• Earthquakes and Structures
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• v.14 no.4
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• pp.305-312
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• 2018

Significant research efforts were undertaken to evaluate seismic performance of vertically irregular buildings on flat ground. However, there is scarcity of study on seismic performance of buildings on hill slopes. The present study attempts to investigate seismic behaviour of reinforced concrete irregular stepback building frames with different configurations on sloping ground. Based on extensive regression study of free vibration results of four hundred seventeen frames with varying ground slope, number of story and span number, a modification is proposed to the code based empirical fundamental time period estimation formula. The modification to the fundamental time period estimation formula is a simplified function of ground slope and a newly introduced equivalent height parameter to reflect the effect of stiffness and mass irregularity. The derived empirical formula is successfully validated with various combinations of slope and framing configurations of buildings. The correlation between the predicted and the actual time period obtained from the free vibration analysis results are in good agreement. The various statistical parameters e.g., the root mean square error, coefficient of determination, standard average error generally used for validation of such regression equations also ensure the prediction capability of the proposed empirical relation with reasonable accuracy.

• Earthquakes and Structures
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• v.22 no.6
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• pp.571-584
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• 2022

The residual displacement ratio (RDRs) response spectra have been generally used as an important means to evaluate the post-earthquake repairability, and the ratios of residual to maximum inelastic displacement are considered to be more appropriate for development of the spectra. This methodology, however, assumes that the expected residual displacement can be computed as the product of the RDRs and maximum inelastic displacement, without considering the correlation between these two variables, which inevitably introduces potential systematic error. For providing an adequately accurate estimate of residual displacement, while accounting for the collapse resistance performance prior to the repairability evaluation, a probability-based procedure to estimate the residual displacement demands using the nonlinear static analysis (NSA) is developed for single-degree-of-freedom (SDOF) systems. To this end, the energy-based equivalent damping ratio used for NSA is revised to obtain the maximum displacement coincident with the nonlinear time history analysis (NTHA) results in the mean sense. Then, the possible systematic error resulted from RDRs spectra methodology is examined based on the NTHA results of SDOF systems. Finally, the statistical relation between the residual displacement and the NSA-based maximum displacement is established. The results indicate that the energy-based equivalent damping ratio will underestimate the damping for short period ranges, and overestimate the damping for longer period ranges. The RDRs spectra methodology generally leads to the results being non-conservative, depending on post-yield stiffness. The proposed approach emphasizes that the repairability evaluation should be based on the premise of no collapse, which matches with the current performance-based seismic assessment procedure.

• Structural Engineering and Mechanics
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• v.83 no.4
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• pp.515-535
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• 2022

The strength models for fiber-reinforced polymer (FRP)-confined normal strength concrete (NC) cylinders available in the literature have been suggested based on small databases using limited variables of such structural members portraying less accuracy. The artificial neural network (ANN) is an advanced technique for precisely predicting the response of composite structures by considering a large number of parameters. The main objective of the present investigation is to develop an ANN model for the axial strength of FRP-confined NC cylinders using various parameters to give the highest accuracy of the predictions. To secure this aim, a large experimental database of 313 FRP-confined NC cylinders has been constructed from previous research investigations. An evaluation of 33 different empirical strength models has been performed using various statistical parameters (root mean squared error RMSE, mean absolute error MAE, and coefficient of determination R²) over the developed database. Then, a new ANN model using the Group Method of Data Handling (GMDH) has been proposed based on the experimental database that portrayed the highest performance as compared with the previous models with R²=0.92, RMSE=0.27, and MAE=0.33. Therefore, the suggested ANN model can accurately capture the axial strength of FRP-confined NC cylinders that can be used for the further analysis and design of such members in the construction industry.

• Wind and Structures
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• v.31 no.4
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• pp.311-319
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• 2020

India is a developing nation and heavily spends on the development of wind power plants to meet the national energy demand. The objective of this paper is to investigate wind power potential of Ennore site using wind data collected over a period of two years by three parameter Weibull distribution. The Weibull parameters are estimated using maximum likelihood, least square method and moment method and the accuracy is determined using R² and root mean square error values. The site specific capacity factor is calculated by the mathematical model developed by three parameter Weibull distribution at different hub heights above the ground level. At last, the wind energy economic analysis is carried out using capacity factor at 30 m, 40 m and 50 m height for different wind turbine models. The analysis showed that the site has potential to install utility wind turbines to generate energy at the lowest cost per kilowatt-hour at height of 50 m. This research provides information of wind characteristics of potential sites and helps in selecting suitable wind turbine.

