Traditional ultrasonic non-destructive testing (NDT) techniques face dual challenges in industrial applications: imaging accuracy and imaging speed. The application of ultrasonic computed tomography (USCT) in the medical field provides a new approach for industrial NDT. Although this technique improves inversion accuracy, the intensive computation required for inversion limits its potential for online applications in industrial testing. To address these issues, this paper proposes a hardware system for ultrasonic tomography and a neural network inversion method integrating attention mechanisms, enabling the complete process from ultrasonic data acquisition to rapid reconstruction of cross-sectional images. The testbed controls the ultrasonic array elements to emit and receive signals, performing 360° scanning of the object under test and obtaining a signal matrix. After dimensionality reduction, the data is input into HAU2Net, which provides a cross-sectional image within milliseconds. This network incorporates attention heads at different encoding and decoding layers of U2Net and is trained with dynamically adjusted loss function constraints. It effectively processes the tomographic ultrasonic data collected by the hardware platform, extracting features and reconstructing the images. Compared with methods such as K-wave, SegNet, MgNO, and U2Net, experimental results show that the proposed HAU2Net outperforms others in terms of PSNR/SSIM metrics, model size, and prediction time. Additionally, robustness tests under noise and real-world conditions further validate the superior performance of HAU2Net. This research provides an efficient and high-precision solution for industrial ultrasonic NDT, with significant theoretical and practical implications.

Hyperspectral images (HSIs) and RGB multimodal information have proven effective for solid waste recognition. However, optimizing spectral band selection and feature extraction remains a challenge. This paper proposes an End-to-End Adaptive Fusion Network (E2E-AFNet) that integrates Dueling Double Deep Q Network (D3QN) with Near Infrared-RGB (NIR-RGB) feature extraction to achieve unified band selection and feature fusion. Using plastic waste as a case study, we design the Mask-D3QN SBS module to guide spectral input, which is processed by a multispectral feature extraction backbone. This backbone consists of a Multi-Scale Spectral Correlation Unit (MSC Unit) and a Multi-Scale Contour Feature Extraction Unit (MCF Unit), forming a dual-branch structure for feature decoupling. Additionally, the Mutual Attention Feature Interaction Module (MAFIM) efficiently fuses NIR-RGB features for object detection. A reward mechanism based on multimodal detection loss optimizes spectral input selection, enabling end-to-end adaptive fusion. Ablation results show that introducing the MSC and MCF modules improves the F1 score by 6.34 % and 6.45 %, respectively. Their joint use provides an additional similar to 0.4 % gain, and incorporating the MAFIM module further increases the F1 score by 0.58 %. Further experiments show that the unified band-selection and fusion framework E2E-AFNet outperforms traditional methods, achieving an mAP of 90.48 % and an mAR of 90.87 %. By effectively combining band selection with multi-modal fusion, this approach enhances feature completeness and improves detection performance.

This research was undertaken to address environmental concerns associated with industrial solid waste and to reduce cement consumption in geotechnical engineering. It specifically investigates the feasibility of using steel slag (SS) and silica fume (SF) as partial substitutes for ordinary Portland cement (OPC) in soil stabilization. The effects of SS, SF, OPC, and initial moisture content on the unconfined compressive strength (UCS) of stabilized soil were investigated through single-factor experiments and response surface methodology (RSM). The results show that SS and SF can synergistically enhance the strength of stabilized soil, although their interaction effect was not statistically significant within the investigated ranges. Compared with soil stabilized solely with OPC, the addition of 18 % SS and 10 % SF reduced OPC consumption by 3 % without compromising strength. Microstructural and compositional analyses further revealed that SS mainly supplied calcium- and silica-bearing components, while SF provided highly reactive silica and micro-filling effects, jointly promoting hydration reactions and improving the compactness of the stabilized soil matrix. As a result, more hydration products were formed in the OPC/SS/SF-stabilized soil than in the OPC-stabilized soil, which contributed to pore filling and strength enhancement. This study provides useful guidance for the sustainable utilization of industrial solid waste and the low-carbon development of soil stabilization materials.

