Machine Learning

Larrañaga, Pedro; Atienza, David; Díaz-Rozo, Javier; Ogbechie, Alberto; Puerto-Santana, Carlos; Bielza, Concha

doi:10.1201/9781351128384-2

Cited by 3 publications

(4 citation statements)

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Section: Artificial Neural Network For Iot Applicationsmentioning

confidence: 99%

“…AI and ML are the next step in self-sensing materials toward smartization 19,20 and the Internet of Everything. Today, AI and ML technologies can be found in a wide variety of knowledge fields, such as environmental engineering, 21 structural engineering, 22,23 earth sciences, 24 computational mechanics, 20 and materials science. 25 Some factors driving this change include increased complexity of algorithms and learning models with growth of computing power per watt and availability of data.…”

Section: Artificial Neural Network For Iot Applicationsmentioning

confidence: 99%

“…These semi-automated or even fully automated tasks are of great importance when dealing with environments and real-time data monitoring, such as the case of a technological upgrade on an industrial manufacturing plant. Moreover, it has been proven to provide an advantage that facilitates faster transfer from a laboratory idea to industrial production and application 20 ; allows one to perform real-time characterization 26 of fault/anomaly detection 19,20,27,28 and do component benchmarking. 20,28…”

Section: Artificial Neural Network For Iot Applicationsmentioning

confidence: 99%

“…Moreover, it has been proven to provide an advantage that facilitates faster transfer from a laboratory idea to industrial production and application 20 ; allows one to perform real-time characterization 26 of fault/anomaly detection 19,20,27,28 and do component benchmarking. 20,28…”

Section: Artificial Neural Network For Iot Applicationsmentioning

confidence: 99%

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A deep neural network for electrical resistance calibration of self-sensing carbon fiber polymer composites compatible with edge computing structural monitoring hardware electronics

Tomás

Jalali

Tabatha

2023

Structural Health Monitoring

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The self-sensing ability of materials, in particular carbon fiber polymer composites (SSCFPC), is a must-have requirement when designing a structural monitoring network for remote assessment of structural serviceability. This work presents a study using an Artificial Deep Neural Network (ADNN) wherein is evaluated the electrical resistance ( R) output of specimens subjected to an unchanged deformation state of 2.86% strain for prolonged periods of time. Six ADNN architectures are evaluated with varying numbers of neurons on pre-defined hidden layers, sharing the same four data inputs and one output. The dataset is based on 3276 data points collected during the experimental campaign of an innovative electrode design embedded in SSCFPC specimens. The effect of the number of iterations and the architecture of the neural network is investigated in proposed ADNN models. Simple moving average, and moving Standard Deviation, [Formula: see text], are determined and plotted in terms of z-score to assist in performance evaluation of proposed ADNN models. The optimal ADNN architecture is found among six proposed architectures and for each of the four SSCFPC mixtures. Results show the proposed model architectures are able to predict values of R with greater accuracy than traditional regression mathematical methods when traditional statistical coefficients are used. However, when analyzing data in a time-series manner, results show further research is needed to achieve optimal accuracy results. The analysis presented focused on the structural monitoring network infrastructure and hardware electronics compatibility for further development of this type of SSCFPC as a self-sensing composite material with ability of automatic calibration and suitable for real-time data acquisition and artificial intelligence modeling.

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Section: Artificial Neural Network For Iot Applicationsmentioning

confidence: 99%

Section: Artificial Neural Network For Iot Applicationsmentioning

confidence: 99%

Section: Artificial Neural Network For Iot Applicationsmentioning

confidence: 99%

Section: Artificial Neural Network For Iot Applicationsmentioning

confidence: 99%

See 2 more Smart Citations

A deep neural network for electrical resistance calibration of self-sensing carbon fiber polymer composites compatible with edge computing structural monitoring hardware electronics

Tomás

Jalali

Tabatha

2023

Structural Health Monitoring

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BotGRABBER: SVM-Based Self-Adaptive System for the Network Resilience Against the Botnets’ Cyberattacks

Lysenko

Bobrovnikova

Savenko

et al. 2019

Communications in Computer and Information Science

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Machine learning - based framework for construction delay mitigation

Sanni-Anibire¹,

Zin²,

Olatunji³

2021

ITcon

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The construction industry, for many decades, has been underperforming in terms of the success of project delivery. Construction delays have become typical of many construction projects leading to lawsuits, project termination, and ultimately dissatisfied stakeholders. Experts have highlighted the lack of adoption of modern technologies as a cause of underproductivity. Nevertheless, the construction industry has an opportunity to tackle many of its woes through Construction 4.0, driven by enabling digital technologies such as machine learning. Consequently, this paper describes a framework based on the application of machine learning for delay mitigation in construction projects. The key areas identified for machine learning application include "cost estimation", "duration estimation", and "delay risk assessment". The developed framework is based on the CRISP-DM graphical framework. Relevant data were obtained to implement the framework in the three key areas identified, and satisfactory results were obtained. The machine learning methods considered include Multi Linear Regression Analysis, K-Nearest Neighbours, Artificial Neural Networks, Support Vector Machines, and Ensemble methods. Finally, interviews with professional experts were carried out to validate the developed framework in terms of its applicability, appropriateness, practicality, and reliability. The main contribution of this research is in its conceptualization and validation of a framework as a problem-solving strategy to mitigate construction delays. The study emphasized the cross-disciplinary campaign of the modern construction industry and the potential of machine learning in solving construction problems.

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Machine Learning

Cited by 3 publications

References 0 publications

A deep neural network for electrical resistance calibration of self-sensing carbon fiber polymer composites compatible with edge computing structural monitoring hardware electronics

A deep neural network for electrical resistance calibration of self-sensing carbon fiber polymer composites compatible with edge computing structural monitoring hardware electronics

BotGRABBER: SVM-Based Self-Adaptive System for the Network Resilience Against the Botnets’ Cyberattacks

Machine learning - based framework for construction delay mitigation

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