• Steel and Composite Structures
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• v.39 no.2
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• pp.159-170
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• 2021

Concrete filled steel tubes (CFSTs) are extensively used in a variety of structures due to their structural and economic advantages over other types of structures. Considerable research has been conducted with regards to their short-term behaviour, and very limited studies have focused on their long-term behaviour. In this study, a series of tests were carried out on high strength squat (short) CFSTs and concrete cylinders under controlled conditions of temperature and humidity to better understand their time dependent behaviour. A number of parameters were investigated including the influence of steel and concrete bond, confinement, level of sustained load and sizes of specimens. The results revealed that creep strains increased by more than 40% if there was no bonding between steel tube and concrete core. As expected, creep and shrinkage of concrete inside a steel tube were significantly less than those developed in exposed concrete. At the end of a creep period of six months, all the specimens were tested to failure to observe the influence of sustained loads on the ultimate strength. It was found that creep does not have a major effect on the strength of short CFSTs in the specific experimental study conducted here, which was less than 2.5%.

• Steel and Composite Structures
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• v.29 no.6
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• pp.703-716
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• 2018

Rapid detection of damages in civil engineering structures, in order to assess their possible disorders and as a result produce competent decision making, are crucial to ensure their health and ultimately enhance the level of public safety. In traditional intelligent health monitoring methods, the features are manually extracted depending on prior knowledge and diagnostic expertise. Inspired by the idea of unsupervised feature learning that uses artificial intelligence techniques to learn features from raw data, a two-stage learning method is proposed here for intelligent health monitoring of civil engineering structures. In the first stage, $Nystr{\ddot{o}}m$ method is used for automatic feature extraction from structural vibration signals. In the second stage, Moving Kernel Principal Component Analysis (MKPCA) is employed to classify the health conditions based on the extracted features. In this paper, KPCA has been implemented in a new form as Moving KPCA for effectively segmenting large data and for determining the changes, as data are continuously collected. Numerical results revealed that the proposed health monitoring system has a satisfactory performance for detecting the damage scenarios of a three-story frame aluminum structure. Furthermore, the enhanced version of KPCA methods exhibited a significant improvement in sensitivity, accuracy, and effectiveness over conventional methods.

• Steel and Composite Structures
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• v.38 no.5
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• pp.599-615
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• 2021

The ultimate strength behaviour of sandwich composite beams with J-hooks and normal weight concrete (SCSSBJNs) are studied through two-point loading tests on ten full-scale SCSSBJNs. The test results show that the SCSSBJN with different parameters under two-point loads exhibits three types of failure modes, i.e., flexure, shear, and combined shear and flexure mode. SCSSBJN failed in different failure modes exhibits different load-deflection behaviours, and the main difference of these three types of behaviours exist in their last working stages. The influences of thickness of steel faceplate, shear span ratio, concrete core strength, and spacing of J-hooks on structural behaviours of SCSSBJN are discussed and analysed. These test results show that the failure mode of SCSSBJN was sensitive to the thickness of steel faceplate, shear span ratio, and concrete core strength. Theoretical models are developed to estimate the cracking, yielding, and ultimate bending resistance of SCSSBJN as well as its transverse cross-sectional shear resistance. The validations of predictions by these theoretical models proved that they are capable of estimating strengths of novel SCSSBJNs.

• Steel and Composite Structures
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• v.43 no.4
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• pp.501-509
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• 2022

Cracks are common defects in concrete structures. Thus far, crack inspection has been manually performed using the contact inspection method. This manpower-dependent method inevitably increases the cost and work hours. Various non-contact studies have been conducted to overcome such difficulties. However, previous studies have focused on developing a methodology for non-contact inspection or local quantitative detection of crack width or length on concrete surfaces. However, crack depth can affect the safety of concrete structures. In particular, although macrocrack depth is structurally fatal, it is difficult to find it with the existing method. Therefore, an experimental investigation based on non-contact infrared thermography and multivariate machine learning was performed in this study to estimate the hidden macrocrack depth. To consider practical applications for inspection, an experiment was conducted that considered the simulated piloting of an unmanned aerial vehicle equipped with infrared thermography equipment. The crack depths (10-60 mm) were comparatively evaluated using linear regression, gradient boosting, and random forest (AI regression methods).

• Smart Structures and Systems
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• v.31 no.2
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• pp.141-154
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• 2023

After an earthquake, information regarding potential damage to buildings close to the epicenter is very important during the initial emergency response. This study proposes the use of crowdsourced measured acceleration response data collected from smartphones located within buildings to perform system identification of building structures during earthquake excitations, and the feasibility of the proposed approach is studied. The principal advantage of using crowdsourced smartphone data is the potential to determine the condition of millions of buildings without incurring hardware, installation, and long-term maintenance costs. This study's goal is to assess the feasibility of identifying the lowest fundamental natural frequencies of buildings without knowing the orientations and precise locations of the crowds' smartphones in advance. Both input-output and output-only identification methods are used to identify the lowest fundamental natural frequencies of numerical finite element models of a real building structure. The effects of time synchronization and the orientation alignment between nearby smartphones on the identification results are discussed, and the proposed approach's performance is verified using large-scale shake table tests of a scaled steel building. The presented results illustrate the potential of using crowdsourced smartphone data with the proposed approach to identify the lowest fundamental natural frequencies of building structures, information that should be valuable in making emergency response decisions.

