• Advances in concrete construction
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• v.13 no.5
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• pp.385-394
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• 2022

Recently, an increasing number of cutting-edged studies have shown that designing a smart active control for real-time implementation requires piles of hard-work criteria in the design process, including performance controllers to reduce the tracking errors and tolerance to external interference and measure system disturbed perturbations. This article proposes an effective artificial-intelligence method using these rigorous criteria, which can be translated into general control plants for the management of civil engineering installations. To facilitate the calculation, an efficient solution process based on linear matrix (LMI) inequality has been introduced to verify the relevance of the proposed method, and extensive simulators have been carried out for the numerical constructive model in the seismic stimulation of the active rigidity. Additionally, a fuzzy model of the neural network based system (NN) is developed using an interconnected method for LDI (linear differential) representation determined for arbitrary dynamics. This expression is constructed with a nonlinear sector which converts the nonlinear model into a multiple linear deformation of the linear model and a new state sufficient to guarantee the asymptomatic stability of the Lyapunov function of the linear matrix inequality. In the control design, we incorporated H Infinity optimized development algorithm and performance analysis stability. Finally, there is a numerical practical example with simulations to show the results. The implication results in the RMS response with as well as without tuned mass damper (TMD) of the benchmark building under the external excitation, the El-Centro Earthquake, in which it also showed the simulation using evolved bat algorithmic LMI fuzzy controllers in term of RMS in acceleration and displacement of the building.

• Steel and Composite Structures
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• v.44 no.1
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• pp.33-47
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• 2022

This paper presents a finite element model for predicting the monotonic behaviour of bolted endplate joints connecting steel-concrete composite walls and steel beams. The demountable Hollo-bolts are utilised to facilitate the quick installation and dismantling for replacement and reuse. In the developed model, material and geometric nonlinearities were included. The accuracy of the developed model was assessed by comparing the numerical results with previous experimental tests on hollow/composite column-to-steel beam joints that incorporated endplates and Hollo-bolts. In particular, the Hollo-bolts were modelled with the expanded sleeves involved, and different material properties of the Hollo-bolt shank and sleeves were considered based on the information provided by the manufacture. The developed models, therefore, can be applied in the present study to simulate the wall-to-beam joints with similar structural components and characteristics. Based on the validated model, the authors herein compared the behaviour of wall-to-beam joints of two commonly utilised composite walling systems (Case 1: flat steel plates with headed studs; Case 2: lipped channel section with partition plates). Considering the ease of manufacturing, onsite erection and the pertinent costs, composite walling system with flat steel plates and conventional headed studs (Case 1) was the focus of present study. Specifically, additional headed studs were pre-welded inside the front wall plates to enhance the joint performance. On this basis, a series of parametric studies were conducted to assess the influences of five design parameters on the behaviour of bolted endplate wall-to-beam joints. The initial stiffness, plastic moment capacity, as well as the rotational capacity of the composite wall-to-beam joints based on the numerical analysis were further compared with the current design provision.

• Smart Structures and Systems
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• v.30 no.6
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• pp.601-612
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• 2022

The prediction of structural mechanical behaviors is vital important to early perceive the abnormal conditions and avoid the occurrence of disasters. Especially for underground engineering, complex geological conditions make the structure more prone to disasters. Aiming at solving the problems existing in previous studies, such as incomplete consideration factors and can only predict the continuous performance, the deep attention fused temporal convolution network (DATCN) is proposed in this paper to predict the spatial mechanical behaviors of structure, which integrates both the temporal effect and spatial effect and realize the cross-time prediction. The temporal convolution network (TCN) and self-attention mechanism are employed to learn the temporal correlation of each monitoring point and the spatial correlation among different points, respectively. Then, the predicted result obtained from DATCN is compared with that obtained from some classical baselines, including SVR, LR, MLP, and RNNs. Also, the parameters involved in DATCN are discussed to optimize the prediction ability. The prediction result demonstrates that the proposed DATCN model outperforms the state-of-the-art baselines. The prediction accuracy of DATCN model after 24 hours reaches 90 percent. Also, the performance in last 14 hours plays a domain role to predict the short-term behaviors of the structure. As a study case, the proposed model is applied in an underwater shield tunnel to predict the stress variation of concrete segments in space.

