• Computers and Concrete
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• 제20권1호
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• pp.31-38
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• 2017

In the present study an Artificial Neural Network (ANN) was used to predict the compressive strength of self-compacting concrete. The data developed experimentally for self-compacting concrete and the data sets of a total of 99 concrete samples were used in this work. ANN's are considered as nonlinear statistical data modeling tools where complex relationships between inputs and outputs are modeled or patterns are found. In the present ANN model, eight input parameters are used to predict the compressive strength of self-compacting of concrete. These include varying amounts of cement, coarse aggregate, fine aggregate, fly ash, fiber, water, super plasticizer (SP), viscosity modifying admixture (VMA) while the single output parameter is the compressive strength of concrete. The importance of different input parameters for predicting the strengths at various ages using neural network was discussed in the study. There is a perfect correlation between the experimental and prediction of the compressive strength of SCC based on ANN with very low root mean square errors. Also, the efficiency of ANN model is better compared to the multivariable regression analysis (MRA). Hence it can be concluded that the ANN model has more potential compared to MRA model in developing an optimum mix proportion for predicting the compressive strength of concrete without much loss of material and time.

• Wind and Structures
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• 제31권4호
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• pp.299-309
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• 2020

In this study, wind data such as speeds, loads and potential of Muğla which is located in the southwest of Turkey were statistically analyzed. The wind data which consists of hourly wind speed between 2010 and 2013 years, was measured at the 10-meters height in four different ground stations (Datça, Fethiye, Marmaris, Köyceğiz). These stations are operated by The Turkish State Meteorological Service (T.S.M.S). Furthermore, wind data was analyzed by using Log-Normal and Gamma distributions, since these distributions fit better than Weibull, Normal, Exponential and Logistic distributions. Root Mean Squared Error (RMSE) and the coefficients of the goodness of fit (R²) were also determined by using statistical analysis. According to the results, extreme wind speed in the research area was 33 m/s at the Datça station. The effective wind load at this speed is 0.68 kN/㎡. The highest mean power densities for Datça, Fethiye, Marmaris and Köyceğiz were found to be 46.2, 1.6, 6.5 and 2.2 W/㎡, respectively. Also, although Log-normal distribution exhibited a good performance i.e., lower AD (Anderson - Darling statistic (AD) values) values, Gamma distribution was found more suitable in the estimation of wind speed and power of the region.

• Steel and Composite Structures
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• 제45권1호
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• pp.119-131
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• 2022

Due to the complexity of the structure and the limits of classical SEA, a combined SEA approach is employed, with angle-dependent SEA in the low- and mid-frequency ranges and advanced SEA (ASEA) considering indirect coupling in the high-frequency range. As an important component of the steel box girder, the dynamic response of an L-junction periodic ribbed plate is calculated first by the combined SEA and validated by the impact hammer test and finite element method (FEM). Results show that the indirect coupling due to the periodicity of stiffened plate is significant at high frequencies and may cause the error to reach 38.4 dB. Hence, the incident bending wave angle cannot be ignored in comparison to classical SEA. The combined SEA is then extended to investigate the vibration properties of the steel box girder. The bending wave transmission study is likewise carried out to gain further physical insight into indirect coupling. By comparison with FEM and classical SEA, this approach yields good accuracy for calculating the dynamic responses of the steel box girder made of periodic ribbed plates in a wide frequency range. Furthermore, the influences of some important parameters are discussed, and suggestions for vibration and noise control are provided.

