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Estimation of the settlement of pile head using ANN and multivariate linear regression based on the results of load transfer method

  • Khabeer Al-Awad ORCID logo , Mohammed Y. Fattah ORCID logo and Ahmed Sh. J. Al-Zuheriy ORCID logo EMAIL logo
Published/Copyright: July 1, 2024
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Open Engineering
From the journal Open Engineering Volume 14 Issue 1

Abstract

Artificial neural networks, machine learning, and data preparation are normally implemented in a wide range of real-world problems, especially in geotechnical applications with optimistic prospects of accurate procedure outcomes. This technique has been utilized to precisely predict the top settlement of piles with various piles and soil parameters. Generally, the pile settlement is an essential requirement to produce a secure structure and has high-performance services. The current article presents the fitting of the artificial neural network (ANN) outcomes by calculating the coefficient of correlation R 2 between the predicted and the measured or calculated value of pile settlement. The ANN algorithm is developed using Python 3.9 IDLE and open-source libraries such as Keras, sklearn, Numpy, matplotlib, pandas, and Tensorflow. Because of random training and test performance, the model has been run at least ten times. The ANN model score and R 2 are compared for all runs in the testing phase. The higher score and R 2 values are chosen. Moreover, the Multivariate Linear Regression with the sklearn library is also offered in this article and utilized to produce a pile settlement formula by applying the same dataset used in ANN. The score and R 2 for choosing the first run of the ANN are 99.95% and 0.9631, respectively, while the correlation coefficient for the Multivariate Linear Regression in the training and testing phases is 0.972 and 0.919, respectively. Both techniques illustrate considerable results.

Keywords: artificial neural networks; machine learning; top settlement of piles; multivariate linear regression; correlation coefficient; load transfer method

1 Introduction

Deep foundations (piles) are an essential part of any structure; the basic function of these foundations is to transfer the superstructure load to a deeper and stronger bearing stratum. For common knowledge of geotechnical engineering, piles can be chosen when the use of shallow foundations is not recommended because the bearing capacity of subsurface soil is inadequate to carry the proposed building. The settlement of foundations is another basic considerable parameter. Therefore, the estimated subsoil settlement with shallow footing must be also less than the allowable settlement of such footing, otherwise, piles will be alternative footing. The vast challenge, of pile settlement calculations, is enormous uncertainty factors relevant to soil properties that affect the magnitude of the settlement [1]. Poulos [2] reviewed theoretical and experimental investigations in the last twenty to thirty years. All of these studies stated conventional methods to estimate the settlement of different foundation types (shallow and deep foundations). It was concluded that most of such methods could be adjusted or ignored. So, in terms of geotechnical engineering, the comparison between the estimated bearing capacity of a pile using ANN and several empirical formulas was achieved. The outcomes of the ANN model acceptable estimated piles bearing capacity [3,4,5,6,7].

2 Artificial neural networks (ANN)

Neural Networks are an immensely useful model of machine learning techniques, with countless applications for real scenario problems. The application of ANNs to solve numerous geotechnical issues becomes a frequent approach with a high degree of success, and this is because of the development of data management. ANNs are one of the most common artificial intelligent forms. The earlier study to apply ANN was introduced in 1985. This study did not reach a finished form of ANN, the recurrent networks and sigma-pi units were only investigated. Although the learning procedure for many very complex problems has not been applied, the results data promising approaches for further study [8]. Generally, ANNs could be considered a good growing tool that can be used in prediction and forecasting fields such as weather forecasting, stock markets, financial institutions, and scientific research studies. Gnananandarao et al. [9] confirmed the ability of ANN models to predict a complex relationship between the nonlinear data of the predicted settlement of shallow foundations on cohesionless soils. The capability of back-propagation neural networks was investigated to predict the settlement of piles with an adequate rate of accuracy in comparison with conventional methods [10,11]. For more applications of ANN models, artificial neural networks (ANNs) have been effectively applied more than the traditional methods to estimate a shallow foundation settlement on granular soils [12]. Overall, ANNs are useful and powerful implements to solve numerous field geotechnical problems [13].

Since ANNs are widely used to predict the foundation settlement, a sequential model of ANN using Python is developed to estimate the pile settlement of single axially loaded piles based on the load transfer method. This is the first objective of the current study.

3 Multivariate linear regression

Very little literature was found in geotechnical research studies utilizing Multivariate Linear Regression (MLR). However, general regression can be overviewed here. Recep [14] stated the use of multiple linear regression MLR and polynomial equations to offer an expression predicting the maximum moment across a cantilever sheet pile with a correlation coefficient of 0.971. The correlation between in situ settlement measured and predicted values using the multivariate adaptive regression splines (MARS) method showed a satisfactory relation so the study offered the highest accuracy in the regression process [14].

The evaluation of the MLR and ANN model’s performance was attained by finding the coefficient of correlation R for estimation of uniaxial compressive strength (UCS) and modulus of elasticity (E) of intact rock [15]. Tarawneh and Imam [16] stated that the ANN model outperformed the Multiple Linear Regression MLR model and can be adequately utilized to predict pile setup. Furthermore, in comparison between a multilayer perceptron (MLP) of artificial neural networks and a regression model, the former model showed higher performance in the prediction of undrained cohesion intercept of fine soil than the latter model [17].

The second objective of the current article is to use multivariate linear regression (MLR) by implementing the ordinary least squares (OLS). This class can be found in the open-source library of sci-kit – learn 1.0.2. The input data set used in this algorithm is the same data set that is utilized in the ANN model. The normalization of input data is also performed to obtain more accurate outcomes. The data set is divided into 60% training (105 samples) and 40% testing (70 samples) for input data for the MLR model. The output of the model is compared with normalized pile settlement by determination of R 2 in the training and testing phases.

3.1 Data input/output

The data set for both ANN and MLR models is obtained from an unpublished numerical study relevant to the current article author. The input data set contains 175 samples, each sample has 17 parameters. These parameters are addressed as features of both models, while the resulting pile head settlement is considered the output of the models, which is named a model's target. The features represent the pile geometry as well as soil profile characteristics. Although the determination of pile settlement using the load transfer method is out of the scope of this article, some details can be presented here related to the ANN and MLR input features.

Poulos and Davis [18] revealed that the pile settlement could be calculated using the parameters of pile length, pile shaft diameter, pile base diameter, pile and soil Young’s modulus, soil Poisson’s ratio, rigid stratum layer depth, slenderness ratio, pile base shaft diameter ratio, pile-soil young’s modulus ratio, pile embedded ratio, incompressible pile settlement factor, compressible pile correction factors, rigid stratum depth correction factor, soil Poisson’s ratio correction factor, stiffness of bearing stratum correction factor, and finally, applied axial load. These 17 parameters or variables were calculated by applying various charts [18]. The settlement of the pile is the outcome of the numerical analysis based on experimental work of the tip and skin resistances of bored piles in London clay [19]. The use of numerical study results rather than the experimental investigation may be a good idea to take into account the pile geometry and soil mechanical properties in ANN inputs.