• Smart Structures and Systems
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• v.31 no.4
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• pp.335-349
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• 2023

Bridges constantly undergo deterioration and damage, the most common ones being concrete damage and exposed rebar. Periodic inspection of bridges to identify damages can aid in their quick remediation. Likewise, identifying components can provide context for damage assessment and help gauge a bridge's state of interaction with its surroundings. Current inspection techniques rely on manual site visits, which can be time-consuming and costly. More recently, robotic inspection assisted by autonomous data analytics based on Computer Vision (CV) and Artificial Intelligence (AI) has been viewed as a suitable alternative to manual inspection because of its efficiency and accuracy. To aid research in this avenue, this study performs a comparative assessment of different architectures, loss functions, and ensembling strategies for the autonomous segmentation of bridge components and damages. The experiments lead to several interesting discoveries. Nested Reg-UNet architecture is found to outperform five other state-of-the-art architectures in both damage and component segmentation tasks. The architecture is built by combining a Nested UNet style dense configuration with a pretrained RegNet encoder. In terms of the mean Intersection over Union (mIoU) metric, the Nested Reg-UNet architecture provides an improvement of 2.86% on the damage segmentation task and 1.66% on the component segmentation task compared to the state-of-the-art UNet architecture. Furthermore, it is demonstrated that incorporating the Lovasz-Softmax loss function to counter class imbalance can boost performance by 3.44% in the component segmentation task over the most employed alternative, weighted Cross Entropy (wCE). Finally, weighted softmax ensembling is found to be quite effective when used synchronously with the Nested Reg-UNet architecture by providing mIoU improvement of 0.74% in the component segmentation task and 1.14% in the damage segmentation task over a single-architecture baseline. Overall, the best mIoU of 92.50% for the component segmentation task and 84.19% for the damage segmentation task validate the feasibility of these techniques for autonomous bridge component and damage segmentation using RGB images.

• Tunnel and Underground Space
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• v.33 no.4
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• pp.265-280
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• 2023

Water leakage in tunnels is a defect that can affect tunnel stability and the ground movement by changing the stress and pore water pressure of the surrounding ground. Long-term or large-scale water leaks may lead to damage of tunnel structure and the surrounding environment, such as tunnel lining instability and ground surface settlement. The present study numerically investigated the effects of water leakage on the structural stability of a tunnel and the ground behavior. The tunnel was assumed to be under undrained conditions for preventing the inflow of the surrounding water and leaks occurred in the concrete lining after completion of the tunnel construction. A coupled hydro-mechanical analysis using a TOUGH-FLAC simulator developed in Python was conducted for assessing the leakage induced-behavior of the tunnel structure and ground under different conditions of the amount and location of water leak. Additionally, the effect of hydro-mechanical coupling terms on the results of coupled response was investigated and discussed.

• Journal of the Korean GEO-environmental Society
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• v.24 no.5
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• pp.5-12
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• 2023

Recently, due to urban development and expansion, construction plans have been increasing adjacent to existing tunnel structures such as subways, roads, and large pipelines. Structural plans adjacent to existing tunnels have different effects on tunnel stability depending on the construction method, degree of proximity, and location of new structures. In particular, the pressure water tunnel shows a very large difference from other road tunnels and railway tunnels in geotechnical characteristics and operation characteristics. Therefore, it is necessary to review the safety zone due to adjacent construction in consideration of the geotechnical characteristics of the water tunnel and the new sturure construction method. In this study, the existing tunnel safety zone standards were investigated. A stability evaluation performed numerical analysis considering the deterioration of concrete lining in operation and the characteristics of water tunnel. In addition, the impact of vibration caused by pile construction and blasting excavation of new structures was reviewed. Based on this, a pressure water tunnel safety zone was proposed in consideration of adjacent construction.

• Geomechanics and Engineering
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• v.32 no.1
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• pp.69-84
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• 2023

This paper proposes a novel frame element on Winkler-Pasternak foundation for analysis of a non-ductile reinforced concrete (RC) member resting on foundation. These structural members represent flexural-shear critical members, which are commonly found in existing buildings designed and constructed with the old seismic design standards (inadequately detailed transverse reinforcement). As a result, these structures always experience shear failure or flexure-shear failure under seismic loading. To predict the characteristics of these non-ductile structures, efficient numerical models are required. Therefore, the novel frame element on Winkler-Pasternak foundation with inclusion of the shear-flexure interaction effect is developed in this study. The proposed model is derived within the framework of a displacement-based formulation and fiber section model under Timoshenko beam theory. Uniaxial nonlinear material constitutive models are employed to represent the characteristics of non-ductile RC frame and the underlying foundation. The shear-flexure interaction effect is expressed within the shear constitutive model based on the UCSD shear-strength model as demonstrated in this paper. From several features of the presented model, the proposed model is simple but able to capture several salient characteristics of the non-ductile RC frame resting on foundation, such as failure behavior, soil-structure interaction, and shear-flexure interaction. This confirms through two numerical simulations.

