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This study aims to focus on the resistance to elevated temperatures of up to 700ºC of high-performance concrete (HPC) compared to ordinary Portland concrete (OPC) with regards to mass loss and residual compressive and flexural strength.

Two mixtures were developed to test. The first mixture, OPC, was used as the control, and the second mixture was HPC. After 28 days under water (per Chinese standard), the samples were tested for compressive strength and residual strength.

The test results showed that at elevated temperatures of up to 500ºC, each mixture experienced mass loss. Below this temperature, the strength and the mass loss did not differ greatly.

When adding a 10 per cent silica fume, 25 per cent fly, 25 per cent slag to HPC, the compressive strength increased by 17 per cent and enhanced the residual compressive strength. A sharp decrease was observed in the residual flexural strength of HPC when compared to OPC after exposure to temperatures of 700ºC.

During service period, the bridge structures will be affected by the environment and load, so the carrying capacity will decline. The purpose of this paper is to research on the bearing capacity of bridge structures with time.

Destructive test and non-linear finite element analysis are carried out by utilizing two pretensioning prestressed concrete hollow slabs in service for 20 years; using the structural test deflection value to simulate the stiffness degradation of the service bridge and the finite element calculation results verify the accuracy of the calculation.

The flexural rigidity of the main beam when the test beam is destructed is degraded to approximately 20 percent of that before the test, which agrees well with the result of finite element analysis and indicates that the method of deducing the flexural rigidity of the structure according to the measured deflection value can effectively simulate the rigidity degradation law of the bridge in service. The crack resistance property of the test beam degrades obviously and the ultimate bearing capacity of the bending resistance does not degrade obviously.

The research results truly reflect the destruction process, destructive form, bearing capacity and rigidity degradation law of the old beam of the concrete bridge in service for 20 years and can provide technical basis for optimization design of newly built bridges of the same type and maintenance and reinforcement design of existing old bridges.

This study aims to investigate the effectiveness of different additives (individual effects) in improving the strength of concrete to resist temperatures of up to 60ºC.

In all, 13 different mixtures with a constant water/binder ratio of 0.36 and grade M40 were prepared by using ordinary Portland concrete alone, or with partial replacement by fly ash (FA), blast-furnace slag, silica fume (SF) and a combination of all three. After 7 and 28 days under water, their strength and residual strength were measured.

The results of testing revealed that the addition of 10 per cent SF was found to result in the greatest increase in compressive strength and flexural strength along with decreased the residual strengths. The addition of FA increased the compressive strength and enhanced the residual compressive strength. However, it also decreased the residual flexural strength.

The addition of slag achieved better flexural strength and the best residual compressive strength. The combination of additives also enhanced the compressive strength but was not found to be better than using SF alone.

The calculation of the shear capacity of inclined section for prestressed reinforced concrete beams is an important topic in the design of concrete members. The purpose of this paper, based on the truss-arch model, is to analyze the shear mechanism in prestressed reinforced concrete beams and establish the calculation formula for shear capacity.

Considering the effect of the prestressed reinforcement axial force on the angle of the diagonal struts and regression coefficient of softening cocalculation of shear capacity is established. According to the shape of the cracks of prestressed reinforced concrete beams under shear compression failure, the tie-arch model for the calculation of shear capacity is established. Shear-failure-test beam results are collected to verify the established formula for shear bearing capacity.

Through theoretical analysis and experimental beam verification, it is confirmed in this study that the truss-arch model can be used to analyze the shear mechanism of prestressed reinforced concrete members accurately. The calculation formula for the angle of the diagonal struts chosen by considering the effect of prestress is accurate. The relationship between the softening coefficient of concrete and strength of concrete that is established is correct. Considering the effect of the destruction of beam shear plasticity of the concrete on the surface crack shape, the tie-arch model, which is established where the arch axis is parabolic, is applicable.

The formula for shear capacity of prestressed reinforced concrete beams based on this theoretical model can guarantee the effectiveness of the calculation results when the structural properties vary significantly. Engineers can calculate the parameters of prestressed reinforced concrete beams by using the shear capacity calculation formula proposed in this paper.

The purpose of this paper is to develop a bi-objective optimization framework to select prefabricated construction service composition. An improved algorithm-genetic simulated annealing algorithm (GSA) is employed to demonstrate the application of the framework.

The weighted aggregate multi-dimensional collaborative relationship is used to quantitatively evaluate the synergistic effect. The quality of service is measured using the same method. The research proposed a service combination selection framework of prefabricated construction that comprehensively considers the quality of service and synergistic effect. The framework is demonstrated by using a GSA that can accept poor solutions with a certain probability. Furthermore, GSA is compared with the genetic algorithm (GA), simulated annealing algorithm (SA) and particle swarm optimization algorithm (PSO) to validate the performance.

The results indicated that GSA has the largest optimal fitness value and synergistic effect compared with other algorithms, and the convergence time and convergence iteration of the improved algorithm are generally at a low level.

The contribution of this study is that the proposed framework enables project managers to clarify the interactions of the prefabricated construction process and provides guidance for project collaborative management. In addition, GSA helps to improve the probability of successful collaboration between potential partners, therefore enhancing client satisfaction.

Many countries have experienced, or are experiencing, urbanization. One such example is China. Even though the large‐scale rural‐urban migration seems chaotic on the surface, there are certain underlying forces driving individual decisions. The purpose of this paper is to provide some understanding of the relationship between human capital, migration, and occupational choices.

The paper starts with an overlapping generations model. Human capital plays various roles across different occupations – it does not affect the income of farmers, it affects income of workers linearly, and it has increasing returns in rural non‐farm business. The paper then derives income profiles for individuals with heterogeneous human capital, and finds the human capital thresholds of occupations. The paper calibrates the model to China, and simulates the model to answer two questions: how does an improving human capital distribution affect rural wages, quantities of migrants and return migrants? How does a fast‐growing urban wage rate affect rural wages, quantities of migrants and return migrants?

First, depending on the initial human capital level, policies aiming to enhance human capital may have different impacts on migration. If the initial human capital level is low, these policies will yield more permanent migrants; on the contrary, if the initial human capital is at a relatively high level, then a shrinking permanent migrant class with a growing entrepreneur class can be expected. This results in an inverted U‐shaped relation between the initial human capital level and the size of the permanent migrant class. Second, even though the non‐farm business of return migrants helps raise rural wages, the income inequality between rural and urban areas is not eliminated and migration is persistent. Third, borrowing constraints limit the size of rural non‐farm businesses and slow down the development of rural industry. The fourth and final point is that, migration costs discourage labor mobility and reduce the quantities of both permanent migrants and entrepreneurs.

This is an original paper on this subject.