• Earthquakes and Structures
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• v.25 no.3
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• pp.161-175
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• 2023

High-damping rubber bearings (HDRB) are commonly used as seismic isolation devices to protect civil engineering structures from earthquakes. However, the nonlinear hysteresis characteristics of the HDRB, such as their dependence on material properties and hardening phenomena, make predicting their behavior during earthquakes difficult. This study proposes a hysteretic model that can accurately predicts the behavior of shear deformation considering the nonlinearity when designing the seismic isolation structures using HDR bearings. To model the hysteretic characteristics of the HDR, dynamic loading tests were performed by applying sinusoidal and random waves on scaled-down specimens. The test results show that the nonlinear characteristics of the HDR strongly correlate with the shear strain experienced in the past. Furthermore, when shear deformation occurred above a certain level, the hardening phenomenon, wherein the stiffness increased rapidly, was confirmed. Based on the experimental results, the dynamic characteristics of the HDR, equivalent stiffness, equivalent damping ratio, and strain energy were quantitatively evaluated and analyzed. In this study, an improved bilinear HDR model that can reproduce the dependence on shear deformation and hardening phenomena was developed. Additionally, by proposing an objective parameter-setting procedure based on the experimental results, the model was devised such that similar parameters could be set by anyone. Further, an actual dynamic analysis could be performed by modeling with minimal parameters. The proposed model corresponded with the experimental results and successfully reproduced the mechanical characteristics evaluated from experimental results within an error margin of 10%.

• Geomechanics and Engineering
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• v.32 no.1
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• pp.35-47
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• 2023

Deformability of road pavements in the form of ruts represent a safety risk for road users. In the procedures for dimensioning the pavement structure, the requirement that such deformations do not occur is imperatively included, which results in the appropriate selection of elements (material, geometry) of the pavement structure. Deformability and functionality, will depend of the correct design of pavement structure during exploitation period. Nevertheless, there are many examples where deformations are observed on the pavement structure, in the form of rutting at parts of the road with relatively short length, realised in the same climatic and the same geoenvironmental conditions. The performed analysis of deformability led to the conclusion that the level of deformation is a function of the speed of traffic. This effect is observed on city roads, but also outside of urban areas at roads with speed limits are significant, due to the traffic management, traffic jams (intersections, etc.). Still, the lower speed cause greater deformations. The authors tried to describe the deformability of flexible pavement structures, from the aspects of geotechnical problems, as a function of driving speed. Outcome of the analysis is a traffic load correction coefficient, in terms of using the existing methods of flexible pavement structures design.

• Structural Engineering and Mechanics
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• v.81 no.1
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• pp.93-102
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• 2022

The optimum design of reinforced concrete cantilever retaining walls subjected to seismic loads is an extremely important challenge in structural and geotechnical engineering, especially in seismic zones. This study proposes an adaptive sperm swarm optimization algorithm (ASSO) for economic design of retaining structure under static and seismic loading. The proposed ASSO algorithm utilizes a time-varying velocity damping factor to provide a fine balance between the explorative and exploitative behavior of the original method. In addition, the new method considers a reasonable velocity limitation to avoid the divergence of the sperm movement. The proposed algorithm is benchmarked with a set of test functions and the results are compared with the standard sperm swarm optimization (SSO) and some other robust metaheuristic from the literature. For seismic optimization of retaining structures, Mononobe-Okabe method is employed for dynamic loading conditions and total construction cost of the structure is considered as the single objective function. The optimization constraints include both geotechnical and structural restrictions and the design variables are the geometrical dimensions of the wall and the amount of steel reinforcement. Finally, optimization of two benchmark retaining structures under static and seismic loads using the ASSO algorithm is presented. According to the numerical results, the ASSO may provide better optimal solutions, and the designs obtained by ASSO have a lower cost by up to 20% compared with some other methods from the literature.

• Structural Engineering and Mechanics
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• v.86 no.6
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• pp.731-738
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• 2023

This article presents an analytical approach to explore the bending behaviour of of two-dimensional (2D) functionally graded (FG) nanobeams based on a two-variable higher-order shear deformation theory and nonlocal strain gradient theory. The kinematic relations are proposed according to novel trigonometric functions. The material gradation and material properties are varied along the longitudinal and the transversal directions. The equilibrium equations are obtained by using the virtual work principle and solved by applying Navier's technique. A comparative evaluation of results against predictions from literature demonstrates the accuracy of the proposed analytical model. Moreover, a detailed parametric analysis checks for the sensitivity of the bending and stresses response of (2D) FG nanobeams to nonlocal length scale, strain gradient microstructure scale, material distribution and geometry.

• Earthquakes and Structures
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• v.23 no.3
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• pp.221-229
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• 2022

The external post-tension technique is one of the best strengthening methods for reinforcement and improvement of the various steel structures and substructure components such as beams. In the present work, the load carrying capacity of the post-tensioned tapered steel beams with external shape memory alloy (SMA) tendons are studied. 3D nonlinear finite element method with ABAQUS software is used to determine the effects of the increase in the flexural strength, and the improvement of the load carrying capacity. The effect of the different parameters, such as geometrical characteristics and the post-tension force applied to the tendons are also studied in this research. The results reveal that the external post-tension with SMA tendons in comparison with the steel tendons causes a significant improvement of the loading capacity. According to this, using SMA tendon for the reinforcement of the tapered beams causes a decrease in weight of these structures and as a consequence causes economic benefits for their application. This method can be used extensively for steel beams due to low executive costs and simplicity of the operation for post-tension.