• Steel and Composite Structures
• /
• v.43 no.3
• /
• pp.389-401
• /
• 2022

This study investigated the flowability and mechanical properties of cost-effective steel fiber reinforced ultra-high performance concrete (UHPC) by using locally available materials for field-cast application. To examine the effect of mixture constituents, five mixtures with different fractions of silica fume, silica powder, ground granulated blast furnace slag (GGBS), silica sand, and crushed natural sand were proportionally prepared. Comprehensive experiments for different mixture designs were conducted to evaluate the fresh- and hardened-state properties of self-consolidating UHPC. The results showed that the proposed UHPC had similar mechanical properties compared with conventional UHPC while the flow retention over time was enhanced so that the field-cast application seemed appropriately cost-effective. The self-consolidating UHPC with high flowability and low viscosity takes less total mixing time than conventional UHPC up to 6.7 times. The X-ray computed tomographic imaging was performed to investigate the steel fiber distribution inside the UHPC by visualizing the spatial distribution of steel fibers well. Finally, the tensile stress-strain curve for the proposed UHPC was proposed for the implementation to the structural analysis and design.

• Structural Engineering and Mechanics
• /
• v.81 no.6
• /
• pp.769-780
• /
• 2022

This paper studies the dynamic behavior of laminated composite circular cylindrical shells interacting with a fluid. The mathematical formulation of the dynamic problem for an elastic body is developed based on the variational principle of virtual displacements and the relations of linear elasticity theory. The behavior of an ideal compressible fluid is described by the potential theory, the equations of which together with boundary conditions are transformed to a weak form. The hydrodynamic pressure exerted by the fluid on the internal surface of the shell is calculated according to the linearized Bernoulli equation. The numerical implementation of the mathematical formulation has been done using the semi-analytical finite element method. The influence of the ply angle and lay-up configurations of laminated composites on the natural vibration frequencies and the hydroelastic stability boundary have been analyzed for shells with different geometrical dimensions and under different kinematic boundary conditions set at their edges. It has been found that the optimal value of the ply angle depends on the level of filling of the shell with a fluid. The obtained results support the view that by choosing the optimal configuration of the layered composite material it is possible to change upwards or downwards the frequency and mode shape, as well as the critical velocity for stability loss over a wide range.

• Steel and Composite Structures
• /
• v.43 no.3
• /
• pp.353-362
• /
• 2022

Bridge fire hazard has become a growing concern over the last decade due to the rapid increase of ground transportation of hazardous materials and resulting fire incidents. The lack of fire safety provisions in steel bridges can be a significant issue owing steel thermal properties that lead to fast degradation of steel properties at elevated temperatures. Alternatively, the development of composite action between steel girders and concrete decks can increase the fire resistance of steel bridges and meet fire safety requirements in some applications. This paper reviews the fire problem in steel bridges and the fire behavior of composite steel-concrete bridge girders. A numerical model is developed to trace the fire response of a typical bridge girder and is validated using measurements from fire tests. The selected bridge girder is composed by a hot rolled steel section strengthened with bearing stiffeners at midspan and supports. A concrete slab sitting on the top of the girder is connected to the slab through shear studs to provide full composite action. The validated numerical model was used to investigate the fire resistance of real scale bridge girders and the effect of the composite action under different scenarios (standard and hydrocarbon fires). Results showed that composite action can significantly increase the fire resistance of steel bridge girders. Besides, fire severity played an important role in the fire behavior of composite girders and both factors should be taken into consideration in the design of steel bridges for fire safety.

