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Pavement cracking is a primary cause of early damage in asphalt pavements, and fiber-reinforcement technology is an effective method for enhancing the anti-cracking performance of pavement mixtures. However, due to the multi-scale dispersed structure of pavement mixtures, it is challenging to address cracking and damage with a single fiber type or fibers of the same scale. To investigate the toughening mechanisms and damage behavior of hybrid-fiber-modified mixtures, we analyzed the fracture process and damage behavior of these mixtures using a combination of basalt fiber and calcium sulfate whisker hybrid fiber modification, along with semicircular bending tests. Additionally, digital imaging was employed to examine the fracture interface characteristics, revealing the toughening mechanisms at play. The results demonstrated that basalt fibers effectively broaden the toughness range of the modified mixture at the same temperature, reduce mixture stiffness, increase residual load at the same displacement, and improve crack resistance in the mixture matrix. While calcium sulfate whiskers enhanced the peak load of the mixture, their high stiffness modulus was found to be detrimental to the mixture's crack toughness. The fracture interface analysis indicated that the three-dimensionally distributed fibers form a spatial network within the mixture, restricting the relative movement of cement and aggregate, delaying crack propagation, and significantly improving the overall crack resistance of the mixture. [ABSTRACT FROM AUTHOR]

To investigate the interplay among technological innovation, industrial structure, production methodologies, economic growth, and environmental consequences within the paradigm of a green economy and to put forth strategies for sustainable development, this study scrutinizes the limitations inherent in conventional deep learning networks. Firstly, this study analyzes the limitations and optimization strategies of multi-layer perceptron (MLP) networks under the background of the green economy. Secondly, the MLP network model is optimized, and the dynamic analysis of the impact of technological innovation on the digital economy is discussed. Finally, the effectiveness of the optimization model is verified by experiments. Moreover, a sustainable development strategy based on dynamic analysis is also proposed. The experimental results reveal that, in comparison to traditional Linear Regression (LR), Decision Tree (DT), Random Forest (RF), Support Vector Machine (SVM), and Naive Bayes (NB) models, the optimized model in this study demonstrates improved performance across various metrics. With a sample size of 500, the optimized model achieves a prediction accuracy of 97.2% for forecasting future trends, representing an average increase of 14.6%. Precision reaches 95.4%, reflecting an average enhancement of 19.2%, while sensitivity attains 84.1%, with an average improvement of 11.8%. The mean absolute error is only 1.16, exhibiting a 1.4 reduction compared to traditional models and confirming the effectiveness of the optimized model in prediction. In the examination of changes in industrial structure using 2020 data to forecast the output value of traditional and green industries in 2030, it is observed that the output value of traditional industries is anticipated to decrease, with an average decline of 11.4 billion yuan. Conversely, propelled by the development of the digital economy, the output value of green industries is expected to increase, with an average growth of 23.4 billion yuan. This shift in industrial structure aligns with the principles and trends of the green economy, further promoting sustainable development. In the study of innovative production methods, the green industry has achieved an increase in output and significantly enhanced production efficiency, showing an average growth of 2.135 million tons compared to the average in 2020. Consequently, this study highlights the dynamic impact of technological innovation on the digital economy and its crucial role within the context of a green economy. It holds certain reference significance for research on the dynamic effects of the digital economy under technological innovation. [ABSTRACT FROM AUTHOR]