Results 211 to 220 of about 116,061 (328)

Flexural behavior of rubberized concrete beams: Insights from experimental and numerical investigations

open access: yesStructural Concrete, EarlyView.
Abstract Rubberized concrete (RuC), which incorporates recycled tire rubber aggregates in the matrix, offers a viable solution for managing waste tire disposal. Although adding rubber aggregates reduces the compressive strength of concrete, research suggests that in specific applications, such as flexure‐controlled members, this adverse effect can be ...
Ernesto Hernández   +3 more
wiley   +1 more source

Toward transparent AI: Predicting strength of fly ash foam composite concrete using explainable ML models

open access: yesStructural Concrete, EarlyView.
Abstract Fly ash foam composite concrete (FFC) is a sustainable, lightweight alternative to traditional concrete. However, accurately predicting its compressive strength (CS) through conventional laboratory methods is challenging due to its non‐linear behavior induced by the addition of foam content and fly ash. Also, the laboratory determination of CS
Atta Ullah   +5 more
wiley   +1 more source

New insights into one of the oldest glacial deposits in the northern Alpine foreland (Höchsten, SW Germany)

open access: yesBoreas, EarlyView.
Diamictic deposits within a presumed Early Pleistocene overdeepened basin are investigated with a combined sedimentological‐geotechnical approach including analysis via μCT scans. Sedimentary facies, geotechnical properties, and microstructures reflect a transition from glacier‐proximal deposition towards direct ice contact and subglacial deformation ...
Clare A. Bamford   +5 more
wiley   +1 more source

Three‐dimensional morphological analysis of Chang'e‐5 lunar soil using deep learning‐automated segmentation on computed tomography scans

open access: yesComputer-Aided Civil and Infrastructure Engineering, EarlyView.
Abstract Grain morphology is a fundamental characteristic of lunar soil that influences its mechanical properties, sintering behavior, and in situ resource utilization. However, traditional two‐dimensional imaging methods are time‐consuming and lack full three‐dimensional (3D) structural information. This study presents an automated deep learning‐based
Siqi Zhou   +6 more
wiley   +1 more source

A step toward a micromechanics‐informed neural network for predicting asphalt mixture stiffness

open access: yesComputer-Aided Civil and Infrastructure Engineering, EarlyView.
Abstract Asphalt mixtures show complex mechanical behavior due to their heterogeneous structure. Traditionally, the mechanical characterization of asphalt mixture is done through laboratory testing or micromechanical modeling. While laboratory tests and micromechanical models provide reliable measurements and physical interpretability, they are often ...
Kumar Anupam   +4 more
wiley   +1 more source

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