Augmented Data-Driven Approach towards 3D Printed Concrete Mix Prediction DOI Creative Commons
Saif Rehman, Raja Dilawar Riaz, Muhammad Usman

et al.

Applied Sciences, Journal Year: 2024, Volume and Issue: 14(16), P. 7231 - 7231

Published: Aug. 16, 2024

Formulating a mix design for 3D concrete printing (3DCP) is challenging, as it involves an iterative approach, wasting lot of resources, time, and effort to optimize the strength printability. A potential solution formulation through artificial intelligence (AI); however, being new emerging field, open-source availability datasets limited. Limited significantly restrict predictive performance machine learning (ML) models. This research explores data augmentation techniques like deep generative adversarial network (DGAN) bootstrap resampling (BR) increase available train three ML models, namely support vector (SVM), neural (ANN), extreme gradient boosting regression (XGBoost). Their was evaluated using R2, MSE, RMSE, MAE metrics. Models trained on BR-augmented showed higher accuracy than those DGAN-augmented data. The BR-trained XGBoost exhibited highest R2 scores 0.982, 0.970, 0.972, 0.971, 0.980 cast compressive strength, printed direction 1, 2, 3, slump flow respectively. proposed method predicting (mm), cast, anisotropic (MPa) can effectively predict printable concrete, unlocking its full application in construction industry.

Language: Английский

Artificial Intelligence and Neuroscience: Transformative Synergies in Brain Research and Clinical Applications DOI Open Access

Răzvan Onciul,

Cătălina-Ioana Tătaru,

Adrian Dumitru

et al.

Journal of Clinical Medicine, Journal Year: 2025, Volume and Issue: 14(2), P. 550 - 550

Published: Jan. 16, 2025

The convergence of Artificial Intelligence (AI) and neuroscience is redefining our understanding the brain, unlocking new possibilities in research, diagnosis, therapy. This review explores how AI’s cutting-edge algorithms—ranging from deep learning to neuromorphic computing—are revolutionizing by enabling analysis complex neural datasets, neuroimaging electrophysiology genomic profiling. These advancements are transforming early detection neurological disorders, enhancing brain–computer interfaces, driving personalized medicine, paving way for more precise adaptive treatments. Beyond applications, itself has inspired AI innovations, with architectures brain-like processes shaping advances algorithms explainable models. bidirectional exchange fueled breakthroughs such as dynamic connectivity mapping, real-time decoding, closed-loop systems that adaptively respond states. However, challenges persist, including issues data integration, ethical considerations, “black-box” nature many systems, underscoring need transparent, equitable, interdisciplinary approaches. By synthesizing latest identifying future opportunities, this charts a path forward integration neuroscience. From harnessing multimodal cognitive augmentation, fusion these fields not just brain science, it reimagining human potential. partnership promises where mysteries unlocked, offering unprecedented healthcare, technology, beyond.

Language: Английский

Citations

5
Transfer learning framework for modelling the compressive strength of ultra-high performance geopolymer concrete DOI

Ho Anh Thu Nguyen,

Duy Hoang Pham, Anh Tuấn Lê

et al.

Construction and Building Materials, Journal Year: 2025, Volume and Issue: 459, P. 139746 - 139746

Published: Jan. 1, 2025

Language: Английский

Citations

2
Conditional Generative Adversarial Networks and Deep Learning Data Augmentation: A Multi-Perspective Data-Driven Survey Across Multiple Application Fields and Classification Architectures DOI Creative Commons
Lucas C. Ribas, Wallace Casaca, Ricardo T. Fares

et al.

AI, Journal Year: 2025, Volume and Issue: 6(2), P. 32 - 32

Published: Feb. 7, 2025

Effectively training deep learning models relies heavily on large datasets, as insufficient instances can hinder model generalization. A simple yet effective way to address this is by applying modern augmentation methods, they synthesize new data matching the input distribution while preserving semantic content. While these methods produce realistic samples, important issues persist concerning how well generalize across different classification architectures and their overall impact in accuracy improvement. Furthermore, relationship between dataset size accuracy, determination of an optimal level, remains open question field. Aiming challenges, paper, we investigate effectiveness eight methods—StyleGAN3, DCGAN, SAGAN, RandAugment, Random Erasing, AutoAugment, TrivialAugment AugMix—throughout several networks varying depth: ResNet18, ConvNeXt-Nano, DenseNet121 InceptionResNetV2. By comparing performance diverse datasets from leaf textures, medical imaging remote sensing, assess which offer superior generalization capability with no pre-trained weights. Our findings indicate that tool for dealing small achieving gains up 17%.

Language: Английский

Citations

0
Scour depth prediction around bridge piers of various geometries using advanced machine learning and data augmentation techniques DOI

El Mehdi El Gana,

Abdessalam Ouallali,

Abdeslam Taleb

et al.

Transportation Geotechnics, Journal Year: 2025, Volume and Issue: unknown, P. 101537 - 101537

Published: March 1, 2025

Language: Английский

Citations

0
Machine Learning and Sustainable Geopolymer Materials: A Systematic Review DOI Creative Commons

Ho Anh Thu Nguyen,

Duy Hoang Pham, Yonghan Ahn

et al.

Materials Today Sustainability, Journal Year: 2025, Volume and Issue: 30, P. 101095 - 101095

Published: March 6, 2025

Language: Английский

Citations

0
Machine Learning as an Innovative Engineering Tool for Controlling Concrete Performance: A Comprehensive Review DOI

Fatemeh Mobasheri,

Masoud Hosseinpoor, Ammar Yahia

et al.

Archives of Computational Methods in Engineering, Journal Year: 2025, Volume and Issue: unknown

Published: April 10, 2025

Language: Английский

Citations

0
Developing a rapid detection method for segmentation of durability-induced cracks using U-Net based deep learning models DOI
Yılmaz Yılmaz, Safa Nayır

Advances in Engineering Software, Journal Year: 2025, Volume and Issue: 207, P. 103950 - 103950

Published: May 1, 2025

Language: Английский

Citations

0
Augmented Data-Driven Approach towards 3D Printed Concrete Mix Prediction DOI Creative Commons
Saif Rehman, Raja Dilawar Riaz, Muhammad Usman

et al.

Applied Sciences, Journal Year: 2024, Volume and Issue: 14(16), P. 7231 - 7231

Published: Aug. 16, 2024

Formulating a mix design for 3D concrete printing (3DCP) is challenging, as it involves an iterative approach, wasting lot of resources, time, and effort to optimize the strength printability. A potential solution formulation through artificial intelligence (AI); however, being new emerging field, open-source availability datasets limited. Limited significantly restrict predictive performance machine learning (ML) models. This research explores data augmentation techniques like deep generative adversarial network (DGAN) bootstrap resampling (BR) increase available train three ML models, namely support vector (SVM), neural (ANN), extreme gradient boosting regression (XGBoost). Their was evaluated using R2, MSE, RMSE, MAE metrics. Models trained on BR-augmented showed higher accuracy than those DGAN-augmented data. The BR-trained XGBoost exhibited highest R2 scores 0.982, 0.970, 0.972, 0.971, 0.980 cast compressive strength, printed direction 1, 2, 3, slump flow respectively. proposed method predicting (mm), cast, anisotropic (MPa) can effectively predict printable concrete, unlocking its full application in construction industry.

Language: Английский

Citations

2