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1 |
A graphics-based digital twin framework for computer vision-based post-earthquake structural inspection and evaluation using unmanned aerial vehicles
یک چارچوب دیجیتال دوقلوی مبتنی بر گرافیک برای بازرسی و ارزیابی ساختاری پس از زلزله مبتنی بر بینایی کامپیوتری با استفاده از وسایل نقلیه هوایی بدون سرنشین-2022 Rapid structural inspections and evaluations are critical after earthquakes. Computer vision-based methods have attracted the interest of researchers for their potential to be rapid, safe, and objective. To provide an end-to-end solution for computer vision-based post-earthquake inspection and evaluation of a specific as-built structure, the concepts of physics-based graphics model (PBGM) and digital twin (DT) are combined to develop a graphics-based digital twin (GBDT) framework. The GBDT framework comprises a finite element (FE) model and a computer graphics (CG) model whose state is informed by the FE analysis, representing the state of the structure before and after an earthquake. The CG model is first created making use of the FE model and the photographic survey of the structure, yielding the virtual counterpart of the as-built structure quickly and accurately. Then damage modelling approaches are proposed to predict the location and extent of structural and nonstructural damage under seismic loading, from which photographic representation of the predicted damage is realized in the CG model. The effectiveness of the GBDT framework is demonstrated using a five-story reinforced concrete benchmark building through the design and assessment of various UAV (Unmanned Aerial Vehicle) inspection trajectories for post-earthquake scenarios. The results demonstrate that the proposed GBDT framework has significant potential to enable rapid structural inspection and evaluation, ultimately leading to more efficient allocation of scarce resources in a post-earthquake setting.
keywords: بینایی کامپیوتر | مهندسی زلزله | دوقلو دیجیتال | ارزیابی پس از زلزله | دوقلو دیجیتال مبتنی بر گرافیک | مدل گرافیکی مبتنی بر فیزیک | Computer vision | Earthquake engineering | Digital twin | Post-earthquake assessment | Graphics-based digital twin | Physics-based graphics model |
مقاله انگلیسی |
2 |
Prediction of perforation into concrete accounting for saturation ratio influence at high confinement
پیش بینی سوراخ شدن در بتن برای تأثیر نسبت اشباع در محصور شدن بالا-2021 This paper provides both an analytical and a finite element models aiming at better predicting possible perfo-
ration of reinforced concrete slabs submitted to impacts. Both models account for free water saturation ratio and
high triaxial stress induced into concrete by the impact. Finite element simulations are performed with Abaqus
explicit code using a revised constitutive model for concrete; this coupled damage plasticity model (PRM) ac-
counts for strain rate effects and the influence of saturation ratio on the triaxial behavior. Complementary
original analytical predictions of ballistic limit and residual velocities are provided for both hard and soft im-
pacts. These predictions depend on a recent deviatoric stress-based formulation of compressive strength of
concrete. Numerical and analytical results are consistent with bending and punching experimental tests. keywords: اثرات نرم و سخت | سرعت باقی مانده | بتن آرمه | ظرفیت سوراخ کردن | نسبت اشباع | Soft and hard impacts | Residual velocity | Reinforced concrete | Perforation capacity | Saturation ratio |
مقاله انگلیسی |
3 |
Determination of the interfacial cohesive material law for SRG composites bonded to a masonry substrate
تعیین قانون ماده چسبندگی میان سطحی برای کامپوزیت های SRG که به یک بستر سنگ تراشی متصل می شوند-2020 Fiber reinforced cementitious matrix (FRCM) composites, also known as textile reinforced matrix
(TRM) composites, are a suitable alternative to fiber reinforced polymer (FRP) composites to
strengthen reinforced concrete and masonry structures. In the toolbox of FRCMs, a recentlydeveloped
composite that employs high-strength steel fibers embedded in a hydraulic mortar is
particular appealing for applications on historical masonry constructions. This type of composite
is known as steel reinforced grout (SRG). In this paper, an extensive experimental work is presented.