In this article, the input parameters are L s, D s, D b, E p, E s, Ѵ s, h, L/D s, D b/D s, E p/E s, L/h, I o, R k, R h, R v, I, P, and the output is the pile top settlement (sett), the definition of these features or variables are presented in notation list. The features and resulting pile settlement are tabulated in columns to make the total data set include 175 samples, which are saved as a comma-delimited file to read the data using a pandas data frame. It is worth practicing to enhance the performance of ANN by improving the correlation between the input parameters, extracting the information from poor input data, and applying the ANN to develop new design approaches to geotechnical problems [20].

3.2 Data cleaning

Despite the ANNs being powerful models, the uncertainties related to the measurement of geotechnical parameters should be essentially treated to obtain more realistic results [13]. Therefore, before entering the ANN or MLR model, data cleaning is an effective step in any machine-learning model to improve its performance [21]. For the current study, the data set of pile geometry and soil profile characteristics is prepared in a spreadsheet. Various statistical analysis and data visualization approaches, such as histogram and outlier identification, can be implemented to discover the nature of data correlation. Data cleaning operations include identifying and eliminating column variables (features) that only have a single value, or very few unique values, and finally determining and removing the data set samples that contain duplicate observations [21,22].

In this investigation, four of 17 columns of parameters (features of I o, R k, R h, and R v) are manually merged in one feature I, because the values of I were already calculated dependent on I o, R k, R h, and R v as Poulos and Davis [18] stated this relationship. Some relevant features should be merged into one to boost the performance of the ANN model [21]. Consequently, 13 parameters would be entered in the ANN model instead of 17. The parameters will be only L s, D s, D b, E p, E s, Ѵ s, h, L/D s, D b/D s, E p/E s, L/h, I, and P which are used as input data for the ANN and MLR models.

To understand the nature of the data and the distribution of each input and output parameter, a box and whisker, and histogram plots are presented to identify the outlier and data variation for all input and output data as shown in Figures 1 and 2. This is a beneficial practice to realize the significant influence of the different variables on the accuracy of model output [23].

Figure 1 Box and Whisker plot input data and Pile_Sett as output data.
Figure 1

Box and Whisker plot input data and Pile_Sett as output data.

Figure 2 Histogram of input data and Pile_Sett as output data.
Figure 2

Histogram of input data and Pile_Sett as output data.

4 Ann model structure

A supervisor ANN model with two hidden layers is developed using the open-source library of Keras module with IDLE python 3.9, a flow chart is presented in Figure 3, and a pseudo-code is also shown in the Appendix and shows the algorithm of the model. Such a model is required to read a data set, which consists of independent parameters (features) as input data, as well as the dependent data as a target of desired data (Pile_Sett). The feature data represent pile specification, and soil properties and the target data donate measured top-head pile settlement. As mentioned earlier, these data are obtained from unpublished studies relevant to this article’s author. The ANN model structure consists of an input layer, a couple of hidden layers, and an input layer, several processing elements (PE), or neurons that are usually arranged in hidden layers. In this model, each neuron in the first hidden layer is fully connected to each input parameter via its weight (randomly generated), and a threshold value (bias) is added to form a perceptron. The value of each neuron comes from the summation of the input perceptions. Before moving to the next hidden layer, the value of the neurons can be transferred using the activation function to make the Neural Network nonlinearly work. In the current ANN sequential model, the activation function E-swish is used for both hidden layers output, this function consistently outperformed other activation functions on a wide range of real-world problems and showed state of art results for different data sets [24]. This process is summarized in Figure 4.

Figure 3 A flow chart of ANN algorithm.
Figure 3

A flow chart of ANN algorithm.

Figure 4 ANN structure.
Figure 4

ANN structure.

The neurons (units) learning, of this ANN model, include training, validation, and testing. Therefore, the obtained data are divided into 60% for training, 20% for validation, and 20% for testing. The input data set (features as an array, and target as a vector) is randomly divided according to learning data division percentages. As stated above, the data set has 175 samples (a measurement set of 13 parameters). For the currently used ANN model, the E-swish function is customized, in the Keras sequential model, as an activation function [24,25]. Moreover, mean squared logarithmic error (MSLE) is chosen to be a loss function that is used to solve the regression phase as shown below. MSLE is calculated as the average of the squared differences between the log-transformed actual and predicted values. Adam optimizer is selected to reduce the error and the learning rate is 0.01, this rate shows the best model performance. For more model details, the maximum number of iterations (epoch) is 100. After taking a range of epochs 50, 80, and 100, the maximum number of 100 offers the best accuracy and good training.

(1) MSLE = 1 n ∑ i = 1 n ( log ( y actual i + 1 ) − log ( y predict i + 1 ) ) ,

where n is the number of data set samples, y actual is the actual value of the data set samples, and y predict is the predicted value of the data set samples (the resulting value of ANN model run).

In terms of ANN structure, the current model consists of two hidden layers, in addition to input and output layers. The number of neurons in the first hidden layer is 13, the same number of features. The sequence hidden layer (the second layer) has half the neurons (units) of the first layer (i.e. around six units).

Before entering the ANN model, the normalized input data set illustrates better accuracy than the standardization of such data. Therefore, the normalization of data is performed before running the model [10]. Since this model works by random data splitting and randomly generates the weight and biases for both hidden layers, this model has been run at least ten times. Subsequently, the best value of the coefficient of correlation R 2 and model accuracy has been chosen.

In this study, the data sets have been normalized between 0 and 1 for input and output data sets using the following equation:

(2) Normalized varibale value = the orignal variable value − minmum value of the data set maximum value of the dataset − minmum value of the data set .

5 Model validation and testing

The validation of the training phase is usually executed to confirm the phase performance so that the model effectively achieves its proposed determination. For this practice, the validation data sets have not been utilized as input data for the learning process.

The correlation coefficient R, Mean Squared Logarithmic Error (MSLE) and accuracy score are the essential criteria that are used to assess the predictive ability of the ANN model. The relative correlation between the actual or desired output and the prediction data can be measured by calculation of R 2. The Mean Squared Logarithmic Error (MSLE) is the most common cost function chosen to measure the error in the descent gradient stage.

The accuracy score for the model is also calculated and if for any reason such as randomly dividing the data set, this accuracy score is low, the ANN model is run ten times to obtain a high descent accuracy score.

