Results 181 to 190 of about 1,008,877 (311)

Enhancing concrete strength for sustainability using a machine learning approach to improve mechanical performance. [PDF]

Sci Rep
Khan A, Manan A, Umar M, Mehmood M, Onyelowe KC, Arunachalam KP.   +5 more
europepmc   +1 more source

The Strength of Fine-Aggregate Concrete [PDF]

, 1918
University of Texas at Austin
core   +1 more source

Hydration Behavior of Tricalcium Silicate in Seawater Relevant Salt Systems: A Hybrid Study with the Aid of Machine Learning

Advanced Intelligent Discovery, EarlyView.
The hydration behavior of C3S in seawater‐relevant solutions is studied based on experiments, boundary nucleation and growth (BNG) modeling, and machine learning. The main ions included in seawater modify hydration mechanisms, with MgCl2 showing the strongest acceleration effect at the same concentration.
Yanjie Sun, Chen Zhang, Ting Zhang, Mengze Hong, Peiliang Shen, Chi Sun Poon, Gang Li   +6 more
wiley   +1 more source

A Bio-Inspired Adaptive Probability IVYPSO Algorithm with Adaptive Strategy for Backpropagation Neural Network Optimization in Predicting High-Performance Concrete Strength. [PDF]

Biomimetics (Basel)
Zhang K, Li X, Zhang S, Zhang S.
europepmc   +1 more source

Accelerating Discovery of Organic Molecular Crystals via Materials Informatics and Autonomous Experiments

Advanced Intelligent Discovery, EarlyView.
Materials informatics and autonomous experimentation are transforming the discovery of organic molecular crystals. This review presents an integrated molecule–crystal–function–optimization workflow combining machine learning, crystal structure prediction, and Bayesian optimization with robotic platforms.
Takuya Taniguchi, Kazuki Ishizaki, Ryo Fukasawa   +2 more
wiley   +1 more source

Analyzing the influence of manufactured sand and fly ash on concrete strength through experimental and machine learning methods. [PDF]

Sci Rep
Sathvik S, Oyebisi S, Kumar R, Shakor P, Adejonwo O, Tantri A, Suma V.   +6 more
europepmc   +1 more source

AI‐BioMech: Deep Learning Prediction of Mechanical Behavior in Aperiodic Biological Cellular Materials

Advanced Intelligent Discovery, EarlyView.
AI‐BioMech is a deep learning framework that predicts the mechanical behavior of biological cellular materials directly from 2D images. By replacing traditional finite element analysis with semantic segmentation, it identifies stress and strain distributions with 99% accuracy, offering a high‐speed, scalable alternative for analyzing complex, aperiodic
Haleema Sadia, Marcelo A. Dias, Parvez Alam   +2 more
wiley   +1 more source

Soft computing models for prediction of bentonite plastic concrete strength. [PDF]

Sci Rep
Inqiad WB, Javed MF, Onyelowe K, Siddique MS, Asif U, Alkhattabi L, Aslam F.   +6 more
europepmc   +1 more source

BMPCQA: Bioinspired Metaverse Point Cloud Quality Assessment Based on Large Multimodal Models

Advanced Intelligent Systems, EarlyView.
This study presents a bioinspired metaverse point cloud quality assessment metric, which simulates the human visual evaluation process to perform the point cloud quality assessment task. It first extracts rendering projection video features, normal image features, and point cloud patch features, which are then fed into a large multimodal model to ...
Huiyu Duan, Kang Fu, Sijing Wu, Yunhao Li, Zicheng Zhang, Qiang Hu, Xiongkuo Min, Guangtao Zhai   +7 more
wiley   +1 more source

Relationship Between Schmidt Hammer Rebound Hardness Test and Concrete Strength Tests for Limestone Aggregate Concrete Based on Experimental and Statistical Study. [PDF]

Materials (Basel)
Tugrul Tunc E.
europepmc   +1 more source