The mechanical performance of engineered cementitious composite (ECC) depends greatly on fiber orientation and distribution. In this paper, the effect of fiber orientation on ECC's mechanical properties was investigated using two different casting methods: a flow-induced casting was used to enhance the fiber orientation within ECC mixture and compared with the conventional casting. The fiber orientation was quantified using scanning electron microscope (SEM) and image processing. Mechanical tests on the specimens with various fiber orientations were performed. The failure processes of ECC specimens under compression and tensile tests were analyzed using digital image correlation (DIC) technique. The proposed flow-induced casting enhanced the fiber alignment in the flow direction. Consequently, ECC's mechanical properties were significantly improved with more finer cracks under uniaxial loading. In conclusion, the proposed flow-induced casting can be adopted as an effective approach to improve fiber bridging efficiency in ECC.@en

The performance of engineered cementitious composites (ECCs) under coupled salt freezing and loaded conditions is important for its application on the transportation infrastructure. However, in most of the studies, the specimens were generally loaded prior to the freezing. The influence of sustained load was merely considered. To this end, four sustained deflection levels, i.e., 0%, 10%, 30% and 50% of the deflection at the ultimate flexural strength, and three salt concentrations (1%, 3% and 5%) were applied. Prior to the salt frost resistance test, the fluid absorption of ECC specimens under various conditions were measured. The changes in relative dynamic elastic modulus (RDEM) during the freeze–thaw cycles were captured. The depth and the content profile of free chloride were measured after the coupled sustained load and freezing and thawing cycles. It is shown that 3% NaCl solution leads to the largest deterioration in all cases. There is no visible flaking or damage occurring on the surface. The relationships between locally sustained flexural stress and RDEM loss and also locally sustained flexural stress and free chloride penetration depth were proposed and showed satisfactory results. It is concluded that when ECC is subjected to the FTCs under 1% de-ice salt solution, no depassivation of the steel is expected even under a large deflection level. In terms of 3% and 5% salt solution, the thickness of cover should be no less than 20 mm when a deflection level of 0.5 is applied.

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Engineered cementitious composites (ECCs) belong to a broad class of fibre-reinforced concrete. They incorporate synthetic polyvinyl alcohol (PVA) fibres, cement, fly ash and fine aggregates, and are designed to have a tensile strain capacity typically beyond 3%. This paper presents an investigation on the carbonation behaviour of engineered cementitious composites (ECCs) under coupled sustained flexural load and accelerated carbonation. The carbonation depth under a sustained stress level of 0, 0.075, 0.15, 0.3 and 0.6 relative to flexural strength was measured after 7, 14 and 28 days of accelerated carbonation. Thermogravimetric analysis, mercury intrusion porosimetry and microhardness measurements were carried out to show the coupled influence of sustained flexural load and accelerated carbonation on the changes of the mineral phases, porosity, pore size distribution and microhardness along the carbonation profile. A modified carbonation depth model that can be used to consider the coupled effect of flexural tensile stress and carbonation time was proposed. The results show that an exponential relationship can be observed between stress influence coefficient and flexural tensile stress level in the carbonation depth model of ECC, which is different when using plain concrete. Areas with a higher carbonation degree have greater microhardness, even under a large sustained load level, as the carbonation process refines the pore structure and the fibre bridges the crack effectively.

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The rapid development of the economy has promoted the growth of freight transportation. The truck service areas on expressways, as the main places for truck drivers to rest, play an important role in ensuring the driving safety of trucks. If these service areas are constructed densely or provide a plentiful supply of parking areas, they are costly to construct. However, if the distance between two adjacent truck service areas is very large or the number of truck parking spaces in service areas is small, the supply will fail to meet the parking needs of truck drivers. In this situation, the continuous working time of truck drivers will be longer, and this is likely to cause driver fatigue and even traffic accidents. To address these issues, this paper established a non-linear optimization model for truck service area planning of expressways to optimize truck driving safety. An improved genetic algorithm is proposed to solve the model. A case study of a 215.5-kilometers-length section of the Guang-Kun expressway in China was used to demonstrate the effectiveness of the model and algorithm. As validated by this specific case, the proposed model and solution algorithm can provide an optimal plan for the layout of truck service areas that meet the parking needs of truck drivers while minimizing the service loss rate. The research results of this paper can contribute to the construction of truck service areas and the parking management of trucks on expressways.@en

Dynamic Security Assessment (DSA) for the future power system is expected to be increasingly complicated with the higher level penetration of renewable energy sources (RES) and the widespread deployment of power electronic devices, which drive new dynamic phenomena. As a result, the increasing complexity and the severe computational bottleneck in real-time operation encourage researchers to exploit machine learning to extract offline security rules for the online assessment. However, traditional machine learning methods lack in providing information on the confidence of their corresponding predictions. A better understanding of confidence of the prediction is of key importance for Transmission System Operators (TSOs) to use and rely on these machine learning methods. Specifically, from the perspective of topological changes, it is often unclear whether the machine learning model can still be used. Hence, being aware of the confidence of the prediction supports the transition to using machine learning in real-time operation. In this paper, we propose a novel Conditional Bayesian Deep Auto-Encoder (CBDAC) based security assessment framework to compute a confidence metric of the prediction. This informs not only the operator to judge whether the prediction can be trusted, but it also allows for judging whether the model needs updating. A case study based on IEEE 68-bus system demonstrates that CBDAC outperforms the state-of-the-art machine learning-based DSA methods and the models that need updating under different topologies can be effectively identified. Furthermore, the case study verifies that effective updating of the models is possible even with very limited data.

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