• Computers and Concrete
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• v.14 no.5
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• pp.577-597
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• 2014

This paper aims to develop a prediction model for the hardened properties of waste LCD glass that is used in concrete by analyzing a series of laboratory test results, which were obtained in our previous study. We also summarized the testing results of the hardened properties of a variety of waste LCD glass concretes and discussed the effect of factors such as the water-binder ratio (w/b), waste glass content (G) and age (t) on the concrete compressive strength, flexural strength and ultrasonic pulse velocity. This study also applied a hyperbolic function, an exponential function and a power function in a non-linear regression analysis of multiple variables and established the prediction model that could consider the effect of the water-binder ratio (w/b), waste glass content (G) and age (t) on the concrete compressive strength, flexural strength and ultrasonic pulse velocity. Compared with the testing results, the statistical analysis shows that the coefficient of determination $R^2$ and the mean absolute percentage error (MAPE) were 0.93-0.96 and 5.4-8.4% for the compressive strength, 0.83-0.89 and 8.9-12.2% for the flexural strength and 0.87-0.89 and 1.8-2.2% for the ultrasonic pulse velocity, respectively. The proposed models are highly accurate in predicting the compressive strength, flexural strength and ultrasonic pulse velocity of waste LCD glass concrete. However, with other ranges of mixture parameters, the predicted models must be further studied.

• Smart Structures and Systems
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• v.25 no.3
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• pp.369-384
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• 2020

A wavefield sparse reconstruction technique based on compressed sensing is developed in this work to dramatically reduce the number of measurements. Firstly, a severely underdetermined representation of guided wavefield at a snapshot is established in the spatial domain. Secondly, an optimal compressed sensing model of guided wavefield sparse reconstruction is established based on l₁-norm penalty, where a suite of discrete cosine functions is selected as the dictionary to promote the sparsity. The regular, random and jittered undersampling schemes are compared and selected as the undersampling matrix of compressed sensing. Thirdly, a gradient projection method is employed to solve the compressed sensing model of wavefield sparse reconstruction from highly incomplete measurements. Finally, experiments with different excitation frequencies are conducted on an aluminum plate to verify the effectiveness of the proposed sparse reconstruction method, where a scanning laser Doppler vibrometer as the true benchmark is used to measure the original wavefield in a given inspection region. Experiments demonstrate that the missing wavefield data can be accurately reconstructed from less than 12% of the original measurements; The reconstruction accuracy of the jittered undersampling scheme is slightly higher than that of the random undersampling scheme in high probability, but the regular undersampling scheme fails to reconstruct the wavefield image; A quantified mapping relationship between the sparsity ratio and the recovery error over a special interval is established with respect to statistical modeling and analysis.

• Structural Engineering and Mechanics
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• v.77 no.3
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• pp.343-354
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• 2021

The frequencies formulas of the bridge are of great importance in the design process since these formulas provide insight dynamic characteristics of the structure, which guides the designers to parametric analyses and the layout of the bridge in conceptual or preliminary design. Continuous rigid frame bridge is popular in the mountainous area. Mostly, this type of bridge was simplified either as a girder or cantilever when calculating the frequency, however, studies showed that the different configuration of the bridge made the problem more complex, and there is no unified fundamental calculation pattern for this kind of bridge. In this study, an empirical frequency equation is proposed as a function of pier's height, stiffness of pier and the weight of the structure. A unified fundamental frequency formula is presented based on the energy principle, then the typical continuous rigid frame bridge is investigated by finite element method (FEM) to study the dynamic characteristics of the structure, and then several key parameters are investigated on the effect of structural frequency. These parameters include the number, position and stiffness of the tie beam. Nonlinear regression analyses are conducted with a comprehensive statistical study from plenty of engineering structures. Finally, the proposed frequency equation is validated by field test results. The results show that the fundamental frequency of the continuous rigid frame bridge increases more than 15% when the tie beams are set, and it increases with the stiffness ratio of tie beam to pier. The results also show that the presented unified fundamental frequency has an error of 4.6% compared with the measured results. The investigation can predicate the approximate longitudinal fundamental frequency of continuous ridged frame bridge, which can provide reference for the seismic response and dynamic impact factor design of the pier.

• Advances in Computational Design
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• v.5 no.2
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• pp.195-207
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• 2020

The present study focuses on the application of artificial neural network (ANN) and Multiple linear Regression (MLR) analysis for developing a model to predict the unconfined compressive strength (UCS) and split tensile strength (STS) of the fiber reinforced clay stabilized with grass ash, fly ash and lime. Unconfined compressive strength and Split tensile strength are the nonlinear functions and becomes difficult for developing a predicting model. Artificial neural networks are the efficient tools for predicting models possessing non linearity and are used in the present study along with regression analysis for predicting both UCS and STS. The data required for the model was obtained by systematic experiments performed on only Kaolin clay, clay mixed with varying percentages of fly ash, grass ash, polypropylene fibers and lime as between 10-20%, 1-4%, 0-1.5% and 0-8% respectively. Further, the optimum values of the various stabilizing materials were determined from the experiments. The effect of stabilization is observed by performing compaction tests, split tensile tests and unconfined compression tests. ANN models are trained using the inputs and targets obtained from the experiments. Performance of ANN and Regression analysis is checked with statistical error of correlation coefficient (R) and both the methods predict the UCS and STS values quite well; but it is observed that ANN can predict both the values of UCS as well as STS simultaneously whereas MLR predicts the values separately. It is also observed that only STS values can be predicted efficiently by MLR.

• Korean Chemical Engineering Research
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• v.54 no.5
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• pp.621-629
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• 2016

Recently, the researches on quantitative structure activity relationship (QSAR) for describing toxicities or activities of chemicals based on chemical structural characteristics have been widely carried out in order to estimate the toxicity of chemicals in multiuse facilities. Because the toxicity of chemicals are explained by various kinds of molecular descriptors, an important step for QSAR model development is how to select significant molecular descriptors. This research proposes a statistical selection of significant molecular descriptors and a new QSAR model based on partial least square (PLS). The proposed QSAR model is applied to estimate the logarithm of partition coefficients (log P) of 130 polychlorinated biphenyls (PCBs) and lethal concentration ($LC_{50}$) of 14 PCBs, where the prediction accuracies of the proposed QSAR model are compared to a conventional QSAR model provided by OECD QSAR toolbox. For the selection of significant molecular descriptors that have high correlation with molecular descriptors and activity information of the chemicals of interest, correlation coefficient (r) and variable importance of projection (VIP) are applied and then PLS model of the selected molecular descriptors and activity information is used to predict toxicities and activity information of chemicals. In the prediction results of coefficient of regression ($R^2$) and prediction residual error sum of square (PRESS), the proposed QSAR model showed improved prediction performances of log P and $LC_{50}$ by 26% and 91% than the conventional QSAR model, respectively. The proposed QSAR method based on computational toxicology can improve the prediction performance of the toxicities and the activity information of chemicals, which can contribute to the health and environmental risk assessment of toxic chemicals.