• Structural Engineering and Mechanics
• /
• v.82 no.1
• /
• pp.55-67
• /
• 2022

The precast reinforced concrete frame system is a method for industrialization of construction. However, the seismic performance factor of this structural system is not explicitly clarified in some existing building codes. In this paper, the seismic performance factor for the existing precast concrete building frame systems with cast-in-situ reinforced shear walls were evaluated. Nonlinear behavior of the precast beam-column joints and cast-in-situ reinforced shear walls were considered in the modeling of the structures. The ATC-19's coefficient method was used for calculating the seismic performance factor and the FEMA P-695's approach was adopted for evaluating the accuracy of the computed seismic performance factor. The results showed that the over-strength factor varies from 2 to 2.63 and the seismic performance factor (R factor) varies from 5.1 to 8.95 concerning the height of the structure. Also, it was proved that all of the examined buildings have adequate safety against the collapse at the MCE level of earthquake, so the validity of R factors was confirmed. The obtained incremental dynamic analysis (IDA) results indicated that the minimum adjusted collapse margin ratio (ACMR) of the precast buildings representing the seismic vulnerability of the structures approximately equaled to 2.7, and pass the requirements of FEMA P-695.

• Structural Engineering and Mechanics
• /
• v.82 no.1
• /
• pp.17-30
• /
• 2022

The use of composite patches for the reduction of stresses at the level of the damaged zone in aeronautical structures has experienced rapid expansion given its advantages over conventional mechanical processes (riveting, bolting, etc.). Initially, The research axes in this field were aimed at choosing suitable mechanical properties for the composite and the adhesive, then to optimize the shape of the composite patch in order to ensure good load transfer and avoid having a debonding at the level of the edges essentially for the case of a repair by single side where the bending moment is present due to the non-symmetry of the structure. Our work falls within this context; the objective is to analyze by the finite element method the fracture behavior of a damaged plate repaired by composite patch. Stress reduction at the edge is accomplished by creating a variable angle chamfer on the composite patch. The effects of the crack length, the laminate sequence and the nature of the patch as well as the use of a hybrid patch were investigated. The results show clearly that a beveled patch reduces the stress concentrations in the damaged area and even at its edges. The hybrid patch also ensures good durability of the repair by optimizing its stacking sequence and the location of the different layers according to the fibers orientations.

• Smart Structures and Systems
• /
• v.29 no.4
• /
• pp.561-576
• /
• 2022

Heritage timber structures represent the history and culture of a nation. These structures have been inherited from previous generations; however, they inevitably exhibit deterioration over time, potentially leading to structural deficiencies. Structural Health Monitoring (SHM) offers the potential to assess operational anomalies, deterioration, and damage through processing and analysis of data collected from transducers and sensors mounted on the structure. This paper reports on the design and implementation of a long-term SHM system on the Feiyun Wooden Pavilion in China, a three-story timber building built more than 500 years ago. The principles and features of the design and implementation of SHM systems for heritage timber buildings are systematically discussed. In total, 104 sensors of 6 different types are deployed on the structure to monitor the environmental effects and structural responses, including air temperature and humidity, wind speed and direction, structural temperatures, strain, inclination, and acceleration. In addition, integrated data acquisition and transmission subsystem using a newly developed software platform are implemented. Selected preliminary statistical and correlation analysis using one year of monitoring data are presented to demonstrate the condition assessment capability of the system based on the monitoring data.

• Structural Monitoring and Maintenance
• /
• v.9 no.1
• /
• pp.1-27
• /
• 2022

Extensive laboratory tests were conducted to investigate the effect of load amplitude, geogrid position, and number of geogrid layers, thickness of ballast layer and clay stiffness on behavior of reinforced ballast layer and induced strains in geogrid. A half full-scale railway was constructed for carrying out the tests, the model consists of two rails 800 mm in length with three wooden sleepers (900 mm × 10 mm × 10 mm). The ballast was overlying 500 mm thickness clay in two states, soft and stiff state. Laboratory tests were conducted to investigate the response of the ballast and the clay layers where the ballast was reinforced by a geogrid. Settlement in ballast and clay, soil pressure and pore water pressure induced in the clay were measured in reinforced and unreinforced ballast cases. It was concluded that the effect of frequency on the settlement ratio is almost constant after 500 cycles. This is due to that the total settlement after 500 cycles, almost reached its peak value, which means that the ballast particles become very close to each other, so the frequency is less effective for high contact particles forces. The average maximum vertical stress and pore water pressure increased with frequency.

