Accurate estimation of concrete (including shotcrete) consumption plays a crucial role in tunnel construction. A novel method has been introduced to accurately estimate concrete consumption with terrestrial laser scanning (TLS). The estimation needs to capture TLS data of tunnel surfaces at different stages of construction. Unrolling point clouds, a novel two-stage algorithm consisting of noise removal and hole filling has been used to generate resampled points. Furthermore, resampled points from two scans (before and after lining construction) ultimately generate an innovative computation model composed of multiple hexahedral elements, which is used for calculating volumes. The proposed technique was applied to the Tiantaishan highway tunnel and Da Fang Shan high-speed railway tunnel. The calculation relative error of the rebound rate is 0.19%, and the average relative error in predicting the demand for secondary lining concrete is 0.15%. Compared with 3D Delaunay with curve fitting, the proposed technique offers a more straightforward operation and higher accuracy. Considering factors such as tunnel geometry, support design, and concrete properties, a computational model will provide valuable insights into optimizing resource allocation and reducing material waste during construction.
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http://dx.doi.org/10.1038/s41598-023-51132-x | DOI Listing |
Sci Rep
December 2024
Computer Science Department, Saarland University, Saarbrücken, Germany.
Estimating the numbers and whereabouts of internally displaced people (IDP) is paramount to providing targeted humanitarian assistance. In conflict settings like the ongoing Russia-Ukraine war, on-the-ground data collection is nevertheless often inadequate to provide accurate and timely information. Satellite imagery may sidestep some of these challenges and enhance our understanding of the IDP dynamics.
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December 2024
Imperial College London, London, UK.
Accurate estimation of the soil resilient modulus (M) is essential for designing and monitoring pavements. However, experimental methods tend to be time-consuming and costly; regression equations and constitutive models usually have limited applications, while the predictive accuracy of some machine learning studies still has room for improvement. To forecast M efficiently and accurately, a new model named black-winged kite algorithm-extreme gradient boosting (BKA-XGBOOST) is proposed.
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December 2024
Harman International, HarmanX Neurosense, 30001 Cabot Dr, Novi, MI, 48377, USA.
Cognitive load (CL) is one of the leading factors moderating states and performance among drivers. Heavily increased CL may contribute to the development of mental stress. Averaged heart rate (HR) and heart rate variability (HRV) indices are shown to reflect CL levels in different tasks.
View Article and Find Full Text PDFNat Commun
December 2024
School of Data Science, The Chinese University of Hong Kong-Shenzhen, Shenzhen, China.
Recently, RNA velocity has driven a paradigmatic change in single-cell RNA sequencing (scRNA-seq) studies, allowing the reconstruction and prediction of directed trajectories in cell differentiation and state transitions. Most existing methods of dynamic modeling use ordinary differential equations (ODE) for individual genes without applying multivariate approaches. However, this modeling strategy inadequately captures the intrinsically stochastic nature of transcriptional dynamics governed by a cell-specific latent time across multiple genes, potentially leading to erroneous results.
View Article and Find Full Text PDFJ Chem Inf Model
December 2024
Dipartimento di Bioscienze, Università degli Studi di Milano, Milan 20133, Italy.
The computational study of ligand binding to a target protein provides mechanistic insight into the molecular determinants of this process and can improve the success rate of drug design. All-atom molecular dynamics (MD) simulations can be used to evaluate the binding free energy, typically by thermodynamic integration, and to probe binding mechanisms, including the description of protein conformational dynamics. The advantages of MD come at a high computational cost, which limits its use.
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