通讯机构：
[Yang, YM ] H;Hunan City Univ, Coll Civil Engn, 158 Yinbindong Rd, Yiyang 413049, Peoples R China.
关键词：
Prestressed concrete structures;Bonding behavior;Corrosion of steel strands;Pull -out experiment;Empirical formula
摘要：
Degradation of bonding behavior due to chloride-induced corrosion of steel strands is critical for serviceability of prestressed concrete structures. In this paper, total thirty-one central and eccentric pull-out specimens were tested to study the effects of strand corrosion, combined with stirrups or position of steel strand on global force-slip response, bond strength and failure pattern. Experimental results show that the bond strength of the eccentric pull-out specimen without corrosion or slight corrosion is greater than that of the centrally loading ones, and the opposite is true once the corrosion rate reaches 6%. Stirrups in bonding specimens can effectively restrain the transverse deformation of concrete, and significantly improve the bond strength between corroded strand and concrete. As the corrosion rate of steel strand increases, the bond-slip curves of specimens with stirrups tend to be similar to those without stirrups. Compared with the corroded deformed bar, the degradation of bond behavior caused by deformed bar corrosion is more serious than that of steel strand corrosion. By considering the combined effects of steel strand corrosion, stirrups and position of steel strand, an empirical model is proposed to predict the bond strength between corroded steel strand and concrete with reasonable accuracy.
作者机构：
[Jiang, Changbo; Ma, Yuan; Jiang, CB; Li, Shanshan; Li, Chuannan] Changsha Univ Sci & Technol, Sch Hydraul Engn, Changsha 410114, Peoples R China.;[Li, Shanshan] Hunan City Univ, Coll Civil Engn, Yiyang 413002, Peoples R China.;[Jiang, Changbo; Ma, Yuan; Jiang, CB; Li, Chuannan] Key Lab Dongting Lake Aquat Ecoenvironm Control &, Changsha 410114, Peoples R China.
通讯机构：
[Jiang, CB ] C;Changsha Univ Sci & Technol, Sch Hydraul Engn, Changsha 410114, Peoples R China.;Key Lab Dongting Lake Aquat Ecoenvironm Control &, Changsha 410114, Peoples R China.
关键词：
spatiotemporal trend;hydroclimatic variables;the Dongting Lake basin
摘要：
The Dongting Lake basin, located in the middle Yangtze River region, has long been under the threat of climate change. However, there has been a lack of comprehensive analysis and research on the long-term trends and interactions among hydrometeorological factors within the region. To address this gap, this study collected data from 31 meteorological stations in the region and employed statistical analysis methods, including the non-parametric Mann-Kendall test, Sen's slope test, and cross-wavelet analysis. The results revealed significant increases in temperatures, especially in the spring season, while summer, winter, and annual rainfall also exhibited a significant increase. However, spring and autumn rainfall showed a non-significant decrease, and there was a clear decreasing trend in annual streamflow. Interestingly, evaporation demonstrated a significant increasing trend. The annual average temperature and annual runoff exhibited approximately negative correlations in the 6-10-year resonance period and positive correlations in the 4-6-year resonance period. There are significant positive resonance periods in the relationship between annual precipitation and annual runoff within the range of 0-12 years, indicating that precipitation has a substantial impact and serves as the primary source of runoff. Furthermore, there was a transition between "abundance" and "dry" periods in the annual runoff around 4 a, occurring before and after 1973 and 2005. The change points in annual precipitation and runoff were identified as 1993 and 1983.
作者机构：
[Zhou, Shuming; Yan, Donghuang] Changsha Univ Sci & Technol, Sch Civil Engn, Changsha 410114, Peoples R China.;[Zhou, Shuming] Hunan City Univ, Sch Civil Engn, Yiyang, Peoples R China.
通讯机构：
[Shuming Zhou] S;School of Civil Engineering, Hunan City University, Yiyang 413000, China<&wdkj&>School of Civil Engineering, Changsha University of Science & Technology, Changsha 410114, China<&wdkj&>Author to whom correspondence should be addressed.
关键词：
bridge engineering;reinforced-concrete beam;cracking;bearing capacity;finite element method
摘要：
Cracking is one of the main diseases of small- and medium-span reinforced concrete (RC) bridges. It is a key problem to determine the change in mechanical properties of RC beams after cracking in bridge-performance evaluation. The present study performs static loading tests on seven simply supported T-beams with different crack damage conditions. The influences of crack location, crack depth and steel-bar diameter at a prefabricated crack on the stress, deflection and crack distribution pattern of pre-cracked test beams are investigated. The failure mode and mechanism of pre-cracked beams are revealed. Based on the experimental results, a finite element model of a pre-cracked beam is developed and validated. Following this, a theoretical prediction method is proposed to calculate the ultimate load of pre-cracked RC beams. The results indicate that the direct damage to mid-span section size can significantly affect the stiffness of the RC beam. The local damage of the tensile steel section has insignificant influence on the overall stiffness of the beam. The stiffness degradation of the pre-cracked beam at the quarter span is smaller than that of the pre-cracked beam at mid-span. The strain of the T-beam section in the pre-cracked test conformed to the assumption of the flat section. The experimental observations are in good agreement with the theoretical predictions, which can provide a theoretical basis for the performance evaluation of existing RC beams.