6 Results and discussion

The ANN and MLR models have been run and the outcomes of both models are discussed and presented in this section.

6.1 ANN run

The ANN model is run at least ten times because the input data set is randomly divided for training, validation, and testing phases. The divided dataset may be altered at each new run. Therefore, the ANN score and the coefficient of correlation R 2 in the testing phase also show various values as shown in Figure 5.

Figure 5 ANN Score and R 2 at the testing phase.
Figure 5

ANN Score and R 2 at the testing phase.

From Figure 5, it can be noticed that the first run demonstrates the maximum values for both R 2 and the score of the ANN, they are 96.31 and 99.95% respectively. All outcomes of this run can be chosen as perfect ANN model parameters such as the weights and biases for all layers of connection of the ANN model.

To obtain the optimal values of the parameters, the minimum value of the chosen loss function is calculated by the iterative running of the gradient descent technique. Mathematically, this technique is the derivative of the loss function to attain the minimum point. Learning rate is the size of the derivative step that offers some additional control over how large steps can be made. Despite the large step requiring less time consuming, the desired lowest point could be overshot. Whereas a low learning rate is more precise, the gradient descent takes a very long time to reach the minimum slope of the loss function [24]. Therefore, the used learning rate in this model is 0.01, this rate is commonly utilized [26]. Figure 6 depicts the gradient descent of the loss function for the training and testing phases. It can be noticed that the losses reach the lowest point when the iteration ranges from 80 to 100.

Figure 6 ANN model loss in training and testing phases.
Figure 6

ANN model loss in training and testing phases.

The plot of the measured (target) and predicted settlement for the testing set for the ANN model are shown in Figure 7. The correlation coefficient is 0.963.

Figure 7 The predicted and testing normalized pile top settlement.
Figure 7

The predicted and testing normalized pile top settlement.

The weight of each parameter for the optimal ANN model (best results) can be shown for comparison purposes. To avoid presenting too much information, only the first neuron for each layer is illustrated. The change of each parameter percentage participates in calculating the first neuron value in the first and second hidden layers (Figure 8a and b). For example, the participation percentage of the applied vertical load (P) is 32% in the first neuron of the first hidden layer, whereas this percentage drops to 3% in the first neuron of the second hidden layer. It can be observed that the percentage change of other parameters for the mentioned neurons is illustrated in Figure 8.

Figure 8 (a) The first neuron of the first hidden layer and (b) the first neuron of the second hidden layer.
Figure 8

(a) The first neuron of the first hidden layer and (b) the first neuron of the second hidden layer.

Figure 9 shows the percentage of each of the six weights (portions) to predict the normalized pile settlement in the output layer. Each portion was formed by various percentages of all parameters.

Figure 9 Output neuron with its six weights to predict pile settlement.
Figure 9

Output neuron with its six weights to predict pile settlement.

6.2 MLR run

A linear mathematical model is formed in this section. Therefore, the multivariate linear regression is run using the same data set which is used in the ANN model. All features are also normalized before running the MLR model. The data is divided into 60% for the training phase and 40% for the testing phase.

Figure 10 depicts the correlation between the normalized training pile settlement from the data set which is defined as measured pile settlement and the prediction of normalized pile settlement using Multivariate Linear Regression MLR. This regression offers a coefficient of determination R 2 of 0.972 and Intercept of (−0.10966845).

Figure 10 The correlation between the normalized training data set which is defined as measured pile settlement and the predicted normalizing pile settlement due to regression.
Figure 10

The correlation between the normalized training data set which is defined as measured pile settlement and the predicted normalizing pile settlement due to regression.

In the testing phase, the resulting regression coefficients in the training phase are used with normalized features of the testing phase to estimate the normalized pile top settlement. The following equation is presented by applying the normalized values of all independent parameters, and these parameters are detonated in a later Notation list:

(3) prediction_test = coef . dot ( X _test . T ) + intercept ,

or applying this model on a testing data set of 70 samples as the following

(4) Normalized p ile settlement = − 0.18138084 ( L s ) − 0.2645253 ( D s ) + 0.01658562 ( D b ) − 0.06294832 ( E p ) − 0.02684476 ( E s ) − 0.00455433 ( Ѵ s ) − 0.00282297 ( h ) + 0.24802737 ( L / D s ) − 0.00253914 ( D b / D s ) + 0.49255763 ( E p / E s ) − 0.00588953 ( L / h ) + 0.34158744 ( I ) + 0.35446004 ( P ) .

Figure 11 illustrates the correlation between the normalized testing pile settlement from a data set defined as measured pile settlement and the prediction of normalized pile settlement using the mathematical equation mentioned above. The relationship shows a coefficient of determination R 2 of 0.919.

Figure 11 The correlation between the normalized testing data set which is defined as measured pile settlement and the predicted normalizing pile settlement due to regression.
Figure 11

The correlation between the normalized testing data set which is defined as measured pile settlement and the predicted normalizing pile settlement due to regression.

Comparisons between the results obtained from the optimal ANN model and Multivariate linear regression MLR to estimate the pile settlement are presented in this section. As shown in Figure 12, the passion’s ratio, depth of rigid stratum, pile length to stratum depth ratio, and pile base diameter to shaft diameter ratio are insignificant parameters to predict normalized pile settlement. Unlike, these parameters have substantial proportions in the ANN model.

Figure 12 The participation percentages of parameters to estimate pile settlement using MLR.
Figure 12

The participation percentages of parameters to estimate pile settlement using MLR.

Treatment of pile problems using numerical and ANN models has been covered by several researchers [27,28]. The present study proved the capability of ANN algorithms in solving the problem of pile settlement.

7 Conclusions

Keras-sequential ANN model has been utilized to predict the top head pile settlement. This model has state of the art activation function E-swish in the transformation stage and has a loss function of mean squared logarithmic error (MSLE) in the gradient descent stage as mentioned above. The following conclusions could be obtained:

  1. The results indicate that the current neural network can predict the pile top settlement with high accuracy (coefficient of determination, R 2 = 0.9631, MSLE = 1.06 mm) for anticipated settlements ranging from 2.753 to 38.786 mm.

  2. The distinctively essential advantage of using the ANN model, once the model is trained, the model can predict the top head pile settlement. However, the traditional methods to calculate pile settlement, such as elastic solution and load transfer method, these methods are required using charts and tables manually to calculate pile geometry and soil properties correction factors.

  3. Moreover, it is worth practicing to form a mathematical model predicting pile top head settlement using MLR which also shows a great correlation between estimated and measured pile top head settlement with R 2 = 0.919. Accordingly, in terms of machine learning, the use of MLR is promising to be applied for determining pile settlement based on input data set division.