• Advances in nano research
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• v.12 no.5
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• pp.515-527
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• 2022

The application of fibers in concrete obviously enhances the properties of concrete, also the application of natural fibers in concrete is raising due to the availability, low cost and environmentally friendly. Besides, predicting the mechanical properties of concrete in general and shear strength in particular is highly significant in concrete mixture with fiber nanocomposite reinforced concrete (FRC) in construction projects. Despite numerous studies in shear strength, determining this strength still needs more investigations. In this research, Adaptive Neuro-Fuzzy Inference System (ANFIS) have been employed to determine the strength of reinforced concrete with fiber. 180 empirical data were gathered from reliable literature to develop the methods. Models were developed, validated and their statistical results were compared through the root mean squared error (RMSE), determination coefficient (R²), mean absolute error (MAE) and Pearson correlation coefficient (r). Comparing the RMSE of PSO (0.8859) and ANFIS (0.6047) have emphasized the significant role of structural parameters on the shear strength of concrete, also effective depth, web width, and a clear depth rate are essential parameters in modeling the shear capacity of FRC. Considering the accuracy of our models in determining the shear strength of FRC, the outcomes have shown that the R² values of PSO (0.7487) was better than ANFIS (2.4048). Thus, in this research, PSO has demonstrated better performance than ANFIS in predicting the shear strength of FRC in case of accuracy and the least error ratio. Thus, PSO could be applied as a proper tool to maximum accuracy predict the shear strength of FRC.

• Geomechanics and Engineering
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• v.28 no.4
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• pp.397-404
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• 2022

Accurate prediction of the undrained shaft resistance is essential for robust design of bored piles in undrained condition. The undrained shaft resistance is calculated using the undrained adhesion factor multiplied by the undrained cohesion of the soil. However, the available correlations to predict the undrained adhesion factor have been developed using simple regression techniques and the accuracy of these correlations has not been thoroughly assessed in previous studies. The lack of the assessment of these correlations made it difficult for geotechnical engineers to select the most accurate correlation in routine designs. Furthermore, limited attempts have been made in previous studies to use advanced data mining techniques to develop simple and accurate correlation to predict the undrained adhesion factor. This research, therefore, has been conducted to fill these gaps in knowledge by developing novel and robust correlation to predict the undrained adhesion factor. The development of the new correlation has been conducted using the multi-objective evolutionary polynomial regression analysis. The new correlation outperformed the available empirical correlations, where the new correlation scored lower mean absolute error, mean square error, root mean square error and standard deviation of measured to predicted adhesion factor, and higher mean, a20-index and coefficient of correlation. The correlation also successfully showed the influence of the undrained cohesion and the effective stress on the adhesion factor. Hence, the new correlation enhances the design accuracy and can be used by practitioner geotechnical engineers to ensure optimized designs of bored piles in undrained conditions.

• Advances in Computational Design
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• v.2 no.3
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• pp.225-240
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• 2017

This paper presents the application of multiple linear regression (MLR) and artificial neural network (ANN) techniques for developing the models to predict the unconfined compressive strength (UCS) and Brazilian tensile strength (BTS) of the fiber reinforced cement stabilized fly ash mixes. UCS and BTS is a highly nonlinear function of its constituents, thereby, making its modeling and prediction a difficult task. To establish relationship between the independent and dependent variables, a computational technique like ANN is employed which provides an efficient and easy approach to model the complex and nonlinear relationship. The data generated in the laboratory through systematic experimental programme for evaluating UCS and BTS of fiber reinforced cement fly ash mixes with respect to 7, 14 and 28 days' curing is used for development of the MLR and ANN model. The data used in the models is arranged in the format of four input parameters that cover the contents of cement and fibers along with maximum dry density (MDD) and optimum moisture contents (OMC), respectively and one dependent variable as unconfined compressive as well as Brazilian tensile strength. ANN models are trained and tested for various combinations of input and output data sets. Performance of networks is checked with the statistical error criteria of correlation coefficient (R), mean square error (MSE) and mean absolute error (MAE). It is observed that the ANN model predicts both, the unconfined compressive and Brazilian tensile, strength quite well in the form of R, RMSE and MAE. This study shows that as an alternative to classical modeling techniques, ANN approach can be used accurately for predicting the unconfined compressive strength and Brazilian tensile strength of fiber reinforced cement stabilized fly ash mixes.