• Computers and Concrete
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• 제22권2호
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• pp.197-207
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• 2018

In the present study, group method of data handling networks (GMDH) are adopted and evaluated for shear strength prediction of both FRP-reinforced concrete members with and without stirrups. Input parameters considered for the GMDH are altogether 12 influential geometrical and mechanical parameters. Two available and very recently collected comprehensive datasets containing 112 and 175 data samples are used to develop new models for two cases with and without shear reinforcement, respectively. The proposed GMDH models are compared with several codes of practice. An artificial neural network (ANN) model and an ANFIS based model are also developed using the same databases to further assessment of GMDH. The accuracy of the developed models is evaluated by statistical error parameters. The results show that the GMDH outperforms other models and successfully can be used as a practical and effective tool for shear strength prediction of members without stirrups ($R^2=0.94$) and with stirrups ($R^2=0.95$). Furthermore, the relative importance and influence of input parameters in the prediction of shear capacity of reinforced concrete members are evaluated through parametric and sensitivity analyses.

• Wind and Structures
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• 제29권5호
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• pp.313-321
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• 2019

The wind blowing at high velocity in an open storage yard leads to wind erosion and loss of material. Fence structures can be constructed around the periphery of the storage yard to reduce the erosion. The fence will cause turbulence and recirculation behind it which can be utilized to reduce the wind erosion and loss of material. A properly designed fence system will produce lesser turbulence and longer shelter effect. This paper aims to show the applicability of Support Vector Machine (SVM) to predict the recirculation length. A SVM model was built, trained and tested using the experimental data gathered from the literature. The newly developed model is compared with numerical turbulence model, in particular, modified $k-{\varepsilon}$ model along with the experimental results. From the results, it was observed that the SVM model has a better capability in predicting the recirculation length. The SVM model was able to predict the recirculation length at a lesser time as compared to modified $k-{\varepsilon}$ model. All the results are analyzed in terms of statistical measures, such as root mean square error, correlation coefficient, and scatter index. These examinations demonstrate that SVM has a strong potential as a feasible tool for predicting recirculation length.

• Structural Engineering and Mechanics
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• 제72권2호
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• pp.203-216
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• 2019

Reliability analysis of composite structures considering random variation of involved parameters is quite important as composite materials revealed large statistical variations in their mechanical properties. The reliability analysis of such structures by the first order reliability method (FORM) and Monte Carlo Simulation (MCS) based approach involves repetitive evaluations of performance function. The response surface method (RSM) based metamodeling technique has emerged as an effective solution to such problems. In the application of metamodeling for uncertainty quantification and reliability analysis of composite structures; the finite element model is usually formulated by either classical laminate theory or first order shear deformation theory. But such theories show significant error in calculating the structural responses of composite structures. The present study attempted to apply the RSM based MCS for reliability analysis of composite shell structures where the surrogate model is constructed using higher order shear deformation theory (HSDT) of composite structures considering the uncertainties in the material properties, load, ply thickness and radius of curvature of the shell structure. The sensitivity of responses of the shell is also obtained by RSM and finite element method based direct approach to elucidate the advantages of RSM for response sensitivity analysis. The reliability results obtained by the proposed RSM based MCS and FORM are compared with the accurate reliability analysis results obtained by the direct MCS by considering two numerical examples.

• Computers and Concrete
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• 제31권2호
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• pp.111-121
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• 2023

The influence of material composition such as aggregate types, addition of supplementary cementitious materials as well as exposed temperature levels have significant impacts on concrete residual mechanical strength properties when exposed to elevated temperature. This study is based on data obtained from literature for fly ash blended concrete produced with natural and recycled concrete aggregates to efficiently develop prediction models for estimating its residual compressive strength after exposure to high temperatures. To achieve this, an extensive database that contains different mix proportions of fly ash blended concrete was gathered from published articles. The specific design variables considered were percentage replacement level of Recycled Concrete Aggregate (RCA) in the mix, fly ash content (FA), Water to Binder Ratio (W/B), and exposed Temperature level. Thereafter, a simplified mathematical equation for the prediction of concrete's residual compressive strength using Gene Expression Programming (GEP) was developed. The relative importance of each variable on the model outputs was also determined through global sensitivity analysis. The GEP model performance was validated using different statistical fitness formulas including R², MSE, RMSE, RAE, and MAE in which high R² values above 0.9 are obtained in both the training and validation phase. The low measured errors (e.g., mean square error and mean absolute error are in the range of 0.0160 - 0.0327 and 0.0912 - 0.1281 MPa, respectively) in the developed model also indicate high efficiency and accuracy of the model in predicting the residual compressive strength of fly ash blended concrete exposed to elevated temperatures.

