Startseite Effect of surface roughness on the interface behavior of clayey soils
Artikel Open Access

Effect of surface roughness on the interface behavior of clayey soils

  • Hala K. Kadhim EMAIL logo und Mohamad Alyounis
Veröffentlicht/Copyright: 8. März 2024
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Abstract

This study investigates the effect of surface roughness on the interface behavior between clayey soils and structural materials, aiming to determine the necessary parameters for soil-structural interaction. The research site, located in one of Iraq’s seismically active regions, was selected for its significance. Experimental measurements were conducted using the SRT-6210 Digital Surface Roughness Tester to assess the roughness characteristics of steel and concrete samples. Four distinct roughness parameters were measured, and their correlation with shear parameters was analyzed. The shear behavior of clay-steel and clay-concrete interfaces was successfully described using the average roughness parameter (Ra), which exhibited the strongest correlation with shear parameters. Direct shear box and interface shear box tests were employed to identify soil’s shear strength parameters and evaluate interface shear strength parameters. The experimental findings highlight the significant influence of surface roughness on the shear strength parameters of clay-steel and clay-concrete interfaces. The interface shear strength, friction angle, and adhesion exhibited an increasing trend with roughness. Notably, shear strength increased by approximately 29.76% when concrete sample roughness was below 20 μm and by 32.8% when steel sample roughness was below 30 μm. Moreover, increasing surface roughness improved the interface friction angle of clay-steel and clay-concrete samples by about 37.95 and 36.3%, respectively. Additionally, an increase in roughness led to a rise in the adhesion of concrete and steel samples by approximately 26.24 and 32%, respectively. These findings emphasize the significance of surface roughness in optimizing the interface behavior between clayey soils and structural materials. The results have important implications for enhancing the design and performance of soil-structural systems.

1 Introduction

Understanding the behavior of interfaces between clayey soils and structural materials, such as steel or concrete, is crucial for optimizing the design and performance of soil structural systems. Surface roughness is a crucial factor in determining interface characteristics, including friction angles and shear behavior. Several studies have been carried out to explore the relationship between roughness parameters, interface friction angles, and shear behavior at the interface.

Ward provided a comprehensive summary of 23 international standard roughness measurements tailored to specific applications. These standardized measurements have facilitated the characterization of soil-steel interfaces and contributed to the development of normalized roughness analysis and surface topography calculations [1]. Uesugi and Kishida conducted laboratory tests to examine the frictional resistance between mild steel and dry sand. They introduced the concept of normalized roughness to assess the relative roughness of the sand-steel interface, which showed a strong correlation with the coefficient of friction at yield [2]. Studies have also explored the behavior of soil-structural interfaces under various conditions. Uesugi and Kishida observed sliding at the sand-steel interface prior to the peak in frictional resistance and demonstrated the influence of particle displacement on the friction test results [3]. Gadelmawla et al. provided definitions and mathematical formulas for multiple roughness parameters, enabling the calculation of 3D surface topography [4]. Researchers investigated the friction angles between soil and wall materials in direct shear tests, revealing variations depending on the contact surface roughness [5,6]. The mechanical behavior and shear strength of soil must be determined by different ways such as the statistical variation and the correlation models which were studied by Mohammed and Mahmood for gypsum rock soil [7].

The behavior of interfaces between Ottawa sand and steel samples with different roughness levels was examined by Alyounis and Desai [8], who found that higher surface roughness mobilized higher peak strength. Wang et al. investigated the influence of grouting volume on the shear characteristics of cohesive soil–concrete interfaces, observing an increase in interfacial apparent cohesion with higher grouting volume and roughness [9]. Li et al. studied the effects of soil water content, interface roughness, and normal stress on the shear mechanical behavior of silt–steel interfaces, revealing higher shear strength for rough interfaces compared to direct shear tests on silt [10]. However, previous studies have not adequately explored the effects of surface roughness on interface behavior under challenging conditions.

In this study, we focus on evaluating the surface roughness and its impact on the behavior of interfaces between clayey soils and steel or concrete. The selected research site, located in a seismically active region in Iraq [11,12], provides a relevant setting to investigate the interface behavior under challenging conditions. Experimental testing will measure the roughness characteristics of steel and concrete samples using the SRT-6210 Digital Surface Roughness Tester. Shear strength parameters will be determined through direct shear box tests and interface shear box tests. The results of this study will contribute to a better understanding of the interface behavior between clayey soils and structural materials and provide valuable insights for design and construction practices.

