Mesoscale Modeling of Spallation Failure in Fiber-Reinforced Concrete Slab due to Impact Loading

In this paper, a model for damage and fracture, considering the heterogeneity of material properties, is validated and used to investigate the mechanism of spallation in fiber-reinforced concrete (FRC) slabs with various fiber and aggregate contents under impact loading. Numerical simulations show that there is a marked difference in the failure patterns among slabs with various fiber and aggregate contents, and the fibers can remarkably improve the tensile strength of the concrete slab, effectively prevent the initiation and propagation of cracks, and inhibit the occurrence of spallation. Moreover, numerical simulations capture the whole process of the propagation of incident compressive stress waves in the FRC and the reflection of stress waves upon concrete surfaces and the spallation failure of FRC induced by the reflected tensile stress wave, which is obviously different from the failure pattern of FRC under static loads. The results of this study can also provide a valuable reference for studies on the tensile properties and failure modes of heterogeneous quasi-brittle materials and the design of FRC slabs with appropriate fiber contents

Full-field quantification of time-dependent and -independent deformation and fracturing of double-notch flawed rock using digital image correlation

International audienc

Effect of pH on primary and secondary crack propagation in sandstone under constant stress (creep) loading

International audienceTime-dependent, chemically assisted crack propagation behavior in rocks is fundamental to understanding the long-term stability of civil engineering structures. In this study, we investigated the propagation of primary and secondary cracks in macrofractured sandstone in distilled water (pH = 5.6) and in an aqueous solution of hydrochloric acid (HClaq, pH = 1.82) using digital image correlation (DIC), comparing the results to those from experiments performed in air. Results show that for the macrofracture angles γ = 0 and 90°, the nucleation position r of the primary crack is not affected by pH, and always occurs at the fracture tip (r = 0) and fracture center (r = 1), respectively. For γ = 30, 45, and 60°, r increases quasi-linearly with the increase of γ in semi-log plots, and an increase in pH moves the primary crack away from the fracture tip. For a given γ, an increase in pH produces an increase in the kink angle k1 of the secondary crack. The influence of pH on secondary crack propagation is most pronounced at high γ values. As the pH increases, the location of the secondary cracks gradually turns counterclockwise with a decreasing swing range, from 37° at pH 1.82–35° at pH 5.6. The secondary crack is inclined at a maximum of 30° and 32° to the maximum principal stress direction at pH 1.82 and pH 5.6, respectively. Right-lateral shear secondary cracks with well-developed tensile tail fractures are more prevalent in higher pH environments. The comparison with dry experiments indicates that distilled water is more aggressive than HClaq. The significant loss of calcium ions in distilled water and the reduction of large pore volume fraction in HClaq are considered the main reasons for the deterioration of the pH effect

A low-cost and robust optical flow CMOS camera for velocity estimation

This paper presents a robust velocity estimation algorithm applicable to low-cost monocular platform with a tri-axial gyroscope. The algorithm is developed based on PX4FLOW platform which is an open software and open hardware solution for monocular speed estimation. The paper provides three main contributions. The first is the improved robustness achieved by various methods including feature block selection, feed-forward rotation, motion compensation, failure case detection and filtering. The second contribution is the improved performance in terms of resolution and search range achieved by adaptive frame interval and advanced search algorithm. The final contribution is a systematical strategy for parameter selection to fully utilize the hardware resources and optimize the performance in different contexts. The performance was benchmarked against the PX4FLOW and GPS. Results indicated that the proposed algorithm achieved levels of robustness exceeding that of PX4FLOW in measuring velocity. Also, the integrated trajectory agreed with the position calculated by an on-board GPS system. © 2013 IEEE

Rock Stability Assessment Based on the Chronological Order of the Characteristic Acoustic Emission Phenomena

Sudden inelastic deformations in rock are associated with acoustic emission (AE). Therefore, AE monitoring technique can be used to study the fracture processes of rock. In this paper, AE tests were conducted on the granitic gneiss specimens under the uniaxial compressive loading conditions. The temporal changes in AE hit parameters and spatial-temporal evolution of AE events during the failure process of the granitic gneiss specimens were studied, and several characteristic AE phenomena (i.e., dramatic increase in dominant frequency, AE energy, and hit rate, the AE event with a high energy level, and the through-going distribution of the AE events with intermediate energy levels) were statistically analyzed before the failure occurred. It was found that the chronological order of the characteristic AE phenomena was relatively certain and correspondingly had a close relationship with the crack development stage. Because of the difference of the stress level at each crack development stage, the stability at different crack development stages is different. Therefore, a rock stability assessment approach was established based on the chronological order of the characteristic AE phenomena, and then the rock stability was assessed using the proposed approach