The reliable monitoring of concrete discharge is hindered by the non-stationary spatiotemporal evolution of flow morphology, where discriminative evidence for anomalies drifts and emerges across distinct process stages. Traditional discrete-time deep learning models, which mitigate temporal sequence learning by treating video frames as uniform data points, fail to capture this inherent physical continuity, identifying anomalies with structural confusion in fine-grained severity assessment. To address this, this study proposes the Stage-aware Continuous-Time Propagation Network (SCTP-Net), a framework that translates the engineering knowledge of continuous material flow into a computational model. Unlike standard discrete approaches, our architecture models the unloading process through a process-inspired dynamic formulation rather than treating video frames as uniformly aggregated observations. We introduce Closed-form Continuous-time Neural Networks (CFC) to represent the continuous intra-stage evolution of the discharge, together with a dual mechanism for inter-stage propagation that encodes evidence accumulation across stages. By integrating these engineering priors-continuity and stage-dependency-with a dynamic fusion strategy, SCTP-Net provides a robust informatics representation of the complex industrial environment. Validated on a six-class discharge dataset under rigorous statistical protocols, the method achieves 93.00 +/- 2.05% Accuracy, outperforming strong CNN + TCN baselines by an 11% margin. These results demonstrate that incorporating process-inspired continuous-time modeling and stage-aware evidence propagation into neural architectures significantly enhances the interpretability and reliability of automated engineering monitoring systems.

This study proposes a predictive methodology that integrates discrete-element simulation with experimental validation to address unstable product size distribution and inefficient manual parameter adjustment in cone crushers. Using porphyritic granite as the research material, the Tavares breakage model parameters were calibrated and validated through uniaxial compression and particle bed breakage tests. A discrete element method (DEM) model of a cone crusher was then developed using these calibrated parameters. The effects of feed size (F10 and F80), closed side setting (CSS), and shaft speed on product size (P10 and P80) and throughput were systematically investigated. Subsequently, a multi-factor weighted prediction model for product size (P10, P80) and throughput was developed using the simulation data. This preliminary model was then refined by incorporating experimental data to improve its predictive accuracy. The results demonstrated that the refined model accurately predicts product size and throughput under various operating conditions, showing good agreement with experimental measurements. This work provides a cost-effective and efficient methodology for parameter optimization and model-based control of cone crushers, enabling setpoint optimization and real-time adjustment of operating parameters.

To improve the recycling efficiency of construction and excavation waste, this study proposes a chain crusher capable of simultaneous soil disaggregation and powder mixing. A coupled discrete element method (DEM) and multi-body dynamics (MBD) framework was established, incorporating the Tavares breakage model and the Archard wear model, to investigate the effects of chain speed, chain layer spacing, chain length, and chain shape on the crushing ratio, cement mixing uniformity, and equipment wear. A volume-fraction-weighted relative standard deviation (RSD) was introduced to accurately evaluate mixing uniformity amidst significant particle size disparities. A Box-Behnken design was conducted with the crushing ratio and the RSD of cement as the response variables to study the influence of different parameters. The results indicate that higher chain speeds and longer chains enhance soil disaggregation, while reduced layer spacing improves cement mixing uniformity. The composite chain shape achieved a good balance between crushing performance and wear resistance. The NSGA-II algorithm yielded an optimal configuration that, relative to the reference design, increased the crushing ratio by 16.8% and reduced the RSD by 18.1%, with prediction errors below 2.4%. These optimization results promote high-quality particle refinement and homogeneity, validating the high efficiency of the simultaneous crushing and mixing process. This work provides a theoretical foundation and practical guidance for the design and optimization of a chain crusher. (c) 2026 Published by Elsevier B.V. on behalf of The Society of Powder Technology Japan. All rights are reserved, including those for text and data mining, AI training, and similar technologies.