2 Materials and methods

2.1 Amplitude parameters of surface roughness

Surface topography, which represents the surface profile, is quantified as surface roughness. It has a significant influence on the behavior of interface shear in geologic materials. On the other side, roughness might promote adhesion. In the context of interface behavior, the following parameters are commonly used to describe interface roughness:

2.1.1 Average roughness (R a)

The average absolute divergence from the mean line for a single sample length, as shown in (Figure 1) and equation (1), can be used to measure roughness irregularities. This parameter gives a good overview of the range of potential heights and is easy to define and measure [4].

(1) R a = i = 1 n y i n ,

where y i is the height of each peak and n is the number of peaks.

Figure 1 
                     Average roughness (R
                        a) [4].
Figure 1

Average roughness (R a) [4].

2.1.2 Root mean square roughness (R q)

The Root mean square roughness measures the standard deviation of the height of asperities above and below the reference plane, as described by equations (2) and (3). This parameter is more responsive to significant deviations from the mean line than the arithmetic average height (R a) [4].

(2) R q = 1 l 0 l { y ( x ) } 2 d x ,

or

(3) R q = 1 n i = 1 n y i 2 ,

where R q is the root mean square roughness. y i = y ( x ) is the height of each peak. l is the length measured by the Digital Surface Roughness Tester. n is thenumber of peaks.

2.1.3 Maximum height of the profile (R t or R max)

This parameter exhibits high sensitivity to high peaks or deep scratches. Rt is the vertical distance along the profile’s assessment length between its highest peak and the lowest valley [4]. The parameter is depicted in Figure 2 and equation (4).

(4) R t = R p + R v = R p 3 + R v 4 ,

where R p is the maximum height of peaks. R v is the maximum depth of valleys.

Figure 2 
                     The diagram of the parameter, R
                        t (R
                        max) [4].
Figure 2

The diagram of the parameter, R t (R max) [4].

2.1.4 Ten-point height (Rz)

This parameter is more sensitive to occasional high peaks or deep valleys than R a, which is shown in Figure 3. It is defined by the International ISO system (ISO 13565-1) as the difference in height between the average of the five highest peaks and the five lowest valleys along the assessment length of the profile. This measurement indicates the fluctuation in surface topography and helps quantify the extent of elevation changes within the profile [4].

(5) R Z ( ISO ) = 1 n i = 1 n p i i = 1 n v i ,

where n is the total number of measurements taken along the length.

Figure 3 
                     The diagram of the ten-point height parameter R
                        z(ISO) [4].
Figure 3

The diagram of the ten-point height parameter R z(ISO) [4].

2.2 Interface testing devices

To study the interface behavior between clayey soil and structural members, which are usually made of concrete or steel, shear tests were carried out between soil and samples of steel and concrete with different roughness. Ten samples of steel and concrete samples were used in this study. Steel samples were sprayed with water regularly to corrode their surfaces until the desired roughness was attained. The smooth surface of concrete samples was attained using a smooth contact surface when casting. Jam paper of varied roughness was put on the cast surface for samples to get the necessary roughness for the remaining samples.

The roughness profiles of the steel and concrete were measured using Contact Profilometer Digital Surface Roughness Tester SRT-6210, as shown in Figure 4. The roughness profiles directly measured by the profilometer with a cutoff length of 2.5 mm. Table 1 summarizes the measured roughness parameters (R a, R z, R q, and R t) for the steel and concrete samples used in this study where

Figure 4 
                  Digital Surface Roughness Tester SRT-6210.
Figure 4

Digital Surface Roughness Tester SRT-6210.

Table 1

Measured roughness parameters (R a, R z, R q, and R t) for steel and concrete samples

Samples R a (µm) R z (µm) R q (µm) R t (µm)
Steel samples
1 1.173 14.65 1.3 12.5
2 3.81 15 3.2 20
3 7.675 76 7 80
4 12.96 97 11 95
5 27.51 109 25.3 113
Concrete samples
1 3.151 48 5.2 51
2 6.871 58 7.95 53.64
3 11.05 69.4 13.2 79
4 15.19 80.5 18.78 85
5 19.01 95 20.3 93

R a is the average roughness parameter, R z is the ten-point height, R q is the Root mean square roughness, and R t is the maximum height of the profile.

The steel and concrete sample surfaces are shown in Figures 5 and 6, respectively. The device is calibrated using a standard roughness glass piece shown in Figure 7 before starting the measurements.

