• Structural Engineering and Mechanics
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• v.86 no.6
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• pp.765-778
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• 2023

This study investigates the free vibration and buckling analyses of isotropic curved plate structures fixed at all ends. The Kirchhoff-Love Plate Theory (KLPT) and Finite Element Method (FEM) are employed to model the curved structure. In order to perform the finite element analysis, a four-node quadrilateral element with 5 degrees of freedom (DOF) at each node is utilized. Additionally, the drilling effect (θ_z) is considered as minimal to satisfy the DOF of the structure. Lagrange's equation of motion is used in order to obtain the first ten natural frequencies and the critical buckling values of the structure. The effects of various radii of curvatures and aspect ratio on the natural frequency and critical buckling load values for the single-bay and two-bay curved frames are investigated within this scope. A computer code based on finite element analysis is developed to perform free vibration and buckling analysis of curved plate frames. The natural frequency and critical buckling load values of the present study are compared with ANSYS R18.2 results. It has been concluded that the results of the present study are in good agreement with ANSYS results for different radii of curvatures and aspect ratio values of both single-bay and two-bay structures.

• Geomechanics and Engineering
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• v.33 no.6
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• pp.611-624
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• 2023

Soil properties make it attractive as a building material due to its mechanical strength, aesthetically appearance, plasticity, and low cost. However, it is frequently necessary to improve and stabilize the soil mechanical properties with binders. Soil-cement is applied for purposes ranging from housing to dams, roads and foundations. Unconfined compression strength (UCS) and split tensile strength (CD) are essential mechanical parameters for ascertaining the aptitude of soil-cement for a given application. However, quantifying these parameters requires specimen preparation, testing, and several weeks. Methodologies that allowed accurate estimation of mechanical parameters in shorter time would represent an important advance in order to ensure shorter deliverable timeline and reduce the amount of laboratory work. In this work, an extensive campaign of UCS and CD tests was carried out in a sandy soil from the Leiria region (Portugal). Then, using the machine learning tool Neural Pattern Recognition of the MATLAB software, a prediction of these two parameters based on six input parameters was made. The results, especially those obtained with resource to a Bayesian regularization-backpropagation algorithm, are frankly positive, with a forecast success percentage over 90% and very low root mean square error (RMSE).

• Structural Engineering and Mechanics
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• v.87 no.1
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• pp.19-28
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• 2023

This paper describes the experimental investigation of steel pallet rack beam-to-column connec-tions. Total behavior of moment-rotation (M-φ) curve and the effect of particular characteristics on the behavior of connection were studied and the associated load strain relationship and corre-sponding failure modes are presented. In this respect, an estimation of SPRBCCs moment and rotation are highly recommended in early stages of design and construction. In this study, a new approach based on Support Vector Machines (SVMs) coupled with discrete wavelet transform (DWT) is designed and adapted to estimate SPRBCCs moment and rotation according to four input parameters (column thickness, depth of connector and load, beam depth,). Results of SVM-WAVELET model was compared with genetic programming (GP) and artificial neural networks (ANNs) models. Following the results, SVM-WAVELET algorithm is helpful in order to enhance the accuracy compared to GP and ANN. It was conclusively observed that application of SVM-WAVELET is especially promising as an alternative approach to estimate the SPRBCCs moment and rotation.

• Steel and Composite Structures
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• v.47 no.6
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• pp.759-779
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• 2023

The paper proposes two hybrid metaheuristic optimization and artificial neural network (ANN) methods for the close prediction of the ultimate axial compressive capacity of concentrically loaded concrete filled double skin steel tube (CFDST) columns. Two metaheuristic optimization, namely genetic algorithm (GA) and particle swarm optimization (PSO), approaches enable the dynamic training architecture underlying an ANN model by optimizing the number and sizes of hidden layers as well as the weights and biases of the neurons, simultaneously. The former is termed as GA-ANN, and the latter as PSO-ANN. These techniques utilize the gradient-based optimization with Bayesian regularization that enhances the optimization process. The proposed GA-ANN and PSO-ANN methods construct the predictive ANNs from 125 available experimental datasets and present the superior performance over standard ANNs. Both the hybrid GA-ANN and PSO-ANN methods are encoded within a user-friendly graphical interface that can reliably map out the accurate ultimate axial compressive capacity of CFDST columns with various geometry and material parameters.

• Geomechanics and Engineering
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• v.34 no.5
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• pp.507-527
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• 2023

Accurately estimation of the geo-mechanical parameters in Artificial Ground Freezing (AGF) is a most important scientific topic in soil improvement and geotechnical engineering. In order for this, one way is using classical and conventional constitutive models based on different theories like critical state theory, Hooke's law, and so on, which are time-consuming, costly, and troublous. The others are the application of artificial intelligence (AI) techniques to predict considered parameters and behaviors accurately. This study presents a comprehensive data-mining-based model for predicting the Young's Modulus of frozen sand under the triaxial test. For this aim, several single and hybrid models were considered including additive regression, bagging, M5-Rules, M5P, random forests (RF), support vector regression (SVR), locally weighted linear (LWL), gaussian process regression (GPR), and multi-layered perceptron neural network (MLP). In the present study, cell pressure, strain rate, temperature, time, and strain were considered as the input variables, where the Young's Modulus was recognized as target. The results showed that all selected single and hybrid predicting models have acceptable agreement with measured experimental results. Especially, hybrid Additive Regression-Gaussian Process Regression and Bagging-Gaussian Process Regression have the best accuracy based on Model performance assessment criteria.

