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Interest in exploring the use of seawater as the mixing water for preparing concrete is increasing due to the lack of freshwater in some coastal regions and remote islands, where seawater is more accessible. However, up to now, the mechanism of the accelerating effect of seawater on the hydration of portland cement (PC) remains unclear. In this study, alite, a major clinker phase in PC, was hydrated with common salt solutions (NaCl, Na2SO4, and MgCl2) in seawater to explore the mechanism of acceleration. The heat release peaks of the salt-added systems shifted to an earlier hydration time with a higher peak value, which indicated the faster hydration rate of alite pastes compared to the deionized (DI) water system. The addition of the single salts was found to increase the concentration of Ca species in solutions, contributing to the increased formation of calcium–silicate–hydrates (C–S–H) and portlandite at early ages. In the Na2SO4 system, gypsum was the new hydration product, while brucite was formed in MgCl2 systems, which caused the sharp decrease of Mg species in the solution. The morphology of the early formed C–S–H was changed with the addition of the salts, and the C–S–H were characterized as thinner and longer fibers. At later ages, the incorporation of the single salts lowered the polymerization degree of C–S–H, but no noticeable morphological change was observed.

Source code coupling and change history are two important data sources for change coupling analysis. The popularity of public open source projects in recent years makes both sources available. Based on our previous research, in this paper, we inspect different dimensions of software changes including change significance or source code dependency levels, extract a set of features from the two sources and propose a bayesian network-based approach for change coupling prediction. By combining the features from the co-changed entities and their dependency relation, the approach can model the underlying uncertainty. The empirical case study on two medium-sized open source projects demonstrates the feasibility and effectiveness of our approach compared to previous work. A Bayesian Network Based Approach for Change Coupling Prediction AbstractSource code coupling and change history are two important data sources for change coupling analysis. The popularity of public open source projects in recent years makes both sources available. Based on our previous research, in this paper, we inspect different dimensions of software changes including change significance or source code dependency levels, extract a set of features from the two sources and propose a bayesian network-based approach for change coupling prediction. By combining the features from the co-changed entities and their dependency relation, the approach can model the underlying uncertainty. The empirical case study on two medium-sized open source projects demonstrates the feasibility and effectiveness of our approach compared to previous work.

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