Single-lap shear tests are performed to study the debonding of SRG strips from a masonry
substrate, which is the critical failure mode for strengthening applications. For SRGs, debonding
typically occurs at the fiber-matrix interface. A large scatter of the experimental results is observed,
which is related to the variability of hydraulic mortars and their ability to impregnate the
fibers. Although strain gauges can be applied directly to the fibers to obtain the experimental
strain profile along the fibers, because of the presence of the matrix these measurements are
complex and in some cases not reliable. Thus, indirect method based on the global response of the
test is proposed to obtain the interfacial properties. Keywords: SRG | Debonding | Cohesive Material Law | Masonry |
مقاله انگلیسی |
4 |
Automated clash resolution for reinforcement steel design in concrete frames via Q-learning and Building Information Modeling
رزولوشن برخورد خودکار برای طراحی فولاد تقویت کننده در قاب های بتنی از طریق یادگیری Q و مدل سازی اطلاعات ساخت-2020 The design of reinforcing steel bars (rebars) is critical to reinforced concrete (RC) structures. Generally, a good
number of rebars are required by a design code, particularly at member connections. As such, rebar clashes (i.e.,
collisions and congestions) would be inevitable. It would be impractical, labor-intensive, and error-prone to
avoid all possible clashes manually or even using standard design software. The building information modeling
(BIM) technology has been utilized by the present architecture, engineering, and construction (ACE) industry for
clash-free rebar designs. However, most existing BIM-based approaches offer the clash resolution strategy for
moving components with an optimization algorithm, and are only applicable to the RC structures with regular
shapes. In particular, the optimized path of rebars cannot be adjusted to avoid the obstacles, thus limiting the
practical applications. Furthermore, most existing studies lack the learning from design code and constructibility
constraints to realize automatic and intelligent arrangement and adjustment of rebars for avoiding the obstacles
encountered in complex RC joints and frame structures. Considering these shortcomings, the authors have recently
proposed an immediate reward-based multi-agent reinforcement learning (MARL) system with BIM, towards
automatic clash-free rebar designs of RC joints without clashes. However, as the immediate reward is
required in the MARL system for guiding the learning of a rebar design, it will not succeed in clash-free rebar
designs of complex RC structures where immediate reward is often unavailable. In this study, this study further
extends the previous work with Q-learning (a model-free reinforcement learning algorithm) for more realistic
path planning considering both immediate and delayed rewards in clash-free rebar designs for real-world RC
structures. In particular, the rebar design problem is treated as a path-planning problem of multi-agent system,
where each rebar is deemed as an intelligence reinforcement learning agent. Next, by employing the Q-learning
as the reinforcement learning engine, the particular form of state, action, and immediate and delayed rewards
for the reinforcement MARL for automatic rebar designs considering more actual constructible constraints and
design codes can be developed. Comprehensive experiments on three typical beam-column joints and a two-story
RC building frame were conducted to evaluate the efficiency of the proposed method. The study results of paths
of rebar designs, success rates, and average time confirm that the proposed framework with MARL and BIM is
effective and efficient. Keywords: Building Information Modeling | Reinforcement learning | Multi-agent | Q-learning | Rebar design | Clash resolution | Reinforced concrete frame |
مقاله انگلیسی |
5 |
Design-oriented approach to determine FRC constitutive law parameters considering the size effect