  4. Finally, as a result, the use of ANNs shows beneficial practices and powerful implementation to predict pile settlement. In the testing phase, the ANN model offers higher performance than the multiverse linear regression MLR as [16,17] reported.

  1. Funding information: Authors state no funding involved.

  2. Author contributions: All authors have accepted responsibility for the entire content of this manuscript and consented to its submission to the journal, reviewed all the results, and approved the final version of the manuscript. Conceptualization, MYF and ASJA-Z; methodology, KA-A and ASJA-Z; software, KA-A; validation, MYF and KA-A; formal analysis, KA-A and ASJA-Z; investigation, KA-A and ASJA-Z; resources, MYF; data curation, KA-A; writing ‒ original draft preparation, KA-A and ASJA-Z; supervision, MYF; project administration, MYF.

  3. Conflict of interest: The authors state no conflict of interest.

  4. Data availability statement: Most data sets generated and analyzed in this study are comprised in this submitted manuscript. The other data sets are available on reasonable request from the corresponding author with the attached information.

Appendix

ANN Pseudocode:

Import standard library; NumPy, pandas, sklearn, TensorFlow, Keras, and matplotlib.

Read dataset of samples file has 13 parameters and 175 samples (features.csv)/pandas

Read separately the target file has 175 samples (target. CSV)/pandas

Convert the DataFrames features and targets to NumPy.arrays

Using the number of neurons in the first hidden layer = parameters No = 13

Using the number of neurons in the second hidden layer = parameters No/2 = 6

Normalization of features and targets

Randomly split the dataset to train 60%, test 40%, and validation 18% of trained data (/sklearn.

Save the data as x_train, y_train, x_test, and y_test.

Customization of the external activation function (E-swish function, ẞ = 1.5) for all hidden layers outcomes.

Update of custom_objects for the Keras model.

Build an ANN model using Keras. sequential

Using Mean Squared Logarithmic Error (MSLE), loss function

Compile with Adam optimizer and learning rate = 0.01

History fitting X_train and Y-train and epoch = 100

Prediction model

Save the weights and biases for all layers

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Received: 2024-03-05
Revised: 2024-03-26
Accepted: 2024-04-01
Published Online: 2024-07-01

© 2024 the author(s), published by De Gruyter

This work is licensed under the Creative Commons Attribution 4.0 International License.