期刊：
Structure and Infrastructure Engineering,2023年19(9):1283-1298 ISSN：1573-2479
通讯作者：
Dongliang Meng
作者机构：
[Yang, Menggang; Meng, Dongliang] School of Civil Engineering, Central South University, Changsha, P.R. China;School of Civil Engineering, Hunan City University, Yiyang, P.R. China;[Gao, Qiong] School of Civil Engineering, Central South University, Changsha, P.R. China<&wdkj&>School of Civil Engineering, Hunan City University, Yiyang, P.R. China
通讯机构：
[Dongliang Meng] S;School of Civil Engineering, Central South University, Changsha, P.R. China
摘要：
This article proposes a seismic optimization strategy to reduce the cost of piers of simply-supported bridges for high-speed railway (HSR) in China. The dimension and reinforcement ratio of piers are defined as variables. The running safety of high-speed trains during normal operation is considered by novelly setting the vibration amplitude of running trains to satisfy the corresponding constraint condition. Other constraint conditions include the structural fundamental frequency, buckling stability, and longitudinal stiffness, as well as the stress and crack width of piers during earthquakes. An improved genetic algorithm with a double-filter strategy is proposed to increase the efficiency of the optimization calculation. Six HSR simply-supported bridges with different pier heights are selected as examples to verify the effectiveness and applicability of the proposed optimization method. The result shows that for the example bridges, the cost of the optimised piers that are not higher than 30 m can be reduced by 33.38% to 50.65%. The optimised piers can ensure the integrity of the HSR bridges subjected to the considered earthquakes and can provide sufficient stiffness to ensure the running safety and stationarity of high-speed trains.
期刊：
TRAITEMENT DU SIGNAL,2023年40(3):1195-1202 ISSN：0765-0019
通讯作者：
Ding, XY
作者机构：
[Ding, Xingyu] Hunan City Univ, Sch Civil Engn, Yiyang 413000, Peoples R China.;[Hu, Wenjun] Yunnan Geol Environm Monitoring Inst, Kunming 650216, Peoples R China.
通讯机构：
[Ding, XY ] H;Hunan City Univ, Sch Civil Engn, Yiyang 413000, Peoples R China.
关键词：
machine vision deep learning geological;geological disaster monitoring geological disaster;geological disaster early warning;deep learning
摘要：
Geological disasters, characterized by their destructive nature, pose significant threats to both human life and ecological environments. The advent of remote sensing technology has rendered hyperspectral remote sensing images an integral data source in monitoring and predicting these phenomena. However, it is noted that minor variations and detailed nuances within the images are often overlooked by traditional computer vision and deep learning techniques. Furthermore, data imbalances during the training of deep learning models have been identified as a potential hindrance to optimal performance. Recognizing these issues and the inherent unpredictability of geological disasters, an innovative approach has been developed. This approach encapsulates an optical flow-based method for enhancing the edges of geological remote sensing images, an improved geological disaster monitoring model leveraging the Isolation Forest algorithm, and an efficient implementation strategy. The suggested methods present numerous advantages, including the acceleration of computations to augment real-time monitoring of geological disasters, an enhanced capacity for handling extensive data, an improved system stability and fault tolerance, and the preservation of fundamental strengths such as linear computational complexity, unsupervised learning, and non-parametric methodologies. By synthesizing these methodological improvements and advantages, a swift, efficient, and flexible strategy for enhancing the Isolation Forest model is put forth. This research supports the development of geological disaster monitoring and early warning systems grounded in computer vision and deep learning, presenting substantial technical aid for related tasks.
摘要：
In the realm of geological and mineral exploration, remote sensing technology has emerged as a pivotal high-tech instrument. However, the effective interpretation of remote sensing images, especially in the context of heterogeneous data processing, noise, and the identification of fine granularity, remains a challenge. In this study, a novel method for the identification of mineral elements within remote sensing imagery was introduced. Firstly, a heterogeneous feature tensor migration technique anchored on the Coupled Heterogeneous Tucker Decomposition (CH-Tucker decomposition) was presented. Through this technique, multi-source remote sensing data were effectively processed and fused. Notably, associated data features from varying resolutions and angles were seamlessly coupled. Subsequently, an optical remote sensing image processing model founded on the RFDNet network was established. This model demonstrated robustness against noise data, thereby enabling the identification of mineral elements with a higher degree of granularity. The proposed methodology exhibited the capacity to extract mineral element information comprehensively and with remarkable accuracy. Thus, this research offers both valuable theoretical insights and practical evidence for furtherance in geological research and mineral element exploration.