Efficient separation in construction and demolition waste (CDW) is a key step to improve recycled aggregate quality and reduce landfill disposal. In this study, a coupled computational fluid dynamics-discrete element method (CFD-DEM) was employed to systematically investigate the motion behavior and separation mechanisms of representative non-spherical CDW particles in a pneumatic separator. The effects of key operational and structural parameters-air velocity, separation zone width, and flap angle on the aggregate loss rate and light material separation efficiency were quantitatively analyzed using response surface methodology (RSM). The results show that increasing the airflow velocity has a more pronounced effect on the separation performance of wood particles with relatively regular shapes, whereas its influence on irregularly shaped particles such as Polypropylene-Homopolymer is comparatively weaker. A wider separation zone increases the kinetic energy dissipation of particles in each component, leading to a deterioration in separation performance. Increasing the flap angle can effectively obstruct the trajectories of all particle components, reducing material loss at the expense of separation efficiency. Based on the established predictive models, multi-objective optimization via the NSGA-II algorithm identified an optimal configuration: air velocity of 34.4 m/s, separation zone width of 829 mm, and flap plate angle of 60.5 degrees. Compared with the initial operating configuration, the aggregate loss rate decreased by 56.6%, while the separation efficiency increased by 23.9%. The optimized configuration significantly reduces material waste and energy intensity, offering a theoretical basis for designing cleaner and more efficient CDW recycling facilities.

Conventional phased-array ultrasound can detect defects in nuts; however, accurately reconstructing their irregular shapes and precise spatial structures remains challenging. To faithfully recover the structural distribution of nut cross sections, a dedicated ultrasound tomographic imaging system and a corresponding reconstruction method were developed to generate spatially resolved images of nuts with irregular surfaces. The imaging process includes three steps. First, as the nut rotates on the experimental platform, the ultrasonic array elements transmit and receive signals to form a signal matrix. Second, the collected sparse data are interpolated using the proposed adaptive interpolation algorithm and then reconstructed into an image through filtered back-projection. Finally, the reconstructed image is processed with a diffusion modelbased super resolution (SR) algorithm to produce a high-resolution, large-scale tomographic image. Employing a 5 MHz, 64-element linear array with water as the coupling medium for signal acquisition, the proposed imaging algorithm achieves optimal structural similarity index (SSIM) and peak signal-to-noise ratio (PSNR) values of 0.961 and 29.264 after adaptive interpolation under noise-free conditions. Following SR processing, it attains superior no-reference quality scores with natural image quality evaluator (NIQE), CLIP-based image quality assessment (CLIPIQA), and blind/reference-less image spatial quality evaluator (BRISQUE) scores of 2.4933, 0.6655, and 34.5602, outperforming conventional SR methods across these metrics. These results demonstrate superior performance in image quality. Physical experiments further indicate that the system can produce high-precision tomographic images of nuts with minimal signal sampling, transmission, and storage, highlighting its practical application potential.

The recycling of substantial waste concrete from urbanization and demolition into high-quality recycled aggregate is crucial for resource utilization. This study investigates the preparation of recycled coarse aggregate using a kneading crusher through a combination of numerical simulation and experimental validation. A coupled EDEM-Adams simulation framework, incorporating a DEM-based particle crushing model with cement paste removal effects, was established to analyze the influence of eccentric shaft phase difference (0 degrees-180 degrees) and rotational speed (160-320 r/min). The underlying crushing mechanism was elucidated by analyzing the relative motion trajectories of the jaw plates. Experimental trials validated the high fidelity of the simulation model. Optimal processing was achieved at a 45 degrees phase difference and a 280 r/min rotational speed, under which the water absorption of the recycled coarse aggregate was reduced by a maximum of 21.1 %. This result demonstrates the method's efficacy in detaching residual cement paste and producing high-quality aggregate.