• Geomechanics and Engineering
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• v.34 no.5
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• pp.561-575
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• 2023

This study investigates to provide a fast solution to the problem of bearing capacity in layered soils with easily obtainable parameters that does not require the use of any charts or calculations of different parameters. Therefore, a hybrid approach including both the finite element (FE) method and machine learning technique have been applied. Firstly, a FE model has been generated which is validated by the results of in-situ loading tests. Then, a total of 192 three-dimensional FE analyses have been performed. A data set has been created utilizing the soil properties, footing sizes, layered conditions used in the FE analyses and the ultimate bearing capacity values obtained from the FE analyses to be used in multigene genetic programming (MGGP). Problem has been modeled with five input and one output parameter to propose a bearing capacity formula. Ultimate bearing capacity values estimated from the proposed formula using data set consisting of 20 data independent of total data set used in MGGP modelling have been compared to the bearing capacities calculated with semi-empirical methods. It was observed that the MGGP method yielded successful results for the problem considered. The proposed formula provides reasonable predictions and efficient enough to be used in practice.

• Steel and Composite Structures
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• v.46 no.6
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• pp.731-744
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• 2023

Vehicle load information is an important role in operating and ensuring the structural health of cable-stayed bridges. In this regard, an efficient and economic method is proposed for vehicle load detection based on the observed cable tension and vehicle position using a graph neural network (GNN). Datasets are first generated using the practical advanced analysis program (PAAP), a robust program for modeling and considering both geometric and material nonlinearities of bridge structures subjected to vehicle load with low computational costs. With the superiority of GNN, the proposed model is demonstrated to precisely capture complex nonlinear correlations between the input features and vehicle load in the output. Four popular machine learning methods including artificial neural network (ANN), decision tree (DT), random forest (RF), and support vector machines (SVM) are refereed in a comparison. A case study of a cable-stayed bridge with the typical truck is considered to evaluate the model's performance. The results demonstrate that the GNN-based model provides high accuracy and efficiency in prediction with satisfactory correlation coefficients, efficient determination values, and very small errors; and is a novel approach for vehicle load detection with the input data of the existing monitoring system.

• Advances in aircraft and spacecraft science
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• v.9 no.4
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• pp.335-347
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• 2022

Airport operations are well-known as a bottleneck in the air traffic system, putting growing pressure on the world's busiest airports to schedule arrivals and departures as efficiently as possible. Effective planning and control are essential for increasing airport efficiency and reducing aircraft delays. Many algorithms for controlling the arrival/departure queuing area are handled, considering it as first in first out queues, where any available aircraft can take off regardless of its relative sequence with other aircraft. In the suggested system, this problem was compared to the problem of scheduling n tasks (plane takeoffs and landings) on a multiple machine (runways). The proposed technique decreases delays (via efficient runway allocation or allowing aircraft to be expedited to reach a scheduled time) to enhance runway capacity and decrease delays. The aircraft scheduling problem entails arranging aircraft on available runways and scheduling their landings and departures while considering any operational constraints. The topic of this work is the scheduling of aircraft landings and takeoffs on multiple runways. Each aircraft's takeoff and landing schedules have time windows, as well as minimum separation intervals between landings and takeoffs. We present and evaluate a variety of comprehensive concepts and solutions for scheduling aircraft arrival and departure times, intending to reduce delays relative to scheduled times. When compared to First Come First Serve scheduling algorithm, the suggested strategy is usually successful in reducing the average waiting time and average tardiness while optimizing runway use.

• Computers and Concrete
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• v.32 no.5
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• pp.475-486
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• 2023

Nondestructive evaluation (NDE) is an important task of civil engineering structure monitoring and inspection, but minor damage such as small cracks in local structure is difficult to observe. If cracks continued expansion may cause partial or even overall damage to the structure. Therefore, monitoring and detecting the structure in the early stage of crack propagation is important. The crack detection technology based on machine vision has been widely studied, but there are still some problems such as bad recognition effect for small cracks. In this paper, we proposed a deep learning method based on sweep signals to evaluate concrete surface crack with a width less than 1 mm. Two convolutional neural networks (CNNs) are used to analyze the one-dimensional (1D) frequency sweep signal and the two-dimensional (2D) time-frequency image, respectively, and the probability value of average damage (ADPV) is proposed to evaluate the minor damage of structural. Finally, we use the standard deviation of energy ratio change (ERVSD) and infrared thermography (IRT) to compare with ADPV to verify the effectiveness of the method proposed in this paper. The experiment results show that the method proposed in this paper can effectively predict whether the concrete surface is damaged and the severity of damage.

• Steel and Composite Structures
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• v.49 no.6
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• pp.645-666
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• 2023

Due to the steadily declining supply of natural coarse aggregates, the concrete industry has shifted to substituting coarse aggregates generated from byproducts and industrial waste. Oil palm shell is a substantial waste product created during the production of palm oil (OPS). When considering the usage of OPSC, building engineers must consider its uniaxial compressive strength (UCS). Obtaining UCS is expensive and time-consuming, machine learning may help. This research established five innovative hybrid AI algorithms to predict UCS. Aquila optimizer (AO) is used with methods to discover optimum model parameters. Considered models are artificial neural network (AO - ANN), adaptive neuro-fuzzy inference system (AO - ANFIS), support vector regression (AO - SVR), random forest (AO - RF), and extreme gradient boosting (AO - XGB). To achieve this goal, a dataset of OPS-produced concrete specimens was compiled. The outputs depict that all five developed models have justifiable accuracy in UCS estimation process, showing the remarkable correlation between measured and estimated UCS and models' usefulness. All in all, findings depict that the proposed AO - XGB model performed more suitable than others in predicting UCS of OPSC (with R², RMSE, MAE, VAF and A_15-index at 0.9678, 1.4595, 1.1527, 97.6469, and 0.9077). The proposed model could be utilized in construction engineering to ensure enough mechanical workability of lightweight concrete and permit its safe usage for construction aims.