• Smart Structures and Systems
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• 제4권5호
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• pp.629-640
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• 2008

A new algorithm is proposed to determine optimal accelerometer locations (OAL) when a structure is identified by frequency domain system identification (SI) method. As a result, a guideline is presented for selecting OAL which can reflect modal response of a structure properly. The guideline is to provide a minimum number of necessary accelerometers with the variation in the number of measurable target modes. To determine OAL for SI applications effectively, the modal sensitivity effective independence distribution vector (MS-EIDV) is developed with the likelihood function of measurements. By maximizing the likelihood of the occurrence of the measurements relative to the predictions, Fisher Information Matrix (FIM) is derived as a function of mode shape sensitivity. This paper also proposes a statistical approach in determining the structural parameters with a presumed parameter error which reflects the epistemic paradox between the determination of OAL and the application of a SI scheme. Numerical simulations have been carried out to examine the proposed OAL algorithm. A two-span multi-girder bridge and a two-span truss bridge were used for the simulation studies. To overcome a rank deficiency frequently occurred in inverting a FIM, the singular value decomposition scheme has been applied.

• Earthquakes and Structures
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• 제25권6호
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• pp.469-479
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• 2023

A probabilistic seismic damage analysis is an essential procedure to identify seismically vulnerable structures, prioritize the seismic retrofit, and ultimately minimize the overall seismic risk. To assess the seismic risk of multiple structures within a region, a large number of nonlinear time-history structural analyses must be conducted and studied. As a result, each assessment requires high computing resources. To overcome this limitation, we explore a deep learning-based metamodel to enable the prediction of the mean and the standard deviation of the seismic damage distribution of track-on steel-plate girder railway bridges in Korea considering the geometric variation. For machine learning training, nonlinear dynamic time-history analyses are performed to generate 800 high-fidelity datasets on the seismic response. Through intensive trial and error, the study is concentrated on developing an optimal machine learning architecture with the pre-identified variables of the physical configuration of the bridge. Additionally, the prediction performance of the proposed method is compared with a previous, well-defined, response surface model. Finally, the statistical testing results indicate that the overall performance of the deep-learning model is improved compared to the response surface model, as its errors are reduced by as much as 61%. In conclusion, the model proposed in this study can be effectively deployed for the seismic fragility and risk assessment of a region with a large number of structures.

• Geomechanics and Engineering
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• 제37권4호
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• pp.395-418
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• 2024

The epicentral region of earthquakes is typically where liquefaction-related damage takes place. To determine the maximum distance, such as maximum epicentral distance (R_e), maximum fault distance (R_f), or maximum hypocentral distance (R_h), at which an earthquake can inflict damage, given its magnitude, this study, using a recently updated global liquefaction database, multiple ML models are built to predict the limiting distances (R_e, R_f, or R_h) required for an earthquake of a given magnitude to cause damage. Four machine learning models LSTM (Long Short-Term Memory), BiLSTM (Bidirectional Long Short-Term Memory), CNN (Convolutional Neural Network), and XGB (Extreme Gradient Boosting) are developed using the Python programming language. All four proposed ML models performed better than empirical models for limiting distance assessment. Among these models, the XGB model outperformed all the models. In order to determine how well the suggested models can predict limiting distances, a number of statistical parameters have been studied. To compare the accuracy of the proposed models, rank analysis, error matrix, and Taylor diagram have been developed. The ML models proposed in this paper are more robust than other current models and may be used to assess the minimal energy of a liquefaction disaster caused by an earthquake or to estimate the maximum distance of a liquefied site provided an earthquake in rapid disaster mapping.