Xiangmiao Wan, Yan Tan^*, Xiong Long

Structural Durability & Health Monitoring, Vol.18, No.4, pp. 505-524, 2024, DOI:10.32604/sdhm.2024.049363

Abstract In this study, we assessed the impact of substituting natural fine aggregates with municipal solid waste incineration bottom ash (MSWI-BA) in steel fiber (SF)-reinforced concrete on its compressive properties post high-temperature exposure. The concrete specimens incorporating MSWI-BA as the fine aggregate and SFs for reinforcement underwent uniaxial compression tests after exposure to high temperatures. Through the tests, we investigated the impact of high-temperature exposure on mechanical properties, such as mass loss rate, stress-strain full curve, compressive strength, peak strain, elastic modulus, and so on, over different thermostatic durations. The analysis revealed that with the increasing… More >

Peng Zhang¹, Dongsheng Shi^1,*, Ping Han^1,2, Wenchao Jiang^1,3

Journal of Renewable Materials, Vol.11, No.2, pp. 669-688, 2023, DOI:10.32604/jrm.2022.022192

Abstract The destruction of concrete building materials in severely cold regions of the north is more severely affected by freeze-thaw cycles, and the relationship between the mechanical properties and pore structure of concrete with fine aggregate from municipal solid waste (MSW) incineration bottom ash after freeze-thaw cycles is analyzed under the degree of freeze-thaw hazard variation. In this paper, the gray correlation method is used to calculate the correlation between the relative dynamic elastic modulus, compressive strength, and microscopic porosity parameters to speculate on the most important factors affecting their changes. The GM (1,1) model was… More >

Gang Yu^1,4,5, Jian Tang^2,*, Jian Zhang³, Zhonghui Wang⁶

Intelligent Automation & Soft Computing, Vol.29, No.1, pp. 273-290, 2021, DOI:10.32604/iasc.2021.016981

Abstract Kernel learning based on structure risk minimum can be employed to build a soft measuring model for analyzing small samples. However, it is difficult to select learning parameters, such as kernel parameter (KP) and regularization parameter (RP). In this paper, a soft measuring method is investigated to select learning parameters, which is based on adaptive multi-layer selective ensemble (AMLSEN) and least-square support vector machine (LSSVM). First, candidate kernels and RPs with K and R numbers are preset based on prior knowledge, and candidate sub-sub-models with K*R numbers are constructed through utilizing LSSVM. Second, the candidate More >