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A Review of Biomass-Derived Biochar and Its Potential in Asphalt Pavement Engineering

Zhou Li

Materials Science Poland

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2024

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Vol. 42, No. 2

81--99

EN

Biomass-derived biochar has gained significant attention due to its unique properties and potential applications in various fields, including asphalt pavement engineering. However, there has been no comprehensive review to date that systematically examines the state-of-theart research on biochar utilization in asphalt pavements, identifies the key knowledge gaps, and provides recommendations for future research directions. This review aims to fill this gap by providing a novel and critical analysis of the sources and production methods of biochar, the techniques for modifying and characterizing its properties, and its recent applications as an asphalt binder modifier, asphalt mixture additive, and stormwater filter material. The review employs a systematic literature search and analysis methodology, using scientific databases such as Web of Science and Scopus, and keywords related to biochar, asphalt, pavement, and environmental and economic aspects. The selected studies are reviewed and synthesized to identify research gaps, challenges, and future directions, with a focus on the technical, environmental, and economic feasibility of biochar utilization in asphalt pavements. The review also examines the life cycle assessment, carbon sequestration potential, and cost-benefit analysis of biochar utilization. The novelty of this review lies in its holistic approach to assessing state-of-the-art knowledge and its identification of key research needs and opportunities for advancing this emerging field. The review aims to provide valuable insights and recommendations for researchers, practitioners, and policymakers interested in leveraging the benefits of biochar for sustainable and high-performance asphalt pavements.

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Energy consumption reduction method for green buildings based on human thermal discomfort posture recognition algorithm

Song Shijiu, Zhou Li

Archives of Civil Engineering

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2024

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Vol. 70, nr 4

563--578

EN

The energy consumption of air conditioning systems accounts for more than 50% of building energy consumption. The supercooled and overheated environment provided by intelligent buildings can bring a large amount of energy loss. How to create comfortable spaces with energy-saving goals is currently the focus of research. The aim of this study is to improve the accuracy of human thermal discomfort pose recognition algorithms. This study first extracts human key points on the ground of bone key points, then normalizes the data, and finally constructs a human thermal uncomfortable posture recognition algorithm on the ground of deep learning technology. The experiment showcases that in the training set, when the iteration number is 1500, the accuracy reaches its maximum value, which is 99.98%. In the test set, the accuracy reached its maximum value of 89.85% when the iteration number was 400. After classifying the dataset, the accuracy of the first type dataset reached 99.51%. The accuracy rate of the second type dataset is 98.56%, and the accuracy rate of the third type dataset is 98.95%. In the comparison of the four algorithms, the accuracy of the research algorithm is significantly higher than the other three algorithms, indicating that the research algorithm can accurately recognize the thermal uncomfortable posture of the human body. This research algorithm can timely and effectively identify the uncomfortable posture of the human body, thereby automatically adjusting indoor temperature and achieving energy conservation and emission reduction.

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A new model for predicting the axial compression capacity of reinforced concrete cylinders strengthened with CFRP

Guo Guang, Zhou Li, Wang Bangkang

Archives of Civil Engineering

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2024

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Vol. 70, nr 1

389--404

EN

Numerous scholars have identified the shortcomings of imprecise terminology and substantial computational inaccuracies in the current models for predicting the axial compression capacity of CFRPstrengthened reinforced concrete (RC) cylinders. To improve the prediction accuracy of the axial compressive capacity model for CFRP-strengthened RC cylinders, the present axial compressive capacity model for CFRP-strengthened RC cylinders was scrutinized and evaluated. Drawing on Mander’s constraint theory and the concrete triaxial strength model, a novel axial compressive capacity model for CFRP-strengthened RC cylinders was proposed. This study collected 116 experimental data on the axial compression of CFRP-strengthened RC cylinders and analyzed the accuracy of various models using the data. The findings indicate that the model proposed in this study outperforms other models in predicting axial compression capacity and demonstrates high prediction accuracy. Furthermore, an analysis is conducted on the variation law of the model’s predicted value with respect to the design parameters. The proposed model in this study identifies concrete strength, stirrup spacing, and elastic modulus of CFRP as the primary factors that influence the axial compression capacity of CFRP-strengthened RC cylinders.