The conventional design method of concrete mix ratio relies on a large number of tests for trial mixing and optimization, and the workload is massive. It is challenging to cope with today's diverse raw materials and the concrete's specific performance to fit modern concrete development. To innovate the design method of concrete mix ratio and effectively use the various complex novel raw materials, the traditional mix ratio test method can be replaced with the intelligent optimization algorithm, and the concrete performance prediction can be realized rapidly and accurately. The mixed ratio of the rubber fiber concrete was designed with its 28-day strength test. Then the range and variance analysis of the orthogonal test results were carried out to determine the optimal mix ratio and its influencing factors. A data set containing 114 sets of valid test data was collected by combining the rubber concrete mix test data published in recent years. Based on this data set, there are six influencing factors; rubber content, rubber particle size, and polypropylene fiber content are considered as the input variables, and the 28-day concrete compression, splitting tensile, and flexural strength are considered as the output variables. A strength prediction model of rubber fiber concrete is established based on the extreme learning machine (ELM). For verifying the ELM prediction model's performance, this article has conducted a comparison experiment between this model and other intelligent algorithm models. The results show that the model has the advantages of high accuracy and high generalization ability compared with other algorithm models such as conventional neural networks. It can be used as an effective method for predicting concrete performance. The method allows for the innovation and development of concrete mixing technology.
The United Nations (UN) has identified 17 Sustainable Development Goals (SDGs) to tackle major barriers to sustainable development by 2030. Achieving these goals will rely on the contribution of all nations and require balancing trade-offs among different sectors. Water and food insecurity have long been the two major challenges facing China. To address these challenges and achieve the SDGs, China needs to safeguard its agricultural irrigation and water conservancy projects. Although China is making efforts to transition its agricultural development to a sustainable trajectory by promoting water-saving irrigation, a number of issues are emerging, both with policy reforms and technological innovations. Through synthesizing the historical development of agriculture and its relationship with policy and political regimes, this paper identifies four major issues that are challenging the sustainability transformation of China’s agricultural irrigation system and water conservancy projects: (1) problems with financial policy coordination between central and local governments; (2) the lack of incentives for farmers to construct and maintain irrigation infrastructure; (3) conflicts between decentralized operation of land and benefits from shared irrigation infrastructure; and (4) deterioration of small-scale irrigation infrastructure calls for action. In addressing these challenges, policy changes are required: government financial accountability at all levels needs to be clarified; subsidies need to be raised for the construction and management of small-scale irrigation and water conservancy projects; local non-profit organizations need to be established to enhance co-management between farmers and government.
This paper describes an approach to the integration of environmental flows recommendations into water resources planning, and demonstrates its application to a case study in the Jiaojiang Basin, Taizhou, Zhejiang Province, the People's Republic of China. In this approach, environmental flows recommendations were provided to the process as a preferred regime, and also as one or more sub-optimal regimes. A risk assessment approach was used to derive the sub-optimal regimes from the preferred regime. The environmental flows rules were then incorporated into a wider water resources model which allowed testing of any number of development scenarios. The model-predicted daily time series' of river flows were passed through a sophisticated form of spells analysis to evaluate the degree of compliance with a specified environmental flows regime. This degree of compliance was balanced against the predicted security of supply to water users. This integrated approach allowed for a greater appreciation of environmental concerns by planners. It also provided an opportunity to the scientists who undertook the environmental flows assessment to contribute to the process of making rational trade-offs between risks to the environment and gains in security of supply.
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