The void ratio of aggregate packing has a significant impact on the performance of concrete. Traditional methods for detecting void ratio are inefficient and cannot be performed online. In this study, materials with different particle shapes were prepared using the Los Angeles abrasion machine. The improved instance segmentation network SE-Transfiner and the image classification network ResNet were used to segment and classify aggregate images, respectively. This approach allowed for the online calculation of the particle shape and grading of coarse aggregates. A Support Vector Regression model was constructed and optimized using a Particle Swarm Optimization algorithm to predict the void ratio. Experimental results showed that the segmentation accuracy of SETransfiner was improved compared to Mask R-CNN, achieving an IoU of over 90% in different particle size ranges compared to OpenCV hard segmentation. The grading detection error of ResNet was reduced from 12 % to within 3 % compared to the instance segmentation network, and the prediction error of the void ratio model was within +/- 0.5%. These results demonstrate that the proposed model can effectively predict the void ratio of coarse aggregates with high accuracy and practical application value.

The classification and recycling of municipal solid waste (MSW) are strategies for resource conservation and pollution prevention, with plastic waste identification being an essential component of waste sorting. Multimodal detection of solid waste has increasingly replaced single-modal methods constrained by limited informational capacity. However, existing hyperspectral feature selection algorithms and multimodal identification methods have yet to leverage cross-modal information exhaustively. Therefore, two RGB-hyperspectral image (RGB-HSI) multimodal instance segmentation datasets were constructed to support research in plastic waste sorting. A feature band selection algorithm based on the Activation Weight function was proposed to automatically select influential hyperspectral bands from multimodal data, thereby reducing the burden of data acquisition, transmission, and inference. Furthermore, the multimodal Selective Feature Network (SFNet) was introduced to balance information across various modalities and stages. Moreover, the Correlation Swin Transformer Block was proposed, specifically crafted to fuse cross-modal mutual information, which can be synergistically employed with SFNet to enhance multimodal recognition capabilities further. Experimental results show that the Activation Weight band selection function can select the most effective feature bands. At the same time, the Correlation SF-Swin Transformer achieved the highest F1-scores of 97.85% and 97.37% in the two plastic waste object detection experiments, respectively. The source code and final models are available at https://github.com/Bazenr/Correlation-SFSwin, and the dataset can be accessed at https://www.kaggle.com/datasets/bazenr/rgb-hsi-rgb-nir-municipal-solid-waste.

The hydrocyclone is pivotal in mineral processing for particle separation, yet challenges such as flow instability and particle misplacement persist. This study systematically investigates the impact of feed body geometry on the internal flow behavior and separation performance of the hydrocyclone, with a focus on optimizing an involute feed body. As verified by computational fluid dynamics (CFD) simulations and experimental data, the involute feed design demonstrates superior performance. Specifically, the effective separation space within the cyclone is increased by 21.3 %, energy consumption is reduced by 6.3 %, and the tangential velocity distribution is enhanced. Key operational parameters (feed concentration, underflow orifice diameter and overflow orifice diameter) were optimized using response surface methodology (RSM). Results revealed that increasing feed concentration and overflow orifice diameter reduced fine particle entrainment in the underflow by 8.6 % and 6.8 %, respectively, while enlarging the underflow orifice diameter compromised separation efficiency. Optimal conditions (24.6 % feed concentration, 41.2 mm underflow, and 117.8 mm overflow) achieved a 3.9 % underflow mud content, significantly improving mud removal performance. This work provides actionable insights for designing a high-efficiency hydrocyclone for separation industries, addressing both energy consumption and separation precision.

This study established a three-dimensional discrete element method (3D DEM) of cementtreated base materials (CTBM), considering the morphology of recycled crushed aggregates (RCAs) derived from construction and demolition waste. Coarse RCA morphology was obtained using X-ray computed tomography and integrated into the DEM model. The linear contact model and linear parallel bond model were selected and key microparameters were determined. The developed 3D DEM model was verified through the actual indirect tensile test of CTBM. Micromechanical analysis, encompassing contact force and displacement fields of particles, was subsequently evaluated based on the virtual uniaxial compression test. The findings revealed that particles near the loading plate exhibited relatively higher contact force, gradually decreasing with distance from the loading position. Regarding the displacement field, the particles closer to the edge of the cross-section and the loading plate on the longitudinal section experienced greater displacement, whereas those in the middle of the specimen and farther from the loading plate had smaller displacements. Furthermore, it was observed that increasing the cement content effectively enhanced the internal contact force and the ability of CTBM to resist uniaxial deformation.

