• Computers and Concrete
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• v.15 no.5
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• pp.759-770
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• 2015

In this study, a multi-layer perceptron neural network (MLPNN) prediction model for compressive strength of the cement mortars has been developed. For purpose of constructing this model, 8 different mixes with 240 specimens of the 2, 7, 28, 56 and 90 days compressive strength experimental results of cement mortars containing fly ash (FA), silica fume (SF) and FA+SF used in training and testing for MLPNN system was gathered from the standard cement tests. The data used in the MLPNN model are arranged in a format of four input parameters that cover the FA, SF, FA+SF and age of samples and an output parameter which is compressive strength of cement mortars. In the model, the training and testing results have shown that MLPNN system has strong potential as a feasible tool for predicting 2, 7, 28, 56 and 90 days compressive strength of cement mortars.

• Computers and Concrete
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• v.10 no.4
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• pp.379-390
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• 2012

In this paper, the mechanical strength of different lightweight mortars made with 0, 5, 10, 15, 20, 25, 30, 35, 40, 45, 50, 55, 60, 65, 70, 75, 80, 85, 90, 95 and 100 percentage of scoria instead of sand and 0.55 water-cement ratio and 350 $kg/m^3$ cement content is investigated. The experimental result showed 7.9%, 16.7% and 49% decrease in compressive strength, tensile strength and mortar density, respectively, by using 100% scoria instead of sand in the mortar. The normalized compressive and tensile strength data are applied for artificial neural network (ANN) generation using generalized regression neural network (GRNN). Totally, 90 experimental data were selected randomly and applied to find the best network with minimum mean square error (MSE) and maximum correlation of determination. The created GRNN with 2 input layers, 2 output layers and a network spread of 0.1 had minimum MSE close to 0 and maximum correlation of determination close to 1.

• Computers and Concrete
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• v.3 no.5
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• pp.357-373
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• 2006

The concepts of four parameters of nominal water-cement ratio, equivalent water-cement ratio, average paste thickness, fly ash-binder ratio were introduced. It was verified that the four parameters and the mix proportion of mortar can be transformed each other. The behaviors (strength, workability, et al.) of mortar primarily determined by the mix proportion of mortar now depend on the four parameters. The prediction models of strength and workability of mortar were built based on artificial neural networks (ANNs). The calculation models of average paste thickness and equivalent water-cement ratio of mortar can be obtained by the reversal deduction of the two prediction models, respectively. A mortar mix proportion design algorithm was proposed. The proposed mortar mix proportion design algorithm is expected to reduce the number of trial and error, save cost, laborers and time.

• Computers and Concrete
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• v.4 no.1
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• pp.1-18
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• 2007

Over the past decades, an enormous amount of effort has been expended in laboratory and field studies on concrete durability estimation. The results of this research are still either widely scattered in the journal literature or mentioned briefly in the standard textbooks. Moreover, the theoretical approaches of deterioration mechanisms with a predictive character are limited to some complicated mathematical models not widespread in practice. A significant step forward could be the development of appropriate software for computer-based estimation of concrete service life, including reliable mathematical models and adequate experimental data. In the present work, the basis for the development of a computer estimation of the concrete service life is presented. After the definition of concrete mix design and structure characteristics, as well as the consideration regarding the environmental conditions where the structure will be found, the concrete service life can be reliably predicted using fundamental mathematical models that simulate the deterioration mechanisms. The prediction is focused on the basic deterioration phenomena of reinforced concrete, such as carbonation and chloride penetration, that initiate the reinforcing bars corrosion. Aspects on concrete strength and the production cost are also considered. Field observations and data collection from existing structures are compared with predictions of service life using the above model. A first attempt to develop a database of service lives of different types of reinforced concrete structure exposed to varying environments is finally included.

• International Journal of Concrete Structures and Materials
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• v.4 no.1
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• pp.37-43
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• 2010

Theoretical models based on modern interpretations of the morphology and interactions of cement hydration products are developed for prediction of the mechanical properties of hydrated cement paste (hcp). The models are based on the emerging nanostructural vision of calcium silicate hydrate (C-S-H) morphology, and account for the intermolecular interactions between nano-scale calcium C-S-H particles. The models also incorporate the effects of capillary porosity and microcracking within hydrated cement paste. The intrinsic modulus of elasticity and tensile strength of hydrated cement paste are determined based on intermolecular interactions between C-S-H nano-particles. Modeling of fracture toughness indicates that frictional pull-out of the micro-scale calcium hydroxide (CH) platelets makes major contributions to the fracture energy of hcp. A tensile strength model was developed for hcp based on the linear elastic fracture mechanics theories. The predicted theoretical models are in reasonable agreements with empirical models developed based on the experimental performance of hcp.