• Smart Structures and Systems
• /
• v.29 no.1
• /
• pp.207-220
• /
• 2022

Structural health monitoring (SHM) plays a vital role in the maintenance and operation of constructions. In recent years, autonomous inspection has received considerable attention because conventional monitoring methods are inefficient and expensive to some extent. To develop autonomous inspection, a potential approach of crack identification is needed to locate defects. Therefore, this study exploits two deep learning-based segmentation models, DeepLabv3+ and Mask R-CNN, for crack segmentation because these two segmentation models can outperform other similar models on public datasets. Additionally, impacts of label quality on model performance are explored to obtain an empirical guideline on the preparation of image datasets. The influence of image cropping and label refining are also investigated, and different strategies are applied to the dataset, resulting in six alternated datasets. By conducting experiments with these datasets, the highest mean Intersection-over-Union (mIoU), 75%, is achieved by Mask R-CNN. The rise in the percentage of annotations by image cropping improves model performance while the label refining has opposite effects on the two models. As the label refining results in fewer error annotations of cracks, this modification enhances the performance of DeepLabv3+. Instead, the performance of Mask R-CNN decreases because fragmented annotations may mistake an instance as multiple instances. To sum up, both DeepLabv3+ and Mask R-CNN are capable of crack identification, and an empirical guideline on the data preparation is presented to strengthen identification successfulness via image cropping and label refining.

• Smart Structures and Systems
• /
• v.29 no.1
• /
• pp.153-166
• /
• 2022

Identifying fine cracks in steel bridge facilities is a challenging task of structural health monitoring (SHM). This study proposed an end-to-end crack image segmentation framework based on a one-step Convolutional Neural Network (CNN) for pixel-level object recognition with high accuracy. To particularly address the challenges arising from small object detection in complex background, efforts were made in loss function selection aiming at sample imbalance and module modification in order to improve the generalization ability on complicated images. Specifically, loss functions were compared among alternatives including the Binary Cross Entropy (BCE), Focal, Tversky and Dice loss, with the last three specialized for biased sample distribution. Structural modifications with dilated convolution, Spatial Pyramid Pooling (SPP) and Feature Pyramid Network (FPN) were also performed to form a new backbone termed CrackDet. Models of various loss functions and feature extraction modules were trained on crack images and tested on full-scale images collected on steel box girders. The CNN model incorporated the classic U-Net as its backbone, and Dice loss as its loss function achieved the highest mean Intersection-over-Union (mIoU) of 0.7571 on full-scale pictures. In contrast, the best performance on cropped crack images was achieved by integrating CrackDet with Dice loss at a mIoU of 0.7670.

• Advances in materials Research
• /
• v.11 no.1
• /
• pp.75-90
• /
• 2022

In this paper, functionality of soft computing algorithms such as Group method of data handling (GMDH), Random forest (RF), Random tree (RT), Linear regression (LR), M5P, and artificial neural network (ANN) have been looked out to predict the compressive strength of concrete mixed with marble powder. Assessment of result suggests that, the overall performance of ANN based model gives preferable results over the different applied algorithms for the estimate of compressive strength of concrete. The results of coefficient of correlation were maximum in ANN model (0.9139) accompanied through RT with coefficient of correlation (CC) value 0.8241 and minimum root mean square error (RMSE) value of ANN (4.5611) followed by RT with RMSE (5.4246). Similarly, other evaluating parameters like, Willmott's index and Nash-sutcliffe coefficient value of ANN was 0.9458 and 0.7502 followed by RT model (0.8763 and 0.6628). The end result showed that, for both subsets i.e., training and testing subset, ANN has the potential to estimate the compressive strength of concrete. Also, the results of sensitivity suggest that the water-cement ratio has a massive impact in estimating the compressive strength of concrete with marble powder with ANN based model in evaluation with the different parameters for this data set.