Slump is an important index for concrete fluidity, which has a direct guiding effect on construction. In recent years, using RGB images for evaluating slump has been confirmed by scholars. Based on previous studies, this paper investigates the superiority of RGB-D image data over RGB image data in predicting slump of concrete and proposes three RGB-D fusion models: The early-stage-fusion model performs feature fusion in the data input stage, while the fully-connected-layer-fusion model performs feature fusion in the classification layer and the middle-stage-fusion model performs feature fusion after each residual block. In the classification of slump 120 mm, 150 mm and 200 mm, the Precision, Recall and F1-score are used to evaluate the model's ability to classify a single class, and the Accuracy, Macro-F1, Kappa and MCC are used to evaluate the model's performance. The experimental results showed that compared with the model using only RGB images, the fusion model achieve better performance, indicating that RGB-D image data can better evaluate concrete slump.

The fineness modulus(FM) represents the level of particle size of manufactured sand. Real-time feedback of FM of manufactured sand is important for industrial sand production, but extracting the particle profile from densely stacked images is a great challenge. In this study, a deep learning and regression analysis -based online measurement method for FM of manufactured sand is proposed. Firstly, the real fineness modulus of the sand produced by the sand -making machine in real time was obtained by the vibration -screening method(VSM). Then, the particle size fraction of larger particles (0.6-4.75 mm) was obtained based on machine vision combined with a convolutional neural network and image processing. Secondly, a multiple linear regression model was developed for the percentage of particle size and FM. Finally, the percentage of particle size was substituted into the regression model as the independent variable to achieve a fast prediction of the unknown FM. The experimental results show that the maximum repeatability errors for FM of different manufactured sands are 0.09 and 0.13 respectively, and the maximum absolute errors of the FM prediction results are 0.18 and 0.17 respectively. The calculation efficiency and error level of this research method can meet the online testing at sand making sites.

The carbonation of cementitious materials with CO2 2 was utilised to prepare fluid solidified soil, and the characteristics of fluid solidified soil were investigated. Experimental tools such as flow extensibility, unconfined compressive strength, thermogravimetric analysis, and scanning electron microscopy were employed to explore the influence laws of different carbon dioxide pressures, cement dosages, and initial water contents on the strength properties and microstructural evolution of fluid consolidated soils. The results showed that with the increase of CO2 2 pressure, the flow characteristics of carbonated fluid solidified soil decreased and the unconfined compressive strength increased. This is due to the fact that after the carbonation process, the formation of carbonation products such as calcium carbonate and hydration products in the fluid solidified soil significantly improves the microstructure of the soil, which is the main reason for the increase in its strength. In addition, the carbonation test revealed that the ratio of the amount of COQ generated to the mass of cement was as high as 18.36 % under the condition of COQ pressure up to 0.20 MPa, which fully proved the high efficiency of the carbonation technology. Therefore, the carbonation technology has great potential and broad application prospects in optimising the performance of fluid solidified soil as well as achieving effective carbon sequestration.

This article focuses on the significant challenges faced by single-modal visual sensors in object detection under the fine-sorting process of solid waste. Object detection using a single sensor has inherent limitations due to the lack of comprehensive material and contour information about the object. Therefore, this article proposes a real-time detection network based on the MSI-RGB dual-source multiscale fusion network (DMFNet) and introduces a coarse-to-fine dual-stage band selection (dual-stage BS) method for reducing spectral redundant information. As a case study, we focus on plastic solid waste and build a dual-sensor experimental platform to collect color and hyperspectral images (HSIs) using a face-matrix color camera and a hyperspectral camera. Specifically, the dual-stage BS method is employed to analyze the hyper-spectral data and a small number of spectral bands were selected to generate a multispectral image (MSI) as the first input source for DMFNet. Meanwhile, the RGB image serves as the second input source for DMFNet. First, a spectral image feature extraction backbone (SIFEBackbone) and a multiscale stacking unit (MSU) are designed to extract multiscale features from spectral images fully. Second, a multibranch fusion unit (MBFU) is designed to realize the fusion of multiscale features with the spectral feature maps in the RGB-enhanced feature extraction stage. The experimental results show that dual-stage BS exhibits advantages over other methods, while DMF-YOLOv7, which integrates DMFNet and YOLOv7 detectors, improves the average precision and recall by 7.2% and 5.5%, respectively, compared with the original YOLOv7, and the mAP reaches 95.4% at IOU =0.5. It effectively reduces the material misrecognition problem and reflects the advantages of the dual-sensor algorithm. The source code is available at: https://github.com/Caicai-D/DMFNet.

