摘要：
This study investigates the development of Alkali-activated Ultra-high performance concrete (AAUHPC) incorporating steel slag powder (SS), ground granulated blast furnace slag (GGBFS), silica fume, and hooked-end steel fibers to achieve sustainable, high-performance construction materials. The effects of SS content (0–70 %) and steel fiber volume fraction (0–2.0 %) on workability, mechanical properties, and microstructure were systematically evaluated through a combination of macroscopic performance tests (setting time, fluidity, compressive/flexural strength, and splitting tensile strength) and microstructural analyses (hydration heat, mercury intrusion porosimetry, X-ray computed tomography, scanning electron microscopy, and micro-hardness). Results reveal that AAUHPC containing 30 % SS and 2.0 % steel fibers achieves a fluidity of 190 mm and a compressive strength of 155.4 MPa at 28 days. Steel slag powder enhances the workability of fresh AAUHPC by extending setting time and improving fluidity, which can be attributed to its lower reactivity and reduced water demand compared to GGBFS. However, excessive SS content (>30 %) may dilute reactive phases and hinder hydration kinetics, leading to decreased mechanical strengths. Although hooked-end steel fibers slightly reduce fluidity, they significantly enhance toughness; specially, a 2.0 % volume fraction yields an 89.8 % increase in flexural strength (18.34 MPa) and a splitting tensile strength of 20.12 MPa, thereby enabling strain-hardening behavior comparable to cement-based UHPC. Microstructural analysis confirms a robust interfacial bonding between fibers and the alkali-activated matrix, facilitated by C-S-H and C-(N,K)-A-S-H gels. This phenomenon has significantly contributed to the high strength and toughness exhibited by AAUHPC. The findings underscore the potential of steel slag powder as a sustainable precursor for applications involving AAUHPC, while also demonstrating the compatibility of alkali-activated materials with hooked-end steel fibers.
This study investigates the development of Alkali-activated Ultra-high performance concrete (AAUHPC) incorporating steel slag powder (SS), ground granulated blast furnace slag (GGBFS), silica fume, and hooked-end steel fibers to achieve sustainable, high-performance construction materials. The effects of SS content (0–70 %) and steel fiber volume fraction (0–2.0 %) on workability, mechanical properties, and microstructure were systematically evaluated through a combination of macroscopic performance tests (setting time, fluidity, compressive/flexural strength, and splitting tensile strength) and microstructural analyses (hydration heat, mercury intrusion porosimetry, X-ray computed tomography, scanning electron microscopy, and micro-hardness). Results reveal that AAUHPC containing 30 % SS and 2.0 % steel fibers achieves a fluidity of 190 mm and a compressive strength of 155.4 MPa at 28 days. Steel slag powder enhances the workability of fresh AAUHPC by extending setting time and improving fluidity, which can be attributed to its lower reactivity and reduced water demand compared to GGBFS. However, excessive SS content (>30 %) may dilute reactive phases and hinder hydration kinetics, leading to decreased mechanical strengths. Although hooked-end steel fibers slightly reduce fluidity, they significantly enhance toughness; specially, a 2.0 % volume fraction yields an 89.8 % increase in flexural strength (18.34 MPa) and a splitting tensile strength of 20.12 MPa, thereby enabling strain-hardening behavior comparable to cement-based UHPC. Microstructural analysis confirms a robust interfacial bonding between fibers and the alkali-activated matrix, facilitated by C-S-H and C-(N,K)-A-S-H gels. This phenomenon has significantly contributed to the high strength and toughness exhibited by AAUHPC. The findings underscore the potential of steel slag powder as a sustainable precursor for applications involving AAUHPC, while also demonstrating the compatibility of alkali-activated materials with hooked-end steel fibers.
