Creep and shrinkage have pronounced effects on the long-term deflection of prestressed concrete members. Under repeated loading, the rate of creep in prestressed concrete members is often accelerated. In this paper, an iterative computational procedure based on the well known Model B3 for creep and shrinkage was developed to predict the time-dependent deflection of partially prestressed concrete members. The developed model was validated using the experimental observed deflection behavior of a simply supported partially prestressed concrete beam under repeated loading. The validated model was then employed to make predictions of the long-term deflection of the prestressed beams under a variety of conditions (e.g., water cement ratio, relatively humidity and time at drying). The simulation results demonstrate that ignoring creep and shrinkage could lead to significant underestimation of the long-term deflection of a prestressed concrete member. The model will prove useful in reducing the long-term deflection of the prestressed concrete members via the optimal selection of a concrete mix and prestressing forces.

Considering the well known environmental issues of cement manufacturing (direct and indirect levels of $CO_2$ emissions), clinker replacement by supplementary cementing materials (SCM) can be a very promising first step in reducing considerably the associated emissions. However, such a reduction is possible up to a particular level of SCM utilization, influenced by the rate of its pozzolanic reaction. In this study a (4-step) structured methodology is proposed in order to be able to further adjust the concrete mix design of a particular SCM, in achieving additional reduction of the associated levels of $CO_2$ emissions and being at the same time accepted from a derived concrete strength and service life point of view. On this note, the aim of this study is twofold. To evaluate the environmental contribution of each concrete component and to provide the best possible mix design configuration, balanced between the principles of sustainability (low environmental cost) and durability (accepted concrete strength and service life ). It is shown that such a balance can be achieved, by utilising SCM by-products in the concrete mix, reducing in this way the fixed environmental emissions without compromising the long-term safety and durability of the structure.

A novel reliability-based work model of k/n (G) system has been developed. Unit failure probability is given based on the load and strength distributions and according to the stress-strength interference theory. Then a dynamic reliability prediction model of repairable k/n (G) system is established using probabilistic differential equations. The resulting differential equations are solved and the value of k can be determined precisely. The number of work unit k in repairable k/n (G) system is obtained precisely. The reliability of whole life cycle of repairable k/n (G) system can be predicted and guaranteed in the design period. Finally, it is illustrated that the proposed model is feasible and gives reasonable prediction.

Ready-mixed soil material, known as a kind of controlled low-strength material, is a new way of soil cement combination. It can be used as backfill materials. In this paper, artificial neural network and nonlinear regression approach were applied to predict the compressive strength of ready-mixed soil material containing Portland cement, slag, sand, and soil in mixture. The data used for analyzing were obtained from our testing program. In the experiment, we carried out a mix design with three proportions of sand to soil (e.g., 6:4, 5:5, and 4:6). In addition, blast furnace slag partially replaced cement to improve workability, whereas the water-to-binder ratio was fixed. Testing was conducted on samples to estimate its engineering properties as per ASTM such as flowability, strength, and pulse velocity. Based on testing data, the empirical pulse velocity-strength correlation was established by regression method. Next, three topologies of neural network were developed to predict the strength, namely ANN-I, ANN-II, and ANN-III. The first two models are back-propagation feed-forward networks, and the other one is radial basis neural network. The results show that the compressive strength of ready-mixed soil material can be well-predicted from neural networks. Among all currently proposed neural network models, the ANN-I gives the best prediction because it is closest to the actual strength. Moreover, considering combination of pulse velocity and other factors, viz. curing time, and material contents in mixture, the proposed neural networks offer better evaluation than interpolated from pulse velocity only.

In this study, analytical and experimental modal analyses of a scaled bridge model are carried out to extract the dynamic characteristics such as natural frequency, mode shapes and damping ratios. For this purpose, a scaled bridge model is constructed in laboratory conditions. Three dimensional finite element model of the bridge is constituted and dynamic characteristics are determined, analytically. To identify the dynamic characteristics experimentally; Experimental Modal Analyses (ambient and forced vibration tests) are conducted to the bridge model. In the ambient vibration tests, natural excitations are provided and the response of the bridge model is measured. Sensitivity accelerometers are placed to collect signals from the measurements. The signals collected from the tests are processed by Operational Modal Analysis; and the dynamic characteristics of the bridge model are estimated using Enhanced Frequency Domain Decomposition and Stochastic Subspace Identification methods. In the forced vibration tests, excitation of the bridge model is induced by an impact hammer and the frequency response functions are obtained. From the finite element analyses, a total of 8 natural frequencies are attained between 28.33 and 313.5 Hz. Considering the first eight mode shapes, these modes can be classified into longitudinal, transverse and vertical modes. It is seen that the dynamic characteristics obtained from the ambient and forced vibration tests are close to each other. It can be stated that the both of Enhanced Frequency Domain Decomposition and Stochastic Subspace Identification methods are very useful to identify the dynamic characteristics of the bridge model. The first eight natural frequencies are obtained from experimental measurements between 25.00-299.5 Hz. In addition, the dynamic characteristics obtained from the finite element analyses have a good correlation with experimental frequencies and mode shapes. The MAC values obtained between 90-100% and 80-100% using experimental results and experimental-analytical results, respectively.

The objective of this study is to determine whether or not the yield line theory, an effective method widely used for slabs made of ordinary concrete, can be used also for the reinforced concrete slabs made of high-strength concrete. Flexural behavior of simply supported slabs in three different sizes were investigated under concentrated load at mid-span. Additionally, behavior of high strength reinforced concrete slabs with 50 mm and 150 mm reinforcement spacings also studied. Failure loads, deflections, experimental and theoretical failure mechanisms were evaluated. The difference between the moments based on yield line theory and experimental moments varied between 1% to 3%. Experimental and analysis results revealed that yield line analysis could conveniently be employed in the analysis of high strength reinforced concrete slabs.

The high strength materials have been more widely used in reinforced concrete structures because of the benefits of the mechanical and durable properties. Generally, it is known that the ductility decreases with an increase in the strength of the materials. In the design of a reinforced concrete beam, both the flexural strength and ductility need to be considered. Especially, when a reinforced concrete structure may be subjected an earthquake, the members need to have a sufficient ductility. So, each design code has specified to provide a consistent level of minimum flexural ductility in seismic design of concrete structures. Therefore, it is necessary to assess accurately the ductility of the beam sections with high strength materials in order to ensure the ductility requirement in design. In this study, the effects of concrete strength, yield strength of reinforcement steel and amount of reinforcement including compression reinforcement on the complete moment-curvature behavior and the curvature ductility factor of doubly reinforcement concrete beam sections have been evaluated and a newly prediction formula for curvature ductility factor of doubly RC beam sections has been developed considering the stress of compression reinforcement at ultimate state. Based on the numerical analysis results, the proposed predictions for the curvature ductility factor are verified by comparisons with other prediction formulas. The proposed formula offers fairly accurate and consistent predictions for curvature ductility factor of doubly reinforced concrete beam sections.