• Journal of the Korean Geotechnical Society
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• v.25 no.11
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• pp.17-26
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• 2009

The concrete cap, which was established on the top of the micropile, usually considered as an important structural component in micropile supported foundation systems. However, relatively few studies have been made on the load sharing behavior of the capped micropile foundation systems. The primary objective of this study is to assess the load sharing behavior of the capped micropile foundation systems. Therefore, a full-scale test on an instrumented capped micropile is conducted for establishing the load-displacement responses. Nonlinear numerical method was used to quantify the load sharing behavior of the pile cap and micropile respectively. As a result, it was found that the pile cap shares about 50% load from final loading steps in the case of 2 by 1 micropile foundation systems. In the case of 2 by 2, the pile cap shares about 30% load from final loading steps. In addition, the load sharing behavior of the micropile cap becomes larger with an increase in spacing and the battered angle of micropile respectively.

• Steel and Composite Structures
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• v.46 no.1
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• pp.75-92
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• 2023

The novel composite cold-formed steel (CFS)/engineered cementitious composites (ECC) beams have been recently presented. The new composite section exhibited superior structural performance as a flexural member, benefiting from the lightweight thin-walled CFS sections with improved buckling and torsional properties due to the restraints provided by thinlayered ECC. This paper investigated the shear performance of the new composite CFS/ECC section. Twenty-eight simply supported beams, with a shear span-to-depth ratio of 1.0, were assembled back-to-back and tested under a 3-point loading scheme. Bare CFS, composite CFS/ECC utilising ECC with Polyethylene fibres (PE-ECC), composite CFS/MOR, and CFS/HSC utilising high-strength mortar (MOR) and high-strength concrete (HSC) as replacements for PE-ECC were compared. Different failure modes were observed in tests: shear buckling modes in bare CFS sections, contact shear buckling modes in composite CFS/MOR and CFS/HSC sections, and shear yielding or block shear rupture in composite CFS/ECC sections. As a result, composite CFS/ECC sections showed up to 96.0% improvement in shear capacities over bare CFS, 28.0% improvement over composite CFS/MOR and 13.0% over composite CFS/HSC sections, although MOR and HSC were with higher compressive strength than PE-ECC. Finally, shear strength prediction formulae are proposed for the new composite sections after considering the contributions from the CFS and ECC components.

• Structural Engineering and Mechanics
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• v.85 no.4
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• pp.469-484
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• 2023

A deep recursive bidirectional Cuda Deep Neural Network Long Short Term Memory (Bi-CuDNNLSTM) layer is recruited in this paper to predict the entire force time histories, and the corresponding hysteresis and backbone curves of reinforced concrete (RC) bridge piers using experimental fast and slow cyclic tests. The proposed stacked Bi-CuDNNLSTM layers involve multiple uncertain input variables, including horizontal actuator displacements, vertical actuators axial loads, the effective height of the bridge pier, the moment of inertia, and mass. The functional application programming interface in the Keras Python library is utilized to develop a deep learning model considering all the above various input attributes. To have a robust and reliable prediction, the dataset for both the fast and slow cyclic tests is split into three mutually exclusive subsets of training, validation, and testing (unseen). The whole datasets include 17 RC bridge piers tested experimentally ten for fast and seven for slow cyclic tests. The results bring to light that the mean absolute error, as a loss function, is monotonically decreased to zero for both the training and validation datasets after 5000 epochs, and a high level of correlation is observed between the predicted and the experimentally measured values of the force time histories for all the datasets, more than 90%. It can be concluded that the maximum mean of the normalized error, obtained through Box-Whisker plot and Gaussian distribution of normalized error, associated with unseen data is about 10% and 3% for the fast and slow cyclic tests, respectively. In recapitulation, it brings to an end that the stacked Bi-CuDNNLSTM layer implemented in this study has a myriad of benefits in reducing the time and experimental costs for conducting new fast and slow cyclic tests in the future and results in a fast and accurate insight into hysteretic behavior of bridge piers.