To quickly measure the water absorption (WA) of Recycled Coarse Aggregates (RCA), we utilize a detection platform designed for RCA to collect two-dimensional images. Utilizing the RCA-net network, we segment the areas of the mortar and aggregate on the RCA surface. Segmentations allow us to extract critical parameters for characterizing the quality of RCA, the proportion of mortar area (PMA). Subsequently, we construct three regression functions between PMA and WA. The experimental results demonstrate that our proposed segmentation method effectively separates both adhered particles of RCA and distinct areas of mortar and aggregate on RCA surfaces. Next, sprinkling water on RCA surfaces can enhance the accuracy of the segmentation. Notably, within particle size ranges of 5-10 mm, 10-20 mm, and 20-31.5 mm, we all observed a significant linear relationship between PMA and WA. We used those linear relationships and the equivalent mass of RCA detected by the image method in each particle size range to construct the prediction model of water absorption. According to the validation result of 24 groups RCA, this model's maximum relative error of RCA water absorption predicted value was 10.6 %. The detection time of this method is short, and the detection time of 2 kg RCA is 3.8 min, with an average computation time per image of merely 0.659 s. This efficiency fulfills the requirements for real-time industrial inspection.

The development of urbanization has brought convenience to people, but it has also brought a lot of harmful construction solid waste. The machine vision detection algorithm is the crucial technology for finely sorting solid waste, which is faster and more stable than traditional methods. However, accurate identification relies on large datasets, while the datasets from the field working conditions are scarce, and the manual annotation cost of datasets is high. To rapidly and automatically generate datasets for stacked construction waste, an acquisition and detection platform was built to automatically collect different groups of RGB-D images for instances labeling. Then, based on the distribution points generation theory and data augmentation algorithm, a rapid-generation method for synthetic construction solid waste datasets was proposed. Additionally, two automatic annotation methods for real stacked construction solid waste datasets based on semi-supervised self-training and RGB-D fusion edge detection were proposed, and datasets under real-world conditions yield better models training results. Finally, two different working conditions were designed to validate these methods. Under the simple working condition, the generated dataset achieved an F1-score of 95.98, higher than 94.81 for the manually labeled dataset. In the complicated working condition, the F1-score obtained by the rapid generation method reached 97.74. In contrast, the F1-score of the dataset obtained manually labeled was only 85.97, which demonstrates the effectiveness of proposed approaches.

The size of the discharge outlet of a cone crusher directly impacts the size of the aggregate produced. However, the discharge outlet is still adjusted manually, which has a significant error and affects production efficiency. For this reason, this study proposed an adaptive control method for cone crushers based on aggregate online detection. Firstly, the aggregate image was segmented using an instance segmentation model and the anchor and structure of the model were optimised. Then, this study proposed an evaluation method for quickly and accurately assessing the overall segmentation effect of network models. By comparing the results with those before optimisation, the accuracy of the optimised network model was improved from 0.923 to 0.940. Finally, an adaptive control experiment was conducted based on the online aggregate detection results. The experimental results showed that the discharge particle size distribution of the cone crusher becomes more stable after intelligent control is added, with the variance of the proportion of cumulative gradation at 15 mm decreased from 34.3 to 14.4. These results indicated that the developed adaptive control system effectively controls the fine processing of coarse aggregates and significantly improves the quality of aggregate crushing and processing.