• Structural Engineering and Mechanics
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• v.84 no.2
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• pp.143-154
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• 2022

Corrosion of steel reinforcement is considered as the main cause of concrete structures deterioration, especially those under humid environmental conditions. Hence, fiber reinforced polymer (FRP) bars are being increasingly used as a replacement for conventional steel owing to their non-corrodible characteristics. However, predicting the shear strength of beams reinforced with FRP bars still challenging due to the lack of robust shear theory. Thus, this paper aims to develop an explicit data driven based model to predict the shear strength of FRP reinforced beams using multi-objective evolutionary polynomial regression analysis (MOGA-EPR) as data driven models learn the behavior from the input data without the need to employee a theory that aid the derivation, and thus they have an enhanced accuracy. This study also evaluates the accuracy of predictive models of shear strength of FRP reinforced concrete beams employed by different design codes by calculating and comparing the values of the mean absolute error (MAE), root mean square error (RMSE), mean (𝜇), standard deviation of the mean (𝜎), coefficient of determination (R²), and percentage of prediction within error range of ±20% (a20-index). Experimental database has been developed and employed in the model learning, validation, and accuracy examination. The statistical analysis illustrated the robustness of the developed model with MAE, RMSE, 𝜇, 𝜎, R², and a20-index of 14.6, 20.8, 1.05, 0.27, 0.85, and 0.61, respectively for training data and 10.4, 14.1, 0.98, 0.25, 0.94, and 0.60, respectively for validation data. Furthermore, the developed model achieved much better predictions than the standard predictive models as it scored lower MAE, RMSE, and 𝜎, and higher R² and a20-index. The new model can be used in future with confidence in optimized designs as its accuracy is higher than standard predictive models.

• Composite Materials and Engineering
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• v.3 no.3
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• pp.201-220
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• 2021

In this study, potential of three machine learning techniques i.e., M5P, Support vector machines and Gaussian processes were evaluated to find the best algorithm for the prediction of flexural strength of concrete mix with steel fibre. The study comprises the comparison of results obtained from above-said techniques for given dataset. The dataset consists of 124 observations from past research studies and this dataset is randomly divided into two subsets namely training and testing datasets with (70-30)% proportion by weight. Cement, fine aggregates, coarse aggregates, water, super plasticizer/ high-range water reducer, steel fibre, fibre length and curing days were taken as input parameters whereas flexural strength of the concrete mix was taken as the output parameter. Performance of the techniques was checked by statistic evaluation parameters. Results show that the Gaussian process technique works better than other techniques with its minimum error bandwidth. Statistical analysis shows that the Gaussian process predicts better results with higher coefficient of correlation value (0.9138) and minimum mean absolute error (1.2954) and Root mean square error value (1.9672). Sensitivity analysis proves that steel fibre is the significant parameter among other parameters to predict the flexural strength of concrete mix. According to the shape of the fibre, the mixed type performs better for this data than the hooked shape of the steel fibre, which has a higher CC of 0.9649, which shows that the shape of fibers do effect the flexural strength of the concrete. However, the intricacy of the mixed fibres needs further investigations. For future mixes, the most favorable range for the increase in flexural strength of concrete mix found to be (1-3)%.

• Advances in environmental research
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• v.4 no.2
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• pp.83-104
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• 2015

Air Quality Index (AQI) is a pointer to broadcast short term air quality. This paper presents one day ahead AQI forecasting on seasonal basis for three major cities in Maharashtra State, India by using Artificial Neural Networks (ANN) and Genetic Programming (GP). The meteorological observations & previous AQI from 2005-2008 are used to predict next day's AQI. It was observed that GP captures the phenomenon better than ANN and could also follow the peak values better than ANN. The overall performance of GP seems better as compared to ANN. Stochastic nature of the input parameters and the possibility of auto-correlation might have introduced time lag and subsequent errors in predictions. Spectral Analysis (SA) was used for characterization of the error introduced. Correlational dependency (serial dependency) was calculated for all 24 models prepared on seasonal basis. Particular lags (k) in all the models were removed by differencing the series, that is converting each i'th element of the series into its difference from the (i-k)"th element. New time series is generated for all seasonal models in synchronization with the original time line & evaluated using ANN and GP. The statistical analysis and comparison of GP and ANN models has been done. We have proposed a promising approach of use of GP coupled with SA for real time prediction of seasonal multicity AQI.