Figure 5 
                  Roughness of the steel samples. (a) Steel samples in two dimensions and (b) steel samples in three dimensions.
Figure 5

Roughness of the steel samples. (a) Steel samples in two dimensions and (b) steel samples in three dimensions.

Figure 6 
                  Roughness of the concrete samples. (a) Concrete samples in two dimensions and (b) Concrete samples in three dimensions.
Figure 6

Roughness of the concrete samples. (a) Concrete samples in two dimensions and (b) Concrete samples in three dimensions.

Figure 7 
                  Precision reference standard.
Figure 7

Precision reference standard.

2.3 Physical characterization of the soils

Soil properties in any location must be determined either by laboratory tests or by correlation between different physical and mechanical properties of soil [13,14]. The soil samples are classified according to the Unified soil Classification system as silty clay and sometimes with sand with low plasticity (CL). It presents a Liquid Limit of 45 and a Plastic Limit of 22. The soil has a dry unit weight of 17.3 kN/m3, a bulk unit weight of 19.7 kN/m3, and a natural moisture content of 14% [15].

2.3.1 Shear strength parameters of clay soil

Shear tests were used to calculate the shear strength of the clayey soil. The direct shear box test was conducted in the Geotechnical Engineering Laboratory at the University of Thi-Qar. The direct shear device is shown in Figure 8. The upper and lower shear boxes are square in cross-section with dimensions of 60 mm × 60 mm × 30 mm (length × width × height). The soil samples would be remolded at the natural moisture content (14%). During the test, each sample was subjected to three levels of normal stress: 5.45, 10.9, and 21.8 kPa. The horizontal displacement after applying normal loads on the hanger was measured. Results are given in Table 2 and (Figure 9). From the direct shear box test, the value of cohesion is 17.7 kPa, and the angle of internal friction is 21.8° .

Figure 8 
                     Shear box test.
Figure 8

Shear box test.

Table 2

Direct shear box test for clay soil

Shear stress τ f (kPa) Normal stress (kPa)
43.4 5.45
54.9 10.9
106.6 21.8
Figure 9 
                     Direct shear box test for clayey soil: (a) shear stress vs horizontal displacement and (b) normal stress vs shear stress.
Figure 9

Direct shear box test for clayey soil: (a) shear stress vs horizontal displacement and (b) normal stress vs shear stress.

2.3.2 Shear strength parameters of interfaces

Ten shear box tests were carried out: five samples for the clay-steel interface and five for the clay-concrete interface. The main variable among the tests is the surface roughness of the structural material (steel or concrete). Steel (or concrete) sample was placed at the lower part of the shear box so that the upper half of the box would move freely over the lower half, and then the upper half of the box is filled with a soil, as shown in Figure 10. The peak shear stress vs the corresponding normal stress curves were plotted for each test to determine the interface shear parameters.

Figure 10 
                     Preparation of the soil sample (interface test).
Figure 10

Preparation of the soil sample (interface test).

3 Results and discussion

3.1 Interface friction angle and interface adhesion

Figures 11 and 12 demonstrate the variation in adhesion (Ca) and interface friction angle ( δ ) , respectively, with the roughness of concrete samples. Figures 13 and 14 show the variation in adhesion (Ca) and interface friction angle ( δ ) with the steel samples’ roughness. Table 3 summarizes the correlation factor (r 2) values for different roughness parameters (R a, R z, R q, and R t) for clay-steel and clay-concrete interfaces. The average roughness parameter (R a) is used in this study since it offers the best correlation; its fitting correlation coefficient ranges from 0.9577 to 0.9837 as shown in the figures. The data presented in Figures 11 and 13 demonstrate a positive correlation between roughness and interface adhesion. Specifically, an increase in roughness resulted in a corresponding increase in adhesion. Notably, the adhesion values for the concrete interface exhibited an overall increase of 26.24%, while the steel samples experienced a 32% increase in adhesion.

Figure 11 
                  Variation in adhesion factor (Ca) with roughness parameters for clay–concrete interface. (a) R
                     a, (b) R
                     z, (c) R
                     q, and (d) R
                     t.
Figure 11

Variation in adhesion factor (Ca) with roughness parameters for clay–concrete interface. (a) R a, (b) R z, (c) R q, and (d) R t.

Figure 12 
                  Variation in interface friction angle 
                        
                           
                           
                              (
                              δ
                              )
                           
                           \left(\delta )
                        
                      with roughness parameters for clay–concrete interface. (a) R
                     a, (b) R
                     z, (c) R
                     q, and (d) R
                     t.
Figure 12

Variation in interface friction angle ( δ ) with roughness parameters for clay–concrete interface. (a) R a, (b) R z, (c) R q, and (d) R t.

