, and predict the behavior of fresh concrete, hardening, and hardened concrete properties based on laboratory data. The methods have been extended further to evaluate the durability and predict or detect the cracks in the service life of concrete, It has even been applied to predict erosion and chemical attaches. This article offers a review of current applications and trends of machine learning techniques and applications in concrete technology. The findings showed that machine learning techniques can predict the output based on historical data and are deemed to be acceptable to evaluate, model, and predict the concrete properties from its fresh state, to its hardening and hardened state to service life. The findings suggested more applications of machine learning can be extended by utilizing the historical data acquitted from scientific laboratory experiments and the data acquitted from the industry to provide a comprehensive platform to predict and evaluate concrete properties. It was found modeling with machine learning saves time and cost in obtaining concrete properties while offering acceptable accuracy.

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Section: Introductionmentioning

confidence: 99%

Machine learning in concrete technology: A review of current researches, trends, and applications

Gamil

2023

Front. Built Environ.

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Best practices for machine learning strategies aimed at process parameter development in powder bed fusion additive manufacturing

Samadiani,

Barnard,

Gunasegaram

et al. 2024

J Intell Manuf

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The process parameters used for building a part utilizing the powder-bed fusion (PBF) additive manufacturing (AM) system have a direct influence on the quality—and therefore performance—of the final object. These parameters are commonly chosen based on experience or, in many cases, iteratively through experimentation. Discovering the optimal set of parameters via trial and error can be time-consuming and costly, as it often requires examining numerous permutations and combinations of parameters which commonly have complex interactions. However, machine learning (ML) methods can recommend suitable processing windows using models trained on data. They achieve this by efficiently identifying the optimal parameters through analyzing and recognizing patterns in data described by a multi-dimensional parameter space. We reviewed ML-based forward and inverse models that have been proposed to unlock the process–structure–property–performance relationships in both directions and assessed them in relation to data (quality, quantity, and diversity), ML method (mismatches and neglect of history), and model evaluation. To address the common shortcomings inherent in the published works, we propose strategies that embrace best practices. We point out the need for consistency in the reporting of details relevant to ML models and advocate for the development of relevant international standards. Significantly, our recommendations can be adopted for ML applications outside of AM where an optimum combination of process parameters (or other inputs) must be found with only a limited amount of training data.

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Automated Machine Learning on High Dimensional Big Data for Prediction Tasks

Jayanthi

Devi

2022

2022 7th International Conference on Communication and Electronics Systems (ICCES)

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Basic Steps in Machine Learning-Based Modeling

Cited by 4 publications

References 41 publications

Machine learning in concrete technology: A review of current researches, trends, and applications

Machine learning in concrete technology: A review of current researches, trends, and applications

Best practices for machine learning strategies aimed at process parameter development in powder bed fusion additive manufacturing

Automated Machine Learning on High Dimensional Big Data for Prediction Tasks

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