روش طراحی گرا برای تعیین پارامترهای قانون سازنده FRC با توجه به اثر اندازه-2020 Tensile strength constitutive laws for fibre reinforced concrete (FRC) are commonly defined through the parameters of flexural tests conducted on standard prismatic specimens. However, there are no specific criteria to determine such parameters using small specimens that could simplify the testing procedure and provide more representative results of slender structural FRC elements. In this line, the influence of size effect becomes an issue particularly relevant during the characterisation stage given that the residual strength decreases while increasing the size of the element. The objective of this document is to propose a methodology to obtain the parameters of the constitutive law using small specimens. For this, FRC residual strength was determined through three-point bending tests on prismatic notched beams of 40 × 40 × 160, 100 × 100 × 400 and 150 × 150 × 600 mm. An analytical model based on sectional analyses aimed at reproducing the flexural strength of FRC was used to assess the results of the alternative methodology to determine the parameters for the constitutive law. The results show that an approach based on the rotation instead of the crack opening as the reference parameter to estimate the stresses for the constitutive law leads to results less influenced by the size effect when designing small elements. |
مقاله انگلیسی |
6 |
Neural network-based seismic response prediction model for building structures using artificial earthquakes
مدل پیش بینی لرزه ای مبتنی بر شبکه عصبی برای سازه های ساختمان با استفاده از زلزله های مصنوعی-2020 In this paper, a new model for predicting seismic responses of buildings based on the
correlation of ground motion (GM) and the structure is presented by simulating numerous
artificial earthquakes (AEQs). In the model, neural network (NN) configurations representing
the relationships between GM characteristics and seismic responses of a structure
are developed to predict responses of the structure with only GM data measured by
monitoring system in future seismic events. To extract the GM characteristics, multiple
AEQs corresponding to the design response spectrum are generated based on probabilistic
vibration theory, instead of using historical earthquakes. In the presented NN configurations,
GM characteristics including mean and predominant period, significant duration,
and peak ground acceleration are established as the input layer and the maximum interstory
drift ratio and maximum displacement are established as the output layer. In addition,
a new parameter called resonance area is proposed to represent the relationship
between a GM and a target structure in the frequency domain and utilized in the NN input
layer. By employing the new parameter, dynamic characteristics of the structure are
considered in the response estimation of the model with related to GM. The model is
applied to seismic response prediction for four multi-degrees-of-freedom (MDOF) structures
with different natural periods using 2700 AEQs. The validities of the presented NN
models are confirmed by investigating the performance of response prediction. The
effectiveness of the resonance area parameter in the NN for predicting the seismic responses
is assessed and discussed. Furthermore, the effects of the constitution of NNs and
computational costs of those NNs on estimation were investigated. Finally, the presented
model is employed for prediction of seismic responses for a structural model of a planar
reinforced concrete building structure. Keywords: Structural health monitoring | Seismic response prediction | Neural network | Artificial earthquake |
مقاله انگلیسی |
7 |
Classification of in-plane failure modes for reinforced concrete frames with infills using machine learning
طبقه بندی حالت های شکست در هواپیما برای قاب های بتونی مسلح با استفاده از یادگیری ماشین-2019 The failure modes of reinforced concrete frame structures with masonry infill panels have strong implications to
their overall seismic performance. This paper explores a data-driven approach to classifying the in-plane failure
modes of infill frames by employing machine learning methods. To this end, an experimental database consisting
of 114 infill frame specimens is constructed. Six machine learning algorithms are implemented and evaluated for
failure-mode classification using nine structural parameters as input variables. The validation results indicate
that most of the models are able to achieve more than 80% prediction accuracy, with the highest accuracy of
85.7% achieved by the Adaptive Boosting and Support Vector Machine algorithms. Keywords: Masonry infills | Failure mode | Reinforced concrete frames | Seismic performance | Machine learning |
مقاله انگلیسی |
8 |
Advanced damage detection technique by integration of unsupervised clustering into acoustic emission
تکنیک پیشرفته تشخیص آسیب با ادغام خوشه های بدون نظارت در انتشار آکوستیک-2019 The use of acoustic emission (AE) technique for damage diagnostic is typically challenging due to
difficulties associated with discrimination of events that occur during different stages of damage
that take place in a material or a structure. In this study, an unsupervised kernel fuzzy c-means
pattern recognition analysis and the principal component method were utilized to categorize