Figure 13 
                  Variation in Adhesion factor (Ca) with roughness parameters for clay–steel interface. (a) R
                     a, (b) R
                     z, (c) R
                     q, and (d): R
                     t.
Figure 13

Variation in Adhesion factor (Ca) with roughness parameters for clay–steel interface. (a) R a, (b) R z, (c) R q, and (d): R t.

Table 3

Correlation factor (r 2) for the relations between surface roughness parameters and interface parameters ( δ and Ca)

Roughness Steel samples Concrete samples
δ Ca δ Ca
Ra 0.9577 0.9621 0.9821 0.9837
Rz 0.8204 0.8136 0.9662 0.9861
Rq 0.9377 0.943 0.9622 0.954
Rt 0.8365 0.8307 0.9418 0.9613

According to the data presented in Figures 12 and 14, it can be observed that the interface friction angle demonstrated an upward trend as the roughness increased. Specifically, the concrete–clay interface experienced a 36.3% increase, while the steel–clay interface exhibited a 37.95% increase. Simultaneously, when comparing the physical and mechanical parameters of the soil, it becomes evident that both the interface adhesion and the interface friction angle have lower values compared with those observed in the soil.

Figure 14 
                  Variation in interface friction angle 
                        
                           
                           
                              (
                              δ
                              )
                           
                           \left(\delta )
                        
                      with roughness parameters for clay–steel interface. (a) R
                     a, (b) R
                     z, (c) R
                     q, and (d) R
                     t.
Figure 14

Variation in interface friction angle ( δ ) with roughness parameters for clay–steel interface. (a) R a, (b) R z, (c) R q, and (d) R t.

3.2 Interface shear strength

Based on the obtained test results, it is possible to construct the shear strength variation curve for the steel–soil and concrete–soil interfaces with varying degrees of roughness, illustrated in Figures 15 and 16, respectively. It is evident that, under constant normal stress conditions, the shear strength exhibited an upward trend corresponding to an increase in roughness.

Figure 15 
                  Relationship between shear strength of interface and roughness for steel samples.
Figure 15

Relationship between shear strength of interface and roughness for steel samples.

Figure 16 
                  Relationship between shear strength of interface and roughness for concrete samples.
Figure 16

Relationship between shear strength of interface and roughness for concrete samples.

When the normal stress σ equals 21.8 kPa, the interface shear strength increases from 18.3 to 23.75 kPa as the roughness increases from 3.151 to 19.01 µm. This corresponded to a relative increase in 29.76% for concrete samples. Similarly, the interface shear strength for steel samples increased from 18.11 to 24.36 kPa as the roughness increased from 1.173 to 27.51 µm, resulting in a relative increase of 32.8%. These findings suggest that the roughness of the clay–steel (or concrete) interface significantly impacts the shear strength.

Figures 17 and 18 depict the relation of shear strength and normal stress for concrete and steel samples, respectively, under the condition of equal roughness. It is obvious that under similar roughness conditions, the relationship involving shear strength with normal stress tends to be approximated as a linear progression. The frictional resistance observed at the interface between clay and concrete exhibited similarities to the shear strength of soil, as referred to by equation (6).

(6) q u = σ tan δ + Ca ,

where q is the ultimate friction resistance at the construction material steel– or concrete–soil interface, σ is the normal stress at the interface, δ is the friction angle at the construction material–soil interface, and Ca is the adhesion factor of the construction material–soil interface.

Figure 17 
                  Variation in shear strength with normal stress for different roughness (clay–concrete interface).
Figure 17

Variation in shear strength with normal stress for different roughness (clay–concrete interface).

Figure 18 
                  Variation in shear strength with normal stress for different roughness (clay–steel interface).
Figure 18

Variation in shear strength with normal stress for different roughness (clay–steel interface).

4 Discussion

In the experimental setup aimed at investigating the impact of roughness, the interface’s shear characteristics between silty clay and concrete or steel samples are primarily influenced by the roughness of the contact surface. The significance of this influence remains when the interface’s normal stress and moisture content are kept constant, as stated by Chen et al. [16]. The shear strength of the interface consists of two main elements: adhesion and frictional resistance, both of which are caused by interface slip. The friction angle is a key factor influencing the level of frictional resistance. Cohesion, which represents the cementation and presence of a water film that connects soil particles on a macroscopic level, plays a vital role in the adhesion of fine soil particles. In this experimental study, the soil sample consists of clay soil, which exhibits substantial cohesion, thereby significantly contributing to its shear strength. The shear strength of the interface under varying roughness conditions ranges from 18.3 to 23.75 kPa for concrete samples, while the interface adhesion ranges from 13.07 to 16.5 kPa.