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  17. Application of the Taguchi method and RSM for process parameter optimization in AWSJ machining of CFRP composite-based orthopedic implants
  18. Improved correlation of soil modulus with SPT N values
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  28. Effect of the reinforcement ratio on the mechanical behaviour of textile-reinforced concrete composite: Experiment and numerical modeling
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  31. Evaluation of the creep strength of samples produced by fused deposition modeling
  32. A combined feedforward-feedback controller design for nonlinear systems
  33. Effect of adjacent structures on footing settlement for different multi-building arrangements
  34. Analyzing the impact of curved tracks on wheel flange thickness reduction in railway systems
  35. Review Articles
  36. Mechanical and smart properties of cement nanocomposites containing nanomaterials: A brief review
  37. Applications of nanotechnology and nanoproduction techniques
  38. Relationship between indoor environmental quality and guests’ comfort and satisfaction at green hotels: A comprehensive review
  39. Communication
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  41. Erratum
  42. Erratum to “Effect of short heat treatment on mechanical properties and shape memory properties of Cu–Al–Ni shape memory alloy”
  43. Special Issue: AESMT-3 - Part II
  44. Integrated fuzzy logic and multicriteria decision model methods for selecting suitable sites for wastewater treatment plant: A case study in the center of Basrah, Iraq
  45. Physical and mechanical response of porous metals composites with nano-natural additives
  46. Special Issue: AESMT-4 - Part II
  47. New recycling method of lubricant oil and the effect on the viscosity and viscous shear as an environmentally friendly
  48. Identify the effect of Fe2O3 nanoparticles on mechanical and microstructural characteristics of aluminum matrix composite produced by powder metallurgy technique
  49. Static behavior of piled raft foundation in clay
  50. Ultra-low-power CMOS ring oscillator with minimum power consumption of 2.9 pW using low-voltage biasing technique
  51. Using ANN for well type identifying and increasing production from Sa’di formation of Halfaya oil field – Iraq
  52. Optimizing the performance of concrete tiles using nano-papyrus and carbon fibers
  53. Special Issue: AESMT-5 - Part II
  54. Comparative the effect of distribution transformer coil shape on electromagnetic forces and their distribution using the FEM
  55. The complex of Weyl module in free characteristic in the event of a partition (7,5,3)
  56. Restrained captive domination number
  57. Experimental study of improving hot mix asphalt reinforced with carbon fibers
  58. Asphalt binder modified with recycled tyre rubber
  59. Thermal performance of radiant floor cooling with phase change material for energy-efficient buildings
  60. Surveying the prediction of risks in cryptocurrency investments using recurrent neural networks
  61. A deep reinforcement learning framework to modify LQR for an active vibration control applied to 2D building models
  62. Evaluation of mechanically stabilized earth retaining walls for different soil–structure interaction methods: A review
  63. Assessment of heat transfer in a triangular duct with different configurations of ribs using computational fluid dynamics
  64. Sulfate removal from wastewater by using waste material as an adsorbent
  65. Experimental investigation on strengthening lap joints subjected to bending in glulam timber beams using CFRP sheets
  66. A study of the vibrations of a rotor bearing suspended by a hybrid spring system of shape memory alloys
  67. Stability analysis of Hub dam under rapid drawdown
  68. Developing ANFIS-FMEA model for assessment and prioritization of potential trouble factors in Iraqi building projects
  69. Numerical and experimental comparison study of piled raft foundation
  70. Effect of asphalt modified with waste engine oil on the durability properties of hot asphalt mixtures with reclaimed asphalt pavement
  71. Hydraulic model for flood inundation in Diyala River Basin using HEC-RAS, PMP, and neural network
  72. Numerical study on discharge capacity of piano key side weir with various ratios of the crest length to the width
  73. The optimal allocation of thyristor-controlled series compensators for enhancement HVAC transmission lines Iraqi super grid by using seeker optimization algorithm
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  75. Effect of nano-TiO2 on physical and rheological properties of asphalt cement
  76. Performance evolution of novel palm leaf powder used for enhancing hot mix asphalt
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  80. Experimental investigation of RC beams strengthened with externally bonded BFRP composites
  81. Generalized RKM methods for solving fifth-order quasi-linear fractional partial differential equation
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  84. Implementation for the cases (5, 4) and (5, 4)/(2, 0)
  85. Center group actions and related concepts
  86. Experimental investigation of the effect of horizontal construction joints on the behavior of deep beams
  87. Deletion of a vertex in even sum domination
  88. Deep learning techniques in concrete powder mix designing
  89. Effect of loading type in concrete deep beam with strut reinforcement
  90. Studying the effect of using CFRP warping on strength of husk rice concrete columns
  91. Parametric analysis of the influence of climatic factors on the formation of traditional buildings in the city of Al Najaf
  92. Suitability location for landfill using a fuzzy-GIS model: A case study in Hillah, Iraq
  93. Hybrid approach for cost estimation of sustainable building projects using artificial neural networks
  94. Assessment of indirect tensile stress and tensile–strength ratio and creep compliance in HMA mixes with micro-silica and PMB
  95. Density functional theory to study stopping power of proton in water, lung, bladder, and intestine
  96. A review of single flow, flow boiling, and coating microchannel studies
  97. Effect of GFRP bar length on the flexural behavior of hybrid concrete beams strengthened with NSM bars
  98. Exploring the impact of parameters on flow boiling heat transfer in microchannels and coated microtubes: A comprehensive review
  99. Crumb rubber modification for enhanced rutting resistance in asphalt mixtures
  100. Special Issue: AESMT-6
  101. Design of a new sorting colors system based on PLC, TIA portal, and factory I/O programs
  102. Forecasting empirical formula for suspended sediment load prediction at upstream of Al-Kufa barrage, Kufa City, Iraq
  103. Optimization and characterization of sustainable geopolymer mortars based on palygorskite clay, water glass, and sodium hydroxide
  104. Sediment transport modelling upstream of Al Kufa Barrage
  105. Study of energy loss, range, and stopping time for proton in germanium and copper materials
  106. Effect of internal and external recycle ratios on the nutrient removal efficiency of anaerobic/anoxic/oxic (VIP) wastewater treatment plant
  107. Enhancing structural behaviour of polypropylene fibre concrete columns longitudinally reinforced with fibreglass bars
  108. Sustainable road paving: Enhancing concrete paver blocks with zeolite-enhanced cement
  109. Evaluation of the operational performance of Karbala waste water treatment plant under variable flow using GPS-X model
