A prediction model for compressive strength of CO2 concrete using regression analysis and artificial neural networks

Tam, Vivian W.Y.; Butera, Anthony; Lê, Khoa N.; Silva, Luis C.F. Da; Evangelista, Ana Catarina Jorge

doi:10.1016/j.conbuildmat.2022.126689

Cited by 50 publications

(8 citation statements)

References 44 publications

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“…CO 2 concrete alleviates these problems by carbonizing RAs, reducing CO 2 emissions, reusing crushed masonry materials, and preserving virgin aggregates. Regression analysis and artificial neural networks have accurately predicted its compressive strength, which proves that the CO 2 concrete's compressive strength supports the widespread adoption of eco-friendly materials [40]. CO 2 concrete is the best at reducing carbon emissions compared to NAC and RAC, as the carbonization process helps retain CO 2 into recycled aggregates, reducing the environmental footprint and improving the mechanical properties and compressive strength of concrete [8].…”

Section: Locking Co2 In Waste Concretementioning

confidence: 64%

Study on sustainable technology of construction waste in carbon neutrality: concrete decarbonization in Hong Kong

2024

Ninth International Conference on Energy Materials and Electrical Engineering (ICEMEE 2023)

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In Hong Kong, one of the most significant current discussions in carbon neutrality before 2050 is the concrete waste solution. Hong Kong has put in years of effort and achieved excellent results in environmental protection, while it is limited to concrete waste recycling technology practices. This study shows current research on concrete waste in Hong Kong and analysis of international frontier issues supported by a considerable amount of literature, attempting to show practical suggestions about carbon-neutral concrete in prevention, reusing, and recycling. Specifically, this study finds that hemp concrete is a potential replacement to prevent concrete waste and CO 2 emission, while recycled lump/aggregate concrete (RLCA) is a better option for Hong Kong high-rise buildings. In addition, waste glass (WG), waste plastic (WP), and waste wood (WW) in landfill area in Hong Kong plays a significant role in recycling concrete. Finally, yet importantly, it is necessary to discuss locking CO 2 in recycling concrete technology under the global carbon neutrality trend.

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Section: Locking Co2 In Waste Concretementioning

confidence: 64%

Study on sustainable technology of construction waste in carbon neutrality: concrete decarbonization in Hong Kong

2024

Ninth International Conference on Energy Materials and Electrical Engineering (ICEMEE 2023)

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“…Machine learning (ML) has recently been introduced to calibrate sensors with nonlinear characteristics for data sensing. [60] The multilayer feedforward neural network is suitable and sufficient for multiple regression tasks and thus it is used to map the piezoelectric signal features to the continuous responses of the grip force and relative characteristics; [61][62][63] the support vector machine (SVM) exhibits excellent performance in classification tasks and is utilized to distinguish the deformation states and speed levels; principal component analysis (PCA) processing is the most commonly used unsupervised linear dimensionality reduction method (Note S5, Supporting Information). [64][65][66] In realtime monitoring, voltage signals of the prototype piezoelectric metamaterial with computational sensing capability are obtained by a real-time data-acquisition board (Figure 3b).…”

Section: Computational Multi-sensingmentioning

confidence: 99%

A Metamaterial Computational Multi‐Sensor of Grip‐Strength Properties with Point‐of‐Care Human‐Computer Interaction

Chen,

Li,

Wang

et al. 2023

Advanced Science

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Grip strength is a biomarker of frailty and an evaluation indicator of brain health, cardiovascular morbidity, and psychological health. Yet, the development of a reliable, interactive, and point‐of‐care device for comprehensive multi‐sensing of hand grip status is challenging. Here, a relation between soft buckling metamaterial deformations and built piezoelectric voltage signals is uncovered to achieve multiple sensing of maximal grip force, grip speed, grip impulse, and endurance indicators. A metamaterial computational sensor design is established by hyperelastic model that governs the mechanical characterization, machine learning models for computational sensing, and graphical user interface to provide visual cues. A exemplify grip measurement for left and right hands of seven elderly campus workers is conducted. By taking indicators of grip status as input parameters, human‐computer interactive games are incorporated into the computational sensor to improve the user compliance with measurement protocols. Two elderly female schizophrenic patients are participated in the real‐time interactive point‐of‐care grip assessment and training for potentially sarcopenia screening. The attractive features of this advanced intelligent metamaterial computational sensing system are crucial to establish a point‐of‐care biomechanical platform and advancing the human‐computer interactive healthcare, ultimately contributing to a global health ecosystem.

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“…The neurons react to the information providing activations. The next step consists of recognizing shapes, for example, letters or numbers [26,27]. These processes described are mimicked by a technique used in artificial intelligence called artificial neural networks (ANNs).…”

Section: Artificial Neural Networkmentioning

confidence: 99%

Design of Type-3 Fuzzy Systems and Ensemble Neural Networks for COVID-19 Time Series Prediction Using a Firefly Algorithm

Melín

Sánchez

Castro

et al. 2022

Axioms

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In this work, information on COVID-19 confirmed cases is utilized as a dataset to perform time series predictions. We propose the design of ensemble neural networks (ENNs) and type-3 fuzzy inference systems (FISs) for predicting COVID-19 data. The answers for each ENN module are combined using weights provided by the type-3 FIS, in which the ENN is also designed using the firefly algorithm (FA) optimization technique. The proposed method, called ENNT3FL-FA, is applied to the COVID-19 data for confirmed cases from 12 countries. The COVID-19 data have shown to be a complex time series due to unstable behavior in certain periods of time. For example, it is unknown when a new wave will exist and how it will affect each country due to the increase in cases due to many factors. The proposed method seeks mainly to find the number of modules of the ENN and the best possible parameters, such as lower scale and lower lag of the type-3 FIS. Each module of the ENN produces an individual prediction. Each prediction error is an input for the type-3 FIS; moreover, outputs provide a weight for each prediction, and then the final prediction can be calculated. The type-3 fuzzy weighted average (FWA) integration method is compared with the type-2 FWA to verify its ability to predict future confirmed cases by using two data periods. The achieved results show how the proposed method allows better results for the real prediction of 20 future days for most of the countries used in this study, especially when the number of data points increases. In countries such as Germany, India, Italy, Mexico, Poland, Spain, the United Kingdom, and the United States of America, on average, the proposed ENNT3FL-FA achieves a better performance for the prediction of future days for both data points. The proposed method proves to be more stable with complex time series to predict future information such as the one utilized in this study. Intelligence techniques and their combination in the proposed method are recommended for time series with many data points.

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A prediction model for compressive strength of CO2 concrete using regression analysis and artificial neural networks

Cited by 50 publications

References 44 publications

Study on sustainable technology of construction waste in carbon neutrality: concrete decarbonization in Hong Kong

Study on sustainable technology of construction waste in carbon neutrality: concrete decarbonization in Hong Kong

A Metamaterial Computational Multi‐Sensor of Grip‐Strength Properties with Point‐of‐Care Human‐Computer Interaction

Design of Type-3 Fuzzy Systems and Ensemble Neural Networks for COVID-19 Time Series Prediction Using a Firefly Algorithm

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