Similarly, for steel samples, the shear strength of the interface ranges from 18.11 to 24.36 kPa, and the interface adhesion ranges from 12.8 to 16.9 kPa. It was observed that the shear strength increased by approximately 29.76% when the roughness of concrete samples was below 20 μm and by 32.8% when the roughness of steel samples was below 30 μm. This illustrates that increased surface roughness promotes improved shear strength between soil and structural materials.

As the degree of roughness increases, there is a gradual reduction in the contact area between construction materials, such as steel or concrete, and the soil. Consequently, the ratio of frictional resistance gradually increases, leading to a corresponding enlargement of the influence of shear strength. The frictional resistance at the interface results from the frictional interaction between the soil particles and the surfaces of the concrete or steel specimens. When the roughness of the interface increases, it primarily affects the friction within the soil particles near the interface and the interface surface. Additionally, the frictional resistance is directly related to the friction angle. In other words, increasing surface roughness improved the interface friction angle of clay-steel and clay-concrete samples by about 37.95 and 36.3%, respectively. This suggests that higher surface roughness enhances the frictional resistance at the interface. Additionally, an increase in roughness increased the adhesion of concrete and steel samples by approximately 26.24 and 32%, respectively. This demonstrates that greater surface roughness promotes better adhesion between the soil and structural materials.

Several factors influence the shear properties of the interface between clay and either concrete or steel. Factors such as applied stresses, soil type, the qualities of the contact surface (whether it is concrete or steel), and the roughness of the contact surface interface have been identified as important considerations.

These findings underscore the significance of surface roughness in optimizing the interface behavior between clayey soils and structural materials. They provide important implications for enhancing the design and performance of soil structural systems, contributing to safer and more resilient structures in seismically active regions. Moreover, the results align with the research conducted by Wang et al. [17]

5 Conclusion

This study investigated the effect of surface roughness on the interface behavior between clayey soils and structural materials, aiming to determine the necessary parameters for soil structural interaction. The experimental measurements using the SRT-6210 Digital Surface Roughness Tester provided valuable insights into the roughness characteristics of steel and concrete samples. The correlation analysis revealed that the average roughness parameter (Ra) exhibited the strongest correlation with shear parameters, making it a significant factor in describing the shear behavior of clay–steel and clay–concrete interfaces.

The direct shear box and interface shear box tests effectively identified the shear strength parameters of the soil and evaluated the interface shear strength parameters, respectively. The experimental findings highlighted the significant influence of surface roughness on the shear strength parameters of clay–steel and clay–concrete interfaces. The shear strength increased by approximately 29.76% when the concrete sample roughness was below 20 μm and 32.8% when the steel sample roughness was below 30 μm. Furthermore, increasing surface roughness improved the interface friction angle of clay–steel and clay–concrete samples by about 37.95 and 36.3%, respectively. An increase in roughness also led to a rise in the adhesion of concrete and steel samples by approximately 26.24 and 32%, respectively.

The findings of this study emphasize the significance of surface roughness in optimizing the interface behavior between clayey soils and structural materials. The results have important implications for enhancing the design and performance of soil structural systems. The identified correlations between roughness parameters and shear behavior provide valuable guidance for engineers and researchers involved in soil structural interaction studies. The understanding gained from this research can contribute to developing improved design guidelines and techniques for such systems.

While this study has provided valuable insights into the effect of surface roughness on interface behavior, it is important to acknowledge its limitations. The research was conducted at a specific site in one of Iraq’s seismically active regions, and the findings may not be directly applicable to other locations. Additionally, the study focused on clay–steel and clay–concrete interfaces, and further investigations are needed to explore the behavior of other soil structural material combinations. Future studies should also consider the influence of other factors, such as moisture content and compaction, on the interface behavior.

  1. Funding information: The manuscript was done depending on the personal effort of the authors, and there is no funding effort from any side or organization.

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

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

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Received: 2023-11-08
Revised: 2023-12-08
Accepted: 2023-12-22
Published Online: 2024-03-08

© 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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  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
Heruntergeladen am 18.9.2025 von https://www.degruyterbrill.com/document/doi/10.1515/eng-2022-0578/html
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