  110. Design and simulation of photonic crystal fiber for highly sensitive chemical sensing applications
  111. Optimization and design of a new column sequencing for crude oil distillation at Basrah refinery
  112. Inductive 3D numerical modelling of the tibia bone using MRI to examine von Mises stress and overall deformation
  113. An image encryption method based on modified elliptic curve Diffie-Hellman key exchange protocol and Hill Cipher
  114. Experimental investigation of generating superheated steam using a parabolic dish with a cylindrical cavity receiver: A case study
  115. Effect of surface roughness on the interface behavior of clayey soils
  116. Investigated of the optical properties for SiO2 by using Lorentz model
  117. Measurements of induced vibrations due to steel pipe pile driving in Al-Fao soil: Effect of partial end closure
  118. Experimental and numerical studies of ballistic resistance of hybrid sandwich composite body armor
  119. Evaluation of clay layer presence on shallow foundation settlement in dry sand under an earthquake
  120. Optimal design of mechanical performances of asphalt mixtures comprising nano-clay additives
  121. Advancing seismic performance: Isolators, TMDs, and multi-level strategies in reinforced concrete buildings
  122. Predicted evaporation in Basrah using artificial neural networks
  123. Energy management system for a small town to enhance quality of life
  124. Numerical study on entropy minimization in pipes with helical airfoil and CuO nanoparticle integration
  125. Equations and methodologies of inlet drainage system discharge coefficients: A review
  126. Thermal buckling analysis for hybrid and composite laminated plate by using new displacement function
  127. Investigation into the mechanical and thermal properties of lightweight mortar using commercial beads or recycled expanded polystyrene
  128. Experimental and theoretical analysis of single-jet column and concrete column using double-jet grouting technique applied at Al-Rashdia site
  129. The impact of incorporating waste materials on the mechanical and physical characteristics of tile adhesive materials
  130. Seismic resilience: Innovations in structural engineering for earthquake-prone areas
  131. Automatic human identification using fingerprint images based on Gabor filter and SIFT features fusion
  132. Performance of GRKM-method for solving classes of ordinary and partial differential equations of sixth-orders
  133. Visible light-boosted photodegradation activity of Ag–AgVO3/Zn0.5Mn0.5Fe2O4 supported heterojunctions for effective degradation of organic contaminates
  134. Production of sustainable concrete with treated cement kiln dust and iron slag waste aggregate
  135. Key effects on the structural behavior of fiber-reinforced lightweight concrete-ribbed slabs: A review
  136. A comparative analysis of the energy dissipation efficiency of various piano key weir types
  137. Special Issue: Transport 2022 - Part II
  138. Variability in road surface temperature in urban road network – A case study making use of mobile measurements
  139. Special Issue: BCEE5-2023
  140. Evaluation of reclaimed asphalt mixtures rejuvenated with waste engine oil to resist rutting deformation
  141. Assessment of potential resistance to moisture damage and fatigue cracks of asphalt mixture modified with ground granulated blast furnace slag
  142. Investigating seismic response in adjacent structures: A study on the impact of buildings’ orientation and distance considering soil–structure interaction
  143. Improvement of porosity of mortar using polyethylene glycol pre-polymer-impregnated mortar
  144. Three-dimensional analysis of steel beam-column bolted connections
  145. Assessment of agricultural drought in Iraq employing Landsat and MODIS imagery
  146. Performance evaluation of grouted porous asphalt concrete
  147. Optimization of local modified metakaolin-based geopolymer concrete by Taguchi method
  148. Effect of waste tire products on some characteristics of roller-compacted concrete
  149. Studying the lateral displacement of retaining wall supporting sandy soil under dynamic loads
  150. Seismic performance evaluation of concrete buttress dram (Dynamic linear analysis)
  151. Behavior of soil reinforced with micropiles
  152. Possibility of production high strength lightweight concrete containing organic waste aggregate and recycled steel fibers
  153. An investigation of self-sensing and mechanical properties of smart engineered cementitious composites reinforced with functional materials
  154. Forecasting changes in precipitation and temperatures of a regional watershed in Northern Iraq using LARS-WG model
  155. Experimental investigation of dynamic soil properties for modeling energy-absorbing layers
  156. Numerical investigation of the effect of longitudinal steel reinforcement ratio on the ductility of concrete beams
  157. An experimental study on the tensile properties of reinforced asphalt pavement
  158. Self-sensing behavior of hot asphalt mixture with steel fiber-based additive
  159. Behavior of ultra-high-performance concrete deep beams reinforced by basalt fibers
  160. Optimizing asphalt binder performance with various PET types
  161. Investigation of the hydraulic characteristics and homogeneity of the microstructure of the air voids in the sustainable rigid pavement
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  163. Special Issue: AESMT-7 - Part I
  164. Preparation and investigation of cobalt nanoparticles by laser ablation: Structure, linear, and nonlinear optical properties
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  166. The effect of urban environment on large-scale path loss model’s main parameters for mmWave 5G mobile network in Iraq
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  171. Behavior of container berth structure under the influence of environmental and operational loads
  172. Energy absorption and impact response of ballistic resistance laminate
  173. Effect of water-absorbent polymer balls in internal curing on punching shear behavior of bubble slabs
  174. Effect of surface roughness on interface shear strength parameters of sandy soils
  175. Evaluating the interaction for embedded H-steel section in normal concrete under monotonic and repeated loads
  176. Estimation of the settlement of pile head using ANN and multivariate linear regression based on the results of load transfer method
  177. Enhancing communication: Deep learning for Arabic sign language translation
  178. A review of recent studies of both heat pipe and evaporative cooling in passive heat recovery
  179. Effect of nano-silica on the mechanical properties of LWC
  180. An experimental study of some mechanical properties and absorption for polymer-modified cement mortar modified with superplasticizer
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  184. Evaluation of microstructure and mechanical properties of Al1050/Al2O3/Gr composite processed by forming operation ECAP
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  193. Geogrid reinforcement for improving bearing capacity and stability of square foundations
  194. Evaluation of the effluent concentrations of Karbala wastewater treatment plant using reliability analysis
  195. Adsorbent made with inexpensive, local resources
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https://doi.org/10.1515/eng-2024-0026
Keywords for this article
artificial neural networks; machine learning; top settlement of piles; multivariate linear regression; correlation coefficient; load transfer method
Creative Commons
BY 4.0

