• Structural Engineering and Mechanics
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• 제83권3호
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• pp.327-340
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• 2022

The Collapse Margin Ratio (CMR) is a notable index used for seismic assessment of the structures. As proposed by FEMA P695, a set of analyses including the Nonlinear Static Analysis (NSA), Incremental Dynamic Analysis (IDA), together with Fragility Analysis, which are typically time-taking and computationally unaffordable, need to be conducted, so that the CMR could be obtained. To address this issue and to achieve a quick and efficient method to estimate the CMR, the Artificial Neural Network (ANN), Response Surface Method (RSM), and Adaptive Neuro-Fuzzy Inference System (ANFIS) will be introduced in the current research. Accordingly, using the NSA results, an attempt was made to find a fast and efficient approach to derive the CMR. To this end, 5016 IDA analyses based on FEMA P695 methodology on 114 various Reinforced Concrete (RC) frames with 1 to 12 stories have been carried out. In this respect, five parameters have been used as the independent and desired inputs of the systems. On the other hand, the CMR is regarded as the output of the systems. Accordingly, a double hidden layer neural network with Levenberg-Marquardt training and learning algorithm was taken into account. Moreover, in the RSM approach, the quadratic system incorporating 20 parameters was implemented. Correspondingly, the Analysis of Variance (ANOVA) has been employed to discuss the results taken from the developed model. Additionally, the essential parameters and interactions are extracted, and input parameters are sorted according to their importance. Moreover, the ANFIS using Takagi-Sugeno fuzzy system was employed. Finally, all methods were compared, and the effective parameters and associated relationships were extracted. In contrast to the other approaches, the ANFIS provided the best efficiency and high accuracy with the minimum desired errors. Comparatively, it was obtained that the ANN method is more effective than the RSM and has a higher regression coefficient and lower statistical errors.

• Structural Engineering and Mechanics
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• 제81권6호
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• pp.665-675
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• 2022

The safety problems of giant hydraulic structures such as dams caused by terrorist attacks, earthquakes, and wars often have an important impact on a country's economy and people's livelihood. For the national defense department, timely and effective assessment of damage to or impending damage to dams and other structures is an important issue related to the safety of people's lives and property. In the field of damage assessment and vulnerability analysis, it is usually necessary to give the damage assessment results within a few minutes to determine the physical damage (crack length, crater size, etc.) and functional damage (decreased power generation capacity, dam stability descent, etc.), so that other defense and security departments can take corresponding measures to control potential other hazards. Although traditional numerical calculation methods can accurately calculate the crack length and crater size under certain combat conditions, it usually takes a long time and is not suitable for rapid damage assessment. In order to solve similar problems, this article combines simulation calculation methods with machine learning technology interdisciplinary. First, the common concrete gravity dam shape was selected as the simulation calculation object, and XFEM (Extended Finite Element Method) was used to simulate and calculate 19 cracks with different initial positions. Then, an LSTM (Long-Short Term Memory) machine learning model was established. 15 crack paths were selected as the training set and others were set for test. At last, the LSTM model was trained by the training set, and the prediction results on the crack path were compared with the test set. The results show that this method can be used to predict the crack propagation path rapidly and accurately. In general, this article explores the application of machine learning related technologies in the field of mechanics. It has broad application prospects in the fields of damage assessment and vulnerability analysis.

• Structural Monitoring and Maintenance
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• 제8권4호
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• pp.309-328
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• 2021

The objective of this work was to develop a methodology for geodetic monitoring on onshore wind towers, to ascertain the existence of displacements from object points located in the tower and at the foundation's base. The geodesic auscultation was carried out in the Gravatá 01 and 02 wind towers of the Eólica Gravatá wind farm, located in the Brazilian municipality of Gravatá-PE, using a stable Measurement Reference System. To verify the existence of displacements, pins were implanted, with semi-spherical surfaces, at the bases of the towers being monitored, measured by means of high-precision geometric leveling and around the Gravatá 02 tower, concrete landmarks, iron rods and reflective sheets were implanted, observed using geodetic/topographic methods: GNSS survey, transverse with forced centering, three-dimensional irradiation, edge measurement method and trigonometric leveling of unilateral views. It was found that in the Gravatá 02 tower the average rays of the circular sections of the transverse welds (ST) were 1.8431 m ± 0.0005 m (ST01) and 1.6994 m ± 0.0268 m of ST22, where, 01 and 22 represent the serial number of the transverse welds along the tower. The average calculation of the deflection between the coordinates of the center of the circular section of the ST22 and the vertical reference alignment of the ST1 was 0°2'39.22" ± 2.83" in the Northwest direction and an average linear difference of 0.0878 m ± 0.0078 m. The top deflection angle was 0°8'44.88" and a linear difference of ± 0.2590 m, defined from a non-linear function adjusted by Least Squares Method (LSM).

• Advances in concrete construction
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• 제13권5호
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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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• 제44권1호
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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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• 제30권6호
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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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• 제31권4호
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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.

• 터널과지하공간
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• 제33권4호
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• pp.265-280
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• 2023

터널누수는 주변지반의 응력 및 간극수압을 변화시켜 터널 안정성 및 지반변형에 영향을 미칠 수 있는 결함요소이다. 장기간 또는 큰 규모의 누수발생은 터널 라이닝의 불안정성 및 지표침하와 같은 터널 구조물 및 주변지반 환경에 손상을 일으킬 수 있다. 본 연구에서는 누수발생 시 터널의 구조 안정성 및 지반거동에 미치는 영향을 수치해석적으로 분석하였다. 고려된 터널은 내부로 주변 지하수의 유입을 허용하지 않는 비배수 조건으로 가정하였고 터널 완공 후 라이닝에서 누수가 발생하는 것으로 설정하였다. 누수로 인한 터널 구조물 및 지반의 거동을 모사하기 위해 수리역학 연계해석이 수행되었으며 파이썬으로 개발된 TOUGH-FLAC 시뮬레이터가 사용되었다. 누수 발생량과 누수위치를 변화시켜 수치모사가 수행되었으며 수리역학 해석을 위한 연계항들이 복합거동 결과에 미치는 영향을 조사하였다.

• 한국지반환경공학회 논문집
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• 제24권5호
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• pp.5-12
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• 2023

최근에는 도시개발 및 확장으로 지하철, 도로, 대형관로 등 기존 터널 구조물에 인접하여 건설 계획이 증가하고 있다. 기존 터널에 인접한 구조물 계획은 신규 구조물의 시공방법 및 인접 정도, 위치에 따라 터널 안정성 미치는 영향이 다르다. 특히 압력 수로터널은 지반 거동 특성 및 운영 특성에 있어 도로터널 및 철도터널과는 매우 큰 차이를 보인다. 따라서 수로터널의 거동 특성 및 신규공사 공법을 고려하여 근접시공으로 인한 안전영역에 대한 재검토가 필요하다. 본 연구에서는 기존 터널 인접 시공 기준을 조사하고 수로터널 특성을 고려하여 안정성 평가를 수행하였다. 안정성 평가는 운용 중인 터널의 콘크리트라이닝의 열화를 고려하여 수치해석적 방법으로 평가하였다. 또한, 신규 구조물의 말뚝 시공 및 발파굴착에 의한 진동 영향을 검토하여 인접 시공방법에 따른 기존 터널 안정성이 확보되는 영역을 검토하였다. 국내 다양한 근접시공 영역에 대한 기준을 바탕으로 운용 중인 수로터널 특성을 반영하여 안전영역을 재검토하였으며, 이를 바탕으로 근접 안전 시공범위를 제시하였다.

• Geomechanics and Engineering
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• 제32권1호
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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.