various damage stages in plain and steel fiber reinforced concrete specimens monitored by AE
technique. Enhancement of the discrimination and characterization of damage mechanisms were
achieved by processing time and frequency domain data. Both domains (time and frequency)
were taken into account to propose new descriptors for crack classification purposes. A cluster of
AE data in three classes of Kernel Fuzzy c-means (KFCM) was obtained. The clustered data was
subsequently correlated with each particular damage stage for identifying the peak frequency
range corresponding to the respective damage stages. Moreover, a novel quantitative technique
called Spatial Intelligent b-value (SIb) Analysis was proposed to quantify damage for each stage. Keywords: Acoustic emission | Torsional loading | Structural health monitoring | Unsupervised pattern recognition | Damage detection | Non-destructive testing |
مقاله انگلیسی |
9 |
Comparative evaluation of MFP and RBF neural networks’ ability for instant estimation of r/c buildings’ seismic damage level
ارزیابی مقایسه ای توانایی شبکه های عصبی MFP و RBF در برآورد فوری میزان آسیب لرزه ای ساختمانهای r / c-2019 The problem of the seismic damage prediction of reinforced concrete (r/c) buildings utilizing two types of
Artificial Neural Networks (ANN) is investigated in the present paper. More specifically, the problem is formulated
and solved in terms of the Function Approximation problem as well as of the Pattern Recognition
problem using Multilayer Feedforward Perceptron Networks (MFP) and Radial-Basis Function (RBF) networks.
The required training data-sets are created by means of Nonlinear Time History Analyses of 90 r/c buildings
which are subjected to 65 earthquakes. The selected buildings differ in total height, in structural system, in
structural eccentricity as well as the existence or not of masonry infills. The seismic damage index which is used
to describe the seismic damage state is the Maximum Interstorey Drift Ratio. The influence of the parameters
which are used for the configuration and the training of MFP and RBF networks on the reliability of their
predictions is also investigated. The generalization ability of the best configured ANNs is examined by means of
two categories of seismic scenarios. The most significant conclusion that turned out is that the trained ANNs can
reliably and rapidly classify the r/c buildings into pre-defined damage classes provided they are appropriately
configured. Keywords: Artificial neural networks | MFP networks | RBF networks | Seismic damage prediction | Structural vulnerability assessment | Reinforced concrete buildings |
مقاله انگلیسی |
10 |
High-performance fiber reinforced concrete as a repairing material to normal concrete structures: Experiments, numerical simulations and a machine learning-based prediction model
بتن مسلح با فیبر با کارایی بالا به عنوان یک ماده ترمیم کننده سازه های بتونی عادی: آزمایش ها ، شبیه سازی عددی و یک مدل پیش بینی مبتنی بر یادگیری ماشین-2019 High-performance fiber reinforced concrete (HPFRC) has been reported as a repairing material to normal
concrete (NC) structures due to its predominant mechanical performance. Here, we investigate the
debonding behavior between HPFRC and NC subjected to direct shear loading. HPFRC specimens are fabricated
and experimentally calibrated to determine the compressive and bending (i.e., flexural) strengths.
HPFRC-NC samples are fabricated using two bonding strategies, i.e., mechanical surface treatments with
and without chemical agent. Direct shear loading is applied to test the HPFRC-NC debonding behavior. A
finite element (FE) model is developed to predict the direct shear debonding response. The FE model is
validated by the experimental observations and then used to characterize the debonding behavior with
various geometric and material parameters, as well as bonding interface treatments. Subsequently, a
robust machine learning model is developed to formulate the shear debonding strength of HPFRC-NC
with those influencing parameters. Design examples are presented to illustrate the efficiency of the proposed
machine learning model in describing the debonding response of HPFRC-NC. A sensitivity analysis
is further conducted to investigate the contribution of the chosen predictors to the debonding behavior of
HPFRC-NC. The reported HPFRC and machine learning-based prediction model provide powerful tools to
address repairing issues in various existing normal concrete structures. Keywords: High-performance fiber reinforced concrete | (HPFRC) | Normal concrete (NC) | Debonding behavior | Machine learning | Prediction model | Direct shear test |
مقاله انگلیسی |