Articles in the same Issue

  1. Regular Articles
  2. Methodology of automated quality management
  3. Influence of vibratory conveyor design parameters on the trough motion and the self-synchronization of inertial vibrators
  4. Application of finite element method in industrial design, example of an electric motorcycle design project
  5. Correlative evaluation of the corrosion resilience and passivation properties of zinc and aluminum alloys in neutral chloride and acid-chloride solutions
  6. Will COVID “encourage” B2B and data exchange engineering in logistic firms?
  7. Influence of unsupported sleepers on flange climb derailment of two freight wagons
  8. A hybrid detection algorithm for 5G OTFS waveform for 64 and 256 QAM with Rayleigh and Rician channels
  9. Effect of short heat treatment on mechanical properties and shape memory properties of Cu–Al–Ni shape memory alloy
  10. Exploring the potential of ammonia and hydrogen as alternative fuels for transportation
  11. Impact of insulation on energy consumption and CO2 emissions in high-rise commercial buildings at various climate zones
  12. Advanced autopilot design with extremum-seeking control for aircraft control
  13. Adaptive multidimensional trust-based recommendation model for peer to peer applications
  14. Effects of CFRP sheets on the flexural behavior of high-strength concrete beam
  15. Enhancing urban sustainability through industrial synergy: A multidisciplinary framework for integrating sustainable industrial practices within urban settings – The case of Hamadan industrial city
  16. Advanced vibrant controller results of an energetic framework structure
  17. Application of the Taguchi method and RSM for process parameter optimization in AWSJ machining of CFRP composite-based orthopedic implants
  18. Improved correlation of soil modulus with SPT N values
  19. Technologies for high-temperature batch annealing of grain-oriented electrical steel: An overview
  20. Assessing the need for the adoption of digitalization in Indian small and medium enterprises
  21. A non-ideal hybridization issue for vertical TFET-based dielectric-modulated biosensor
  22. Optimizing data retrieval for enhanced data integrity verification in cloud environments
  23. Performance analysis of nonlinear crosstalk of WDM systems using modulation schemes criteria
  24. Nonlinear finite-element analysis of RC beams with various opening near supports
  25. Thermal analysis of Fe3O4–Cu/water over a cone: a fractional Maxwell model
  26. Radial–axial runner blade design using the coordinate slice technique
  27. Theoretical and experimental comparison between straight and curved continuous box girders
  28. Effect of the reinforcement ratio on the mechanical behaviour of textile-reinforced concrete composite: Experiment and numerical modeling
  29. Experimental and numerical investigation on composite beam–column joint connection behavior using different types of connection schemes
  30. Enhanced performance and robustness in anti-lock brake systems using barrier function-based integral sliding mode control
  31. Evaluation of the creep strength of samples produced by fused deposition modeling
  32. A combined feedforward-feedback controller design for nonlinear systems
  33. Effect of adjacent structures on footing settlement for different multi-building arrangements
  34. Analyzing the impact of curved tracks on wheel flange thickness reduction in railway systems
  35. Review Articles
  36. Mechanical and smart properties of cement nanocomposites containing nanomaterials: A brief review
  37. Applications of nanotechnology and nanoproduction techniques
  38. Relationship between indoor environmental quality and guests’ comfort and satisfaction at green hotels: A comprehensive review
  39. Communication
  40. Techniques to mitigate the admission of radon inside buildings
  41. Erratum
  42. Erratum to “Effect of short heat treatment on mechanical properties and shape memory properties of Cu–Al–Ni shape memory alloy”
  43. Special Issue: AESMT-3 - Part II
  44. Integrated fuzzy logic and multicriteria decision model methods for selecting suitable sites for wastewater treatment plant: A case study in the center of Basrah, Iraq
  45. Physical and mechanical response of porous metals composites with nano-natural additives
  46. Special Issue: AESMT-4 - Part II
  47. New recycling method of lubricant oil and the effect on the viscosity and viscous shear as an environmentally friendly
  48. Identify the effect of Fe2O3 nanoparticles on mechanical and microstructural characteristics of aluminum matrix composite produced by powder metallurgy technique
  49. Static behavior of piled raft foundation in clay
  50. Ultra-low-power CMOS ring oscillator with minimum power consumption of 2.9 pW using low-voltage biasing technique
  51. Using ANN for well type identifying and increasing production from Sa’di formation of Halfaya oil field – Iraq
  52. Optimizing the performance of concrete tiles using nano-papyrus and carbon fibers
  53. Special Issue: AESMT-5 - Part II
  54. Comparative the effect of distribution transformer coil shape on electromagnetic forces and their distribution using the FEM
  55. The complex of Weyl module in free characteristic in the event of a partition (7,5,3)
  56. Restrained captive domination number
  57. Experimental study of improving hot mix asphalt reinforced with carbon fibers
  58. Asphalt binder modified with recycled tyre rubber
  59. Thermal performance of radiant floor cooling with phase change material for energy-efficient buildings
  60. Surveying the prediction of risks in cryptocurrency investments using recurrent neural networks
  61. A deep reinforcement learning framework to modify LQR for an active vibration control applied to 2D building models
  62. Evaluation of mechanically stabilized earth retaining walls for different soil–structure interaction methods: A review
  63. Assessment of heat transfer in a triangular duct with different configurations of ribs using computational fluid dynamics
  64. Sulfate removal from wastewater by using waste material as an adsorbent
  65. Experimental investigation on strengthening lap joints subjected to bending in glulam timber beams using CFRP sheets
  66. A study of the vibrations of a rotor bearing suspended by a hybrid spring system of shape memory alloys
  67. Stability analysis of Hub dam under rapid drawdown
  68. Developing ANFIS-FMEA model for assessment and prioritization of potential trouble factors in Iraqi building projects
  69. Numerical and experimental comparison study of piled raft foundation
  70. Effect of asphalt modified with waste engine oil on the durability properties of hot asphalt mixtures with reclaimed asphalt pavement
  71. Hydraulic model for flood inundation in Diyala River Basin using HEC-RAS, PMP, and neural network
  72. Numerical study on discharge capacity of piano key side weir with various ratios of the crest length to the width
  73. The optimal allocation of thyristor-controlled series compensators for enhancement HVAC transmission lines Iraqi super grid by using seeker optimization algorithm
  74. Numerical and experimental study of the impact on aerodynamic characteristics of the NACA0012 airfoil
  75. Effect of nano-TiO2 on physical and rheological properties of asphalt cement
  76. Performance evolution of novel palm leaf powder used for enhancing hot mix asphalt
  77. Performance analysis, evaluation, and improvement of selected unsignalized intersection using SIDRA software – Case study
  78. Flexural behavior of RC beams externally reinforced with CFRP composites using various strategies
  79. Influence of fiber types on the properties of the artificial cold-bonded lightweight aggregates
  80. Experimental investigation of RC beams strengthened with externally bonded BFRP composites
  81. Generalized RKM methods for solving fifth-order quasi-linear fractional partial differential equation
  82. An experimental and numerical study investigating sediment transport position in the bed of sewer pipes in Karbala
  83. Role of individual component failure in the performance of a 1-out-of-3 cold standby system: A Markov model approach
  84. Implementation for the cases (5, 4) and (5, 4)/(2, 0)
  85. Center group actions and related concepts
  86. Experimental investigation of the effect of horizontal construction joints on the behavior of deep beams
  87. Deletion of a vertex in even sum domination
  88. Deep learning techniques in concrete powder mix designing
  89. Effect of loading type in concrete deep beam with strut reinforcement
  90. Studying the effect of using CFRP warping on strength of husk rice concrete columns
  91. Parametric analysis of the influence of climatic factors on the formation of traditional buildings in the city of Al Najaf
  92. Suitability location for landfill using a fuzzy-GIS model: A case study in Hillah, Iraq
  93. Hybrid approach for cost estimation of sustainable building projects using artificial neural networks
  94. Assessment of indirect tensile stress and tensile–strength ratio and creep compliance in HMA mixes with micro-silica and PMB
  95. Density functional theory to study stopping power of proton in water, lung, bladder, and intestine
  96. A review of single flow, flow boiling, and coating microchannel studies
  97. Effect of GFRP bar length on the flexural behavior of hybrid concrete beams strengthened with NSM bars
  98. Exploring the impact of parameters on flow boiling heat transfer in microchannels and coated microtubes: A comprehensive review
  99. Crumb rubber modification for enhanced rutting resistance in asphalt mixtures
  100. Special Issue: AESMT-6
  101. Design of a new sorting colors system based on PLC, TIA portal, and factory I/O programs
  102. Forecasting empirical formula for suspended sediment load prediction at upstream of Al-Kufa barrage, Kufa City, Iraq
  103. Optimization and characterization of sustainable geopolymer mortars based on palygorskite clay, water glass, and sodium hydroxide
  104. Sediment transport modelling upstream of Al Kufa Barrage
  105. Study of energy loss, range, and stopping time for proton in germanium and copper materials
  106. Effect of internal and external recycle ratios on the nutrient removal efficiency of anaerobic/anoxic/oxic (VIP) wastewater treatment plant
  107. Enhancing structural behaviour of polypropylene fibre concrete columns longitudinally reinforced with fibreglass bars
