This work presents an approach to model concrete shrinkage. The goal is to permit the concrete industry's experts to develop independent prediction models based on a reduced number of experimental data. The proposed approach combines fuzzy logic and genetic algorithm to optimize the fuzzy decision-making, thereby reducing data collection time. Such an approach was implemented for an experimental data set related to self-compacting concrete. The obtained prediction model was compared against published experimental data (not used in model development) and well-known shrinkage prediction models. The predicted results were verified by statistical analysis, which confirmed the reliability of the developed model. Although the range of application of the developed model is limited, the genetic-fuzzy approach introduced in this work proved suitable for adjusting the prediction model once additional training data are provided. This can be highly inviting for the concrete industry's experts, since they would be able to fine-tune their models depending on the boundary conditions of their production processes.

Various types of reinforcement splicing methods have been developed and implemented in reinforced concrete construction projects for achieving the continuity of reinforcements. Due to the complicated reinforcement arrangements and the difficulties in securing bar spacing, the traditional lap splicing method, which has been widely used in reinforced concrete constructions, often shows low constructability and difficulties in quality control. Also, lap spliced regions are likely to be over-reinforced, which may not be desirable in seismic design. On the other hand, mechanical splicing methods can offer simple and clear arrangements of reinforcement. In order to utilize the couplers for the ribbed-deformed bars, however, additional screw processing at the ends of reinforcing bars is typically required, which often lead to performance degradations of reinforced concrete members due to the lack of workmanship in screw processing or in adjusting the length of reinforcing bars. On the contrary, the use of screw-ribbed reinforcements can easily solve these issues on the mechanical splicing methods, because it does not require the screw process on the bar. In this study, the mechanical coupler suitable for the screw-ribbed reinforcements has been developed, in which any gap between the reinforcements and sleeve device can be removed by grouting high-flow inorganic mortar. This study presents the uniaxial tension tests on the screw-ribbed reinforcement with the mechanical sleeve devices and the cyclic loading tests on RC columns with the developed coupler. The test results show that the mechanical sleeve connection developed in this study has an excellent splicing performance, and that it is applicable to reinforced concrete columns with a proper confinement by hoop reinforcement.

Prediction of concrete properties is an important issue for structural engineers and different methods are developed for this purpose. Most of these methods are based on experimental data and use measured data for parameter estimation. Three typical methods of output estimation are Categorized Linear Regression (CLR), Multiple Linear Regression (MLR) and Artificial Neural Networks (ANN). In this paper a statistical cleansing method based on CLR is introduced. Afterwards, MLR and ANN approaches are also employed to predict the compressive strength of structural lightweight aggregate concrete. The valid input domain is briefly discussed. Finally the results of three prediction methods are compared to determine the most efficient method. The results indicate that despite higher accuracy of ANN, there are some limitations for the method. These limitations include high sensitivity of method to its valid input domain and selection criteria for determining the most efficient network.

The aim of this paper is to determine the effect of soil-structure interaction and time dependent material properties on behavior of concrete box-girder highway bridges. Two different finite element analyses, one stage and construction stage, have been carried out on Komurhan Bridge between Elazi$\breve{g}$ and Malatya province of Turkey, over Fırat River. The one stage analysis assume that structure was built in a second and material properties of structure not change under different loads and site conditions during time. However, construction stage analysis considers that construction time and time dependent material properties. The main and side spans of bridge are 135 m and 76 m, respectively. The bridge had been constructed in 3 years between 1983 and 1986 by balanced cantilever construction method. The parameters of soil-structure interaction (SSI), time dependent material properties and construction method are taken into consideration in the construction stage analysis while SSI is single parameter taking into consideration in the one stage analysis. The 3D finite element model of bridge is created the commercial program of SAP2000. Time dependent material properties are elasticity modulus, creep and shrinkage for concrete and relaxation for steel. Soft, medium, and firm soils are selected for evaluating SSI in both analyses. The results of two different finite element analyses are compared with each other. It is seen that both construction stage and SSI have a remarkable effect on the structural behavior of the bridge.

This paper presents a study on the prediction of transfer length of 13 mm seven-wire prestressing steel strand in pretensioned prestressed concrete members with rectangular cross-section including several material properties and design and manufacture parameters. To this end, a carefully selected database consisting of 207 different cases coming from 18 different sources spanning a variety of practical transfer length prediction situations was compiled. 16 single input features and 5 combined input features are analyzed. A widely used feedforward neural regression model was considered as a representative of several machine learning methods that have already been used in the engineering field. Classical multiple linear regression was also considered in order to comparatively assess performance and robustness in this context. The results show that the implemented model has good prediction and generalization capacity when it is used on large input data sets of practical interest from the engineering point of view. In particular, a neural model is proposed -using only 4 hidden units and 10 input variables-which significantly reduces in 30% and 60% the errors in transfer length prediction when using standard linear regression or fixed formulas, respectively.

The structure analyzed in this paper has particular building style and special structural system. It is a rigid-connected twin-tower skyscraper with asymmetrical distribution of stiffness and masses in two towers. Because of the different stiffness between the north and the south towers, the torsion seismic vibration is significant. In this paper, in order to study the seismic response of the structure under both frequent low-intensity earthquakes as well as rare earthquakes at the levels of intensity 7, the analysis model is built and analyzed with NosaCAD. NosaCAD is an nonlinear structure analysis software based on second-development of AutoCAD with ObjectARX. It has convenient modeling function, high computational efficiency and diversity post-processing functions. The deformations, forces and damages of the structure are investigated based on the analysis. According to the analysis, there is no damage on the structure under frequent earthquakes, and the structure has sufficient capacity and ductility to resist rare earthquakes. Therefore the structure can reach the goal of no damage under frequent earthquakes and no collapse under rare earthquakes. The deformation of the structure is below the limit in Chinese code. The time sequence and distribution of damages on tubes are reasonable, which can dissipate some dynamic energy. At last, according to forces, load-carrying capacity and damage of elements, there are some suggestions on increasing the reinforcement in the core tube at base and in stiffened stories.

The objective of this study is to reveal the sufficiency of neural networks (NN) as a securer, quicker, more robust and reliable method to be used in seismic assessment of existing reinforced concrete buildings. The NN based approach is applied as an alternative method to determine the seismic performance of each existing RC buildings, in terms of damage level. In the application of the NN, a multilayer perceptron (MLP) with a back-propagation (BP) algorithm is employed using a scaled conjugate gradient. NN based model wasd eveloped, trained and tested through a based MATLAB program. The database of this model was developed by using a statistical procedure called P25 method. The NN based model was also proved by verification set constituting of real existing RC buildings exposed to 2003 Bingol earthquake. It is demonstrated that the NN based approach is highly successful and can be used as an alternative method to determine the seismic performance of each existing RC buildings.