A Virtual Crack-Based Numerical Manifold Approach to Crack Initiation, Propagation and Coalescence in Granite

International audienc

Corn Residue Covered Area Mapping with a Deep Learning Method Using Chinese GF-1 B/D High Resolution Remote Sensing Images

Black soil is one of the most productive soils with high organic matter content. Crop residue covering is important for protecting black soil from alleviating soil erosion and increasing soil organic carbon. Mapping crop residue covered areas accurately using remote sensing images can monitor the protection of black soil in regional areas. Considering the inhomogeneity and randomness, resulting from human management difference, the high spatial resolution Chinese GF-1 B/D image and developed MSCU-net+C deep learning method are used to mapping corn residue covered area (CRCA) in this study. The developed MSCU-net+C is joined by a multiscale convolution group (MSCG), the global loss function, and Convolutional Block Attention Module (CBAM) based on U-net and the full connected conditional random field (FCCRF). The effectiveness of the proposed MSCU-net+C is validated by the ablation experiment and comparison experiment for mapping CRCA in Lishu County, Jilin Province, China. The accuracy assessment results show that the developed MSCU-net+C improve the CRCA classification accuracy from IOUAVG = 0.8604 and KappaAVG = 0.8864 to IOUAVG = 0.9081 and KappaAVG = 0.9258 compared with U-net. Our developed and other deep semantic segmentation networks (MU-net, GU-net, MSCU-net, SegNet, and Dlv3+) improve the classification accuracy of IOUAVG/KappaAVG with 0.0091/0.0058, 0.0133/0.0091, 0.044/0.0345, 0.0104/0.0069, and 0.0107/0.0072 compared with U-net, respectively. The classification accuracies of IOUAVG/KappaAVG of traditional machine learning methods, including support vector machine (SVM) and neural network (NN), are 0.576/0.5526 and 0.6417/0.6482, respectively. These results reveal that the developed MSCU-net+C can be used to map CRCA for monitoring black soil protection

Monitoring the Damage of Armyworm as a Pest in Summer Corn by Unmanned Aerial Vehicle Imaging

BACKGROUND: The timely, rapid, and accurate near real-time observations are urgent to monitor the damage of corn armyworm, because the rapid expansion of armyworm would lead to severe yield losses. Therefore, the potential of machine learning algorithms for identifying the armyworm infected areas automatically and accurately by multispectral Unmanned Aerial Vehicle (UAV) dataset is explored in this study. And the study area is in Beicuizhuang Village, Langfang City, Hebei Province, which is the main corn-producing area in the North China Plain. RESULTS: Firstly, we identified the optimal combination of image features by Gini-importance and the comparation of four kinds of machine learning methods including Random Forest (RF), Multilayer Perceptron (MLP), Naive Bayes Classifier (NB) and Support Vector Machine (SVM) was done. And RF was proved to be the most potential with the highest Kappa and OA of 0.9709 and 0.9850, respectively. Secondly, the armyworm infected areas and healthy corn areas were predicted by an optimized RF model in the UAV dataset, and the armyworm incidence levels were classified subsequently. Thirdly, the relationship between the spectral characteristics of different bands and pest incidence levels within the Sentinel-2 and UAV images were analyzed, and the B3 in UAV images and the B6 in Sentinel-2 image were less sensitive for armyworm incidence levels. So the Sentinel-2 image was used to monitor armyworm in two towns. CONCLUSIONS: The optimized dataset and RF model are effective and reliable, which can be used for identifying the corn damage by armyworm using UAV images accurately and automatically in field-scale

A 30-m annual corn residue coverage dataset from 2013 to 2021 in Northeast China

Abstract Crop residue cover plays a key role in the protection of black soil by covering the soil in the non-growing season against wind erosion and chopping for returning to the soil to increase organic matter in the future. Although there are some studies that have mapped the crop residue coverage by remote sensing technique, the results are mainly on a small scale, limiting the generalizability of the results. In this study, we present a novel corn residue coverage (CRC) dataset for Northeast China spanning the years 2013–2021. The aim of our dataset is to provide a basis to describe and monitor CRC for black soil protection. The accuracy of our estimation results was validated against previous studies and measured data, demonstrating high accuracy with a coefficient of determination (R2) of 0.7304 and root mean square error (RMSE) of 0.1247 between estimated and measured CRC in field campaigns. In addition, it is the first of its kind to offer the longest time series, enhancing its significance in long-term monitoring and analysis