  108. Sustainable road paving: Enhancing concrete paver blocks with zeolite-enhanced cement
  109. Evaluation of the operational performance of Karbala waste water treatment plant under variable flow using GPS-X model
  110. Design and simulation of photonic crystal fiber for highly sensitive chemical sensing applications
  111. Optimization and design of a new column sequencing for crude oil distillation at Basrah refinery
  112. Inductive 3D numerical modelling of the tibia bone using MRI to examine von Mises stress and overall deformation
  113. An image encryption method based on modified elliptic curve Diffie-Hellman key exchange protocol and Hill Cipher
  114. Experimental investigation of generating superheated steam using a parabolic dish with a cylindrical cavity receiver: A case study
  115. Effect of surface roughness on the interface behavior of clayey soils
  116. Investigated of the optical properties for SiO2 by using Lorentz model
  117. Measurements of induced vibrations due to steel pipe pile driving in Al-Fao soil: Effect of partial end closure
  118. Experimental and numerical studies of ballistic resistance of hybrid sandwich composite body armor
  119. Evaluation of clay layer presence on shallow foundation settlement in dry sand under an earthquake
  120. Optimal design of mechanical performances of asphalt mixtures comprising nano-clay additives
  121. Advancing seismic performance: Isolators, TMDs, and multi-level strategies in reinforced concrete buildings
  122. Predicted evaporation in Basrah using artificial neural networks
  123. Energy management system for a small town to enhance quality of life
  124. Numerical study on entropy minimization in pipes with helical airfoil and CuO nanoparticle integration
  125. Equations and methodologies of inlet drainage system discharge coefficients: A review
  126. Thermal buckling analysis for hybrid and composite laminated plate by using new displacement function
  127. Investigation into the mechanical and thermal properties of lightweight mortar using commercial beads or recycled expanded polystyrene
  128. Experimental and theoretical analysis of single-jet column and concrete column using double-jet grouting technique applied at Al-Rashdia site
  129. The impact of incorporating waste materials on the mechanical and physical characteristics of tile adhesive materials
  130. Seismic resilience: Innovations in structural engineering for earthquake-prone areas
  131. Automatic human identification using fingerprint images based on Gabor filter and SIFT features fusion
  132. Performance of GRKM-method for solving classes of ordinary and partial differential equations of sixth-orders
  133. Visible light-boosted photodegradation activity of Ag–AgVO3/Zn0.5Mn0.5Fe2O4 supported heterojunctions for effective degradation of organic contaminates
  134. Production of sustainable concrete with treated cement kiln dust and iron slag waste aggregate
  135. Key effects on the structural behavior of fiber-reinforced lightweight concrete-ribbed slabs: A review
  136. A comparative analysis of the energy dissipation efficiency of various piano key weir types
  137. Special Issue: Transport 2022 - Part II
  138. Variability in road surface temperature in urban road network – A case study making use of mobile measurements
  139. Special Issue: BCEE5-2023
  140. Evaluation of reclaimed asphalt mixtures rejuvenated with waste engine oil to resist rutting deformation
  141. Assessment of potential resistance to moisture damage and fatigue cracks of asphalt mixture modified with ground granulated blast furnace slag
  142. Investigating seismic response in adjacent structures: A study on the impact of buildings’ orientation and distance considering soil–structure interaction
  143. Improvement of porosity of mortar using polyethylene glycol pre-polymer-impregnated mortar
  144. Three-dimensional analysis of steel beam-column bolted connections
  145. Assessment of agricultural drought in Iraq employing Landsat and MODIS imagery
  146. Performance evaluation of grouted porous asphalt concrete
  147. Optimization of local modified metakaolin-based geopolymer concrete by Taguchi method
  148. Effect of waste tire products on some characteristics of roller-compacted concrete
  149. Studying the lateral displacement of retaining wall supporting sandy soil under dynamic loads
  150. Seismic performance evaluation of concrete buttress dram (Dynamic linear analysis)
  151. Behavior of soil reinforced with micropiles
  152. Possibility of production high strength lightweight concrete containing organic waste aggregate and recycled steel fibers
  153. An investigation of self-sensing and mechanical properties of smart engineered cementitious composites reinforced with functional materials
  154. Forecasting changes in precipitation and temperatures of a regional watershed in Northern Iraq using LARS-WG model
  155. Experimental investigation of dynamic soil properties for modeling energy-absorbing layers
  156. Numerical investigation of the effect of longitudinal steel reinforcement ratio on the ductility of concrete beams
  157. An experimental study on the tensile properties of reinforced asphalt pavement
  158. Self-sensing behavior of hot asphalt mixture with steel fiber-based additive
  159. Behavior of ultra-high-performance concrete deep beams reinforced by basalt fibers
  160. Optimizing asphalt binder performance with various PET types
  161. Investigation of the hydraulic characteristics and homogeneity of the microstructure of the air voids in the sustainable rigid pavement
  162. Enhanced biogas production from municipal solid waste via digestion with cow manure: A case study
  163. Special Issue: AESMT-7 - Part I
  164. Preparation and investigation of cobalt nanoparticles by laser ablation: Structure, linear, and nonlinear optical properties
  165. Seismic analysis of RC building with plan irregularity in Baghdad/Iraq to obtain the optimal behavior
  166. The effect of urban environment on large-scale path loss model’s main parameters for mmWave 5G mobile network in Iraq
  167. Formatting a questionnaire for the quality control of river bank roads
  168. Vibration suppression of smart composite beam using model predictive controller
  169. Machine learning-based compressive strength estimation in nanomaterial-modified lightweight concrete
  170. In-depth analysis of critical factors affecting Iraqi construction projects performance
  171. Behavior of container berth structure under the influence of environmental and operational loads
  172. Energy absorption and impact response of ballistic resistance laminate
  173. Effect of water-absorbent polymer balls in internal curing on punching shear behavior of bubble slabs
  174. Effect of surface roughness on interface shear strength parameters of sandy soils
  175. Evaluating the interaction for embedded H-steel section in normal concrete under monotonic and repeated loads
  176. Estimation of the settlement of pile head using ANN and multivariate linear regression based on the results of load transfer method
  177. Enhancing communication: Deep learning for Arabic sign language translation
  178. A review of recent studies of both heat pipe and evaporative cooling in passive heat recovery
  179. Effect of nano-silica on the mechanical properties of LWC
  180. An experimental study of some mechanical properties and absorption for polymer-modified cement mortar modified with superplasticizer
  181. Digital beamforming enhancement with LSTM-based deep learning for millimeter wave transmission
  182. Developing an efficient planning process for heritage buildings maintenance in Iraq
  183. Design and optimization of two-stage controller for three-phase multi-converter/multi-machine electric vehicle
  184. Evaluation of microstructure and mechanical properties of Al1050/Al2O3/Gr composite processed by forming operation ECAP
  185. Calculations of mass stopping power and range of protons in organic compounds (CH3OH, CH2O, and CO2) at energy range of 0.01–1,000 MeV
  186. Investigation of in vitro behavior of composite coating hydroxyapatite-nano silver on 316L stainless steel substrate by electrophoretic technic for biomedical tools
  187. A review: Enhancing tribological properties of journal bearings composite materials
  188. Improvements in the randomness and security of digital currency using the photon sponge hash function through Maiorana–McFarland S-box replacement
  189. Design a new scheme for image security using a deep learning technique of hierarchical parameters
  190. Special Issue: ICES 2023
  191. Comparative geotechnical analysis for ultimate bearing capacity of precast concrete piles using cone resistance measurements
  192. Visualizing sustainable rainwater harvesting: A case study of Karbala Province
  193. Geogrid reinforcement for improving bearing capacity and stability of square foundations
  194. Evaluation of the effluent concentrations of Karbala wastewater treatment plant using reliability analysis
  195. Adsorbent made with inexpensive, local resources
  196. Effect of drain pipes on seepage and slope stability through a zoned earth dam
  197. Sediment accumulation in an 8 inch sewer pipe for a sample of various particles obtained from the streets of Karbala city, Iraq
  198. Special Issue: IETAS 2024 - Part I
  199. Analyzing the impact of transfer learning on explanation accuracy in deep learning-based ECG recognition systems
  200. Effect of scale factor on the dynamic response of frame foundations
  201. Improving multi-object detection and tracking with deep learning, DeepSORT, and frame cancellation techniques
  202. The impact of using prestressed CFRP bars on the development of flexural strength
  203. Assessment of surface hardness and impact strength of denture base resins reinforced with silver–titanium dioxide and silver–zirconium dioxide nanoparticles: In vitro study
  204. A data augmentation approach to enhance breast cancer detection using generative adversarial and artificial neural networks
  205. Modification of the 5D Lorenz chaotic map with fuzzy numbers for video encryption in cloud computing
  206. Special Issue: 51st KKBN - Part I
  207. Evaluation of static bending caused damage of glass-fiber composite structure using terahertz inspection
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