Testing Concrete E‐modulus at Very Early Ages Through Several Techniques: An Inter‐laboratory Comparison

The design of concrete structures is based on calculation rules, which often do not take into account the very early age behaviour of the material. However, during this period, structural concrete is subjected to strains due to the hydration process of cement. If these strains are restrained by concrete itself or surrounding boundaries, stresses start to build up that can lead to the formation of cracks. Among the parameters involved in the stress build up, the stiffness evolution is of major importance. This paper reports the use of eight different techniques aimed at stiffness evolution assessment, applied on the same concrete mix, in a round robin experimental test within three laboratories. The observations are compared after having expressed the results at the same equivalent age. Both the loading stress rate and amplitude are observed to have an effect of limited importance on the determination of the quasi‐static elastic modulus, which might be explained by very short term creep. Ultrasonic measurements provide values of E‐modulus that are higher than the values provided by the quasi‐static tests at the time of the concrete setting. Similar mechanisms associated to very short term creep could explain the difference between the quasi‐static and high‐frequency elastic modulus.     

Mechanical properties of concrete containing phase-change material

This study aimed to investigate the mechanical properties of concrete containing solid–liquid phase-change material (PCM) and focused on two key factors. First, a systematic study on the mechanical performance of PCM-modified concretes was conducted, including compressive, elastic modulus, and shrinkage tests. Second, because PCM provides high latent heat during the solid–liquid phase change, the effects of the solid phase and liquid phase on the mechanical properties of concrete were also explored. Results of this study showed that the solid–liquid phase of PCM affected the mechanical properties of concrete. For example, the compressive strength of 10% PCM concrete in solid phase (23 °C) and liquid phase (40 °C) at 28 days was 29.30 and 19.57 MPa, respectively. In addition, with increasing PCM content, the mechanical properties were degraded. For example, 10, 20, and 30% of PCM content lowered the compressive strength by 35.4, 58.4, and 74.3%, respectively. Therefore, concrete with PCM may not be suitable for structural elements. However, PCM is an important solution for optimizing energy consumption in modern buildings. It can absorb or emit large amounts of heat to store or release thermal energy. These properties can be used to control building temperatures resulting in energy saving and carbon reduction.     

4J29可伐合金材料低温热物性及弹性模量测试

为了研究4J29可伐合金的低温热物性和机械性能,为低温系统的设计、结构变形、力学稳定性、装调和光学校准等提供技术保障,分别选用"稳态纵向热流法"、"稳态法"测试了4J29可伐合金在77—300 K温区的热导率和比热容,采用弹性模量试验机测试了77—300 K温区的弹性模量系数。通过标准样品材料TC4对比及误差分析,证明热导率测量误差在9%以内,其它误差均控制在6%以内。     

基于LM算法的粘弹性材料剪切松弛模量的拟合

在对粘弹性材料进行有限元计算时,需要材料的剪切松弛模量,必须对经验公式或实验数据进行转换。根据KWW经验公式与广义Maxwell剪切松弛函数的对应关系,列出了求解松弛时间与剪切松弛模量的超定非线性方程组,用LM优化算法得到了方程组的最优解。初步讨论了初值的选取方法。根据此方法得到的松弛时间与剪切松弛模量的拟合曲线的拟合度达到了0.99以上。所提出的方法也适用于用实验数据(随时间变化的应力应变)求解材料的剪切松弛模量。     

微孔结构对PMI泡沫准静态压缩性能的影响

基于BBC点集建立了聚甲基丙烯酰亚胺(PMI)闭孔泡沫的Kelvin十四面体模型和Laguerre模型,并采用有限元方法研究了其在准静态载荷作用下的压缩性能。分析了孔径大小、泡孔体积离散系数对压缩弹性模量、初始峰值应力和能量吸收能力的影响。结果表明:Kelvin十四面体模型可以较好地预测PMI泡沫的压缩弹性模量和峰值应力;在相同相对密度条件下,小孔径泡沫的初始峰值应力和能量吸收能力均高于大孔径泡沫,而压缩弹性模量则低于大孔径泡沫;随着泡孔体积离散系数的增大,闭孔PMI泡沫压缩弹性模量、初始峰值应力和能量吸收能力均减小。     