• Computers and Concrete
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• v.25 no.5
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• pp.461-470
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• 2020

This paper presents a numerical study on structural behavior of hammer head pier cap beams, extended on verges and reinforced with carbon fiber reinforced polymer (CFRP) plates. A 3-D finite element (FE) model along with a simplified analytical model are presented. Concrete damage plasticity (CDP) was adapted in the FE model and an analytical approach predicting the CFRP anchor strength was adapted in both FE and analytical model. Total five quarter-scaled pier cap beams with various CFRP reinforcing schemes were experimentally tested and analyzed with numerical approaches. Comparison between experimental results, FE results, analytical results and current ACI guideline predictions was presented. The FE results showed good agreement with experimental results in terms of failure mode, ultimate capacity, load-displacement response and strain distribution. In addition, the proposed strut-and-tie based analytical model provides the most accurate prediction of ultimate strength of extended cap beams among the three numerical approaches.

• Proceedings of the Computational Structural Engineering Institute Conference
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• 1999.10a
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• pp.19-26
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• 1999

This paper describes an analytical study on nonlinear hysteretic behavior of hybrid steel beam with reinforced concrete ends. Two types of analytical model, Polygonal Model[PM] and Hybrid Model[HM], were used to represent the nonlinear hysteretic behavior PM used three parameters, HM used an additional parameter to consider the initial stiffness reduction. The parameters calibrated comparing the hysteretic performance obtained from experiments. The purpose of this study is to develop an analytical model which can take into account the initial stiffness reduction of the hybrid members and to represent exactly the hysteretic performance for the hybrid structures with RC and steel. The analytical study showed PM tends to overestimate initial stiffness and strength. However, HM which is capable to consider the initial stiffness reduction gave good prediction on initial stiffness, post-yielding performance, strength, pinching response and so on.

• Smart Structures and Systems
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• v.30 no.2
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• pp.209-219
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• 2022

The determination for bearing capacity of precast nodular piles is conventionally time-consuming and high-cost by using numerous experiments and empirical methods. This study proposes an intelligent method to evaluate the bearing capacity and shaft resistance of the nodular piles with high efficiency based on long short-term memory (LSTM) approach. A series of field tests are first designed to measure the axial force, shaft resistance and displacement of the combined nodular piles under different loadings, in comparison with the single pre-stressed high-strength concrete piles. The test results confirm that the combined nodular piles could provide larger ultimate bearing capacity (more than 100%) than the single pre-stressed high-strength concrete piles. Both the LSTM-based method and empirical methods are used to calculate the shift resistance of the combined nodular piles. The results show that the LSTM-based method has a high-precision estimation on shaft resistance, not only for the ultimate load but also for the working load.

• Computers and Concrete
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• v.19 no.2
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• pp.217-226
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• 2017

This paper summarizes the results of experimental research, and artificial intelligence methods focused on determination of compressive strength of lightweight cement mortar with silica fume and fly ash after sulfate attack. The artificial neural network and the support vector machine were selected as artificial intelligence methods. Lightweight cement mortar mixtures containing silica fume and fly ash were prepared in this study. After specimens were cured in $20{\pm}2^{\circ}C$ waters for 28 days, the specimens were cured in different sulfate concentrations (0%, 1% $MgSO_4^{-2}$, 2% $MgSO_4^{-2}$, and 4% $MgSO_4^{-2}$ for 28, 60, 90, 120, 150, 180, 210 and 365 days. At the end of these curing periods, the compressive strengths of lightweight cement mortars were tested. The input variables for the artificial neural network and the support vector machine were selected as the amount of cement, the amount of fly ash, the amount of silica fumes, the amount of aggregates, the sulfate percentage, and the curing time. The compressive strength of the lightweight cement mortar was the output variable. The model results were compared with the experimental results. The best prediction results were obtained from the artificial neural network model with the Powell-Beale conjugate gradient backpropagation training algorithm.

• Structural Engineering and Mechanics
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• v.54 no.4
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• pp.741-754
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• 2015

This study evaluates prediction models for three EDPs (engineering demand parameters) using data from three symmetrical structures with RC walls designed according to the currently enforced Romanian seismic design code P100-1/2013. The three analyzed EDPs are: the maximum interstorey drift, the maximum top displacement and the maximum shear force at the base of the RC walls. The strong ground motions used in this study consist of three pairs of recordings from the Vrancea intermediate-depth earthquakes of 1977, 1986 and 1990, as well as two other pairs of recordings from significant earthquakes in Turkey and Greece (Erzincan and Aigion). The five pairs of recordings are rotated in a clockwise direction and the values of the EDPs are recorded. Finally, the relation between various IMs (intensity measures) of the strong ground motion records and the EDPs is studied and two prediction models for EDPs are also evaluated using the analysis of residuals.