摘要：
天然水硬性石灰（NHL）兼具气硬性和水硬性成分，被广泛应用于历史建筑砌体墙墙面的修复。工业固废含有一定的Ca、Si元素，满足制备NHL的矿物元素要求。本研究以石灰石、矿渣、粉煤灰和硅灰为原材料，控制不同的钙硅比参数煅烧制备NHL。利用XRD、TG、抗压抗折试验、SEM-EDS、维卡仪等测试了NHL的成分、产物的组成、微观形貌、力学性能、凝结时间等，并开展工程应用。结果表明：工业固废煅烧制备NHL具有可行性，所制得NHL主要由氢氧化钙和硅酸二钙组成，28 d水化碳化产物包括水化硅酸钙、CaCO3和Ca（OH）2。钙硅比为4.6时可以制备出符合欧洲标准的NHL2，对砖石风化表面的修复结果满足可逆性与兼容性要求。本文响应了国家对历史建筑遗迹保护和生态环境保护的号召，有助于创造更好的经济效益与环境效益。 您的浏览器不支持 audio 元素。AI语音播报 Natural hydraulic lime (NHL) has both pneumatic and hydraulic components, so NHL can be widely used in the restoration of historic building masonry walls. Industrial solid waste contains certain elements of Ca and Si, meeting the mineral element requirements for the preparation of NHL. In this study, limestone, slag, fly ash and silica fume were used as raw materials to prepare NHL by calcination with different calcium-silicon ratio parameters. The composition, product composition, microstructure, mechanical properties, and setting time of NHL were tested using XRD, TG, compressive and flexural tests, SEM-EDS, and Vicat apparatus. Meanwhile carry out engineering applications. The study shows that it is feasible to prepare NHL by industrial solid waste calcination. The prepared NHL is mainly composed of calcium hydroxide and dicalcium silicate. The 28 d hydration carbonization products include hydrated calcium silicate, CaCO3, and Ca(OH)2. NHL can be prepared by calcium-silicon ratio at 4.6 that meets European standards. The repair results of the weathered surface of masonry meet the requirements of reversibility and compatibility. The article responds to the call of national policies for the protection of historical architectural relics and ecological environment, which is conducive to protecting historical culture and creating better economic benefit and environmental benefit.
摘要：
Bolted connections have become the most widely used connection method in steel structures. Over the long-term service of the bolts, loosening damage and other defects will inevitably occur due to various factors. To ensure the stability of bolted connections, an efficient and precise method for identifying loosened bolts in a given structure is proposed based on computer vision technology. The main idea of this method is to combine deep learning with image processing techniques to recognize and label the loosening angle from bolt connection images. A rectangular steel plate was taken as the test research object, and three grade 4.8 ordinary bolts were selected for study. The analysis was conducted under two conditions: manual loosening and simulated loosening. The results showed that the method proposed in this article could accurately locate the position of the bolts and identify the loosening angle, with an error value of about ±0.1°, which proves the accuracy and feasibility of this method, meeting the needs of structural health monitoring.
摘要：
Obtaining accurate and real-time spatial distribution information regarding crops is critical for enabling effective smart agricultural management. In this study, innovative decision fusion strategies, including Enhanced Overall Accuracy Index (E-OAI) voting and the Overall Accuracy Index-based Majority Voting (OAI-MV), were introduced to optimize the use of diverse remote sensing data and various classifiers, thereby improving the accuracy of crop/vegetation identification. These strategies were utilized to integrate crop/vegetation classification outcomes from distinct feature sets (including Gaofen-6 reflectance, Sentinel-2 time series of vegetation indices, Sentinel-2 time series of biophysical variables, Sentinel-1 time series of backscatter coefficients, and their combinations) using distinct classifiers (Random Forests (RFs), Support Vector Machines (SVMs), Maximum Likelihood (ML), and U-Net), taking two grain-producing areas (Site #1 and Site #2) in Haixi Prefecture, Qinghai Province, China, as the research area. The results indicate that employing U-Net on feature-combined sets yielded the highest overall accuracy (OA) of 81.23% and 91.49% for Site #1 and Site #2, respectively, in the single classifier experiments. The E-OAI strategy, compared to the original OAI strategy, boosted the OA by 0.17% to 6.28%. Furthermore, the OAI-MV strategy achieved the highest OA of 86.02% and 95.67% for the respective study sites. This study highlights the distinct strengths of various remote sensing features and classifiers in discerning different crop and vegetation types. Additionally, the proposed OAI-MV and E-OAI strategies effectively harness the benefits of diverse classifiers and multisource remote sensing features, significantly enhancing the accuracy of crop/vegetation classification.