Evaluation of rutting potential for asphalt concrete mixes containing copper slag

Copper slag (CS) is a by-product of the copper extraction process, which can be used as coarse and/or fine aggregate in hot mix asphalt (HMA) pavements. This study used CS as a replacement of the fine aggregate with a percentage of up to 40% by total aggregate weight. The objective of this study was to evaluate the effect of CS on the rutting potential of the asphalt concrete mix using two methods. One method is based on the Dynamic modulus |E*| testing result. Actual pavement temperature data from a test section were used with the developed |E*| master curves. EverStressFE finite element program was used to perform a linear elastic load-deformation analysis for a pavement section and to determine the vertical resilient strain in a 40-mm HMA surface layer. The M-E PDG permanent deformation model was used with and Excel Visual Basic for Applications code to predict the accumulated rutting for different CS mixes for 10 million ESALs. The other method used the data from the flow number (FN) test. Based on the |E*| approach, the results indicated that adding 5% CS in the mix increased the predicted rutting from 0.59 to 0.98 mm at 10 million ESALs (increase by 68%). When 40% CS was used, rutting increased by more than 700% compared with the control mix. After analysing the FN results with the Francken model, the results indicated a decrease in FN as CS content is increased, indicating higher rutting potential. The decrease in FN ranged from 9% for 5% CS to 95% for 40% CS. The mixes containing up to 10% CS satisfied the minimum FN criteria for rutting. A calibration process for the M-E PDG distress prediction models that allows the use of waste and by-product materials such as CS should be considered in the future.     

Learn to survive crises: The role of firm resilience,innovation capabilities and environmental dynamism

Exogenous crises, while disruptive, may also present learning opportunities that could affect a firm's viability and performance. In this study, we examine how exogenous crises can constitute learning opportunities and assess their impact on firm survival. In particular, we investigate the role of learning in response to exogenous crises and how firm resilience, innovation capabilities, and environmental dynamism influence this relationship. Drawing from crisis management and organisational learning literature, we propose that these factors can bolster the connection between learning from crises and firm survival. To test our hypotheses, we conduct a nuanced analysis using both regression analysis and Fuzzy Set Qualitative Comparative Analysis (fsQCA) on data from 249 Italian manufacturing Small and Medium-sized Enterprises (SMEs). This approach allows us to simultaneously examine the impact of firm resilience, innovation capabilities, and environmental dynamism on the relationship between learning from crises and firm survival. Our findings offer theoretical and practical insights into the role of learning from crises in a firm's survival. They also highlight the importance of embracing learning opportunities in crisis situations and suggest that how firms deal with crises could be an opportunity to fine-tune their internal processes and thrive in the long run.     

交通网络弹复度与易碎度的测算与分析

弹复性是系统在失效或被破坏之后能够恢复正常工作的能力.弹复性工程是当今国际上研究的热点.本文提出了基于节点对间的独立路径可靠度和交通网络的弹复度的定量测算方法,提出了反映节点和路段对交通网络影响大小的易碎性的概念和测算方法.论文还就弹复度和易碎度的几个相关性质提出了几个重要定理.针对我国的铁路网络,计算了所有节点城市和路段的弹复度和易碎度,并对弹复度和易碎度高的节点的分布规律进行了探讨,得出了一些有益的结论.     

Models to predict the deformation modulus and the coefficient of subgrade reaction for earth filling structures

Determination of deformation modulus and coefficient of subgrade reaction of soils have major importance, whether the projects are in design, and construction or compaction assessment stage of earth filling structures. Plate load test is one of the frequently used method to directly determine the parameters but the method is both costly and time consuming. For this reason, this paper is concerned with the applications of artificial neural networks (ANN) and simple-multiple regression analysis to predict deformation modulus and coefficient of subgrade reaction of compacted soils from compaction parameters (such as maximum dry density (MDD) and optimum moisture content (OMC), field dry density (FDD), and field moisture content (FMC)). Regression analysis and artificial neural network estimation indicated that there are acceptable correlations between deformation modulus and coefficient of subgrade reaction and these parameters. Artificial neural networks model exhibits higher performance than traditional statistical model for predicting deformation modulus and coefficient of subgrade reaction.