• Structural Engineering and Mechanics
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• v.71 no.1
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• pp.57-63
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• 2019

During the last two decades, much joint research regarding vibration based methods has been done, leading to developing various algorithms and techniques. These algorithms and techniques can be divided into modal methods and signal methods. Although modal methods have been widely used for health monitoring and damage detection, signal methods due to higher efficiency have received considerable attention in various fields, including aerospace, mechanical and civil engineering. Signal-based methods are derived directly from the recorded responses through signal processing algorithms to detect damage. According to different signal processing techniques, signal-based methods can be divided into three categories including time domain methods, frequency domain methods, and time-frequency domain methods. The frequency domain methods are well-known and interest in using them has increased in recent years. To determine dynamic behaviours, to identify systems and to detect damages of bridges, different methods and algorithms have been proposed by researchers. In this study, a new algorithm to detect seismic damage in the bridge's piers is suggested. To evaluate the algorithm, an analytical model of a bridge with simple spans is used. Based on the algorithm, before and after damage, the bridge is excited by a sine force, and the piers' responses are measured. The dynamic specifications of the bridge are extracted by Power Spectral Density function. In addition, the Least Square Method is used to detect damage in the bridge's piers. The results indicate that the proposed algorithm can identify the seismic damage effectively. The algorithm is output-only method and measuring the excitation force is not needed. Moreover, the proposed approach does not need numerical models.

• Structural Engineering and Mechanics
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• v.69 no.2
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• pp.221-230
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• 2019

In this paper, shear behavior of non-persistent joint surrounded in concrete and gypsum layers has been investigated using experimental test and numerical simulation. Two types of mixture were prepared for this study. The first type consists of water and gypsum that were mixed with a ratio of water/gypsum of 0.6. The second type of mixture, water, sand and cement were mixed with a ratio of 27%, 33% and 40% by weight. Shear behavior of a non-persistent joint embedded in these specimens is studied. Physical models consisting of two edge concrete layers with dimensions of 160 mm by 130 mm by 60 mm and one internal gypsum layer with the dimension of 16 mm by 13 mm by 6 mm were made. Two horizontal edge joints were embedded in concrete beams and one angled joint was created in gypsum layer. Several analyses with joints with angles of $0^{\circ}$, $30^{\circ}$, and $60^{\circ}$ degree were conducted. The central fault places in 3 different positions. Along the edge joints, 1.5 cm vertically far from the edge joint face and 3 cm vertically far from the edge joint face. All samples were tested in compression using a universal loading machine and the shear load was induced because of the specimen geometry. Concurrent with the experiments, the extended finite element method (XFEM) was employed to analyze the fracture processes occurring in a non-persistent joint embedded in concrete and gypsum layers using Abaqus, a finite element software platform. The failure pattern of non-persistent cracks (faults) was found to be affected mostly by the central crack and its configuration and the shear strength was found to be related to the failure pattern. Comparison between experimental and corresponding numerical results showed a great agreement. XFEM was found as a capable tool for investigating the fracturing mechanism of rock specimens with non-persistent joint.

• Computers and Concrete
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• v.27 no.4
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• pp.333-341
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• 2021

Steel slag, an industrial reject from the steel rolling process, has been identified as one of the suitable, environmentally friendly materials for concrete production. Given that the coarse aggregate portion represents about 70% of concrete constituents, other economic approaches have been found in the use of alternative materials such as steel slag in concrete. Unfortunately, a standard framework for its application is still lacking. Therefore, this study proposed functional model equations for the determination of strength properties (compression and splitting tensile) of steel slag aggregate concrete (SSAC), using gene expression programming (GEP). The study, in the experimental phase, utilized steel slag as a partial replacement of crushed rock, in steps 20%, 40%, 60%, 80%, and 100%, respectively. The predictor variables included in the analysis were cement, sand, granite, steel slag, water/cement ratio, and curing regime (age). For the model development, 60-75% of the dataset was used as the training set, while the remaining data was used for testing the model. Empirical results illustrate that steel aggregate could be used up to 100% replacement of conventional aggregate, while also yielding comparable results as the latter. The GEP-based functional relations were tested statistically. The minimum absolute percentage error (MAPE), and root mean square error (RMSE) for compressive strength are 6.9 and 1.4, and 12.52 and 0.91 for the train and test datasets, respectively. With the consistency of both the training and testing datasets, the model has shown a strong capacity to predict the strength properties of SSAC. The results showed that the proposed model equations are reliably suitable for estimating SSAC strength properties. The GEP-based formula is relatively simple and useful for pre-design applications.

• Earthquakes and Structures
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• v.20 no.1
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• pp.109-122
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• 2021

Significant progress in the oil and gas industry advances the application of pipeline into an intelligent era, which poses rigorous requirements on pipeline safety, reliability, and maintainability, especially when crossing seismic zones. In general, strike-slip faults are prone to induce large deformation leading to local buckling and global rupture eventually. To evaluate the performance and safety of pipelines in this situation, numerical simulations are proved to be a relatively accurate and reliable technique based on the built-in physical models and advanced grid technology. However, the computational cost is prohibitive, so one has to wait for a long time to attain a calculation result for complex large-scale pipelines. In this manuscript, an efficient and accurate surrogate model based on machine learning is proposed for strain demand prediction of buried X80 pipelines subjected to strike-slip faults. Specifically, the support vector regression model serves as a surrogate model to learn the high-dimensional nonlinear relationship which maps multiple input variables, including pipe geometries, internal pressures, and strike-slip displacements, to output variables (namely tensile strains and compressive strains). The effectiveness and efficiency of the proposed method are validated by numerical studies considering different effects caused by structural sizes, internal pressure, and strike-slip movements.

• Advances in nano research
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• v.10 no.1
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• pp.59-69
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• 2021

Electrospinning is a cost-effective and versatile method for producing submicron fibers. Although this method is relatively simple, at the theoretical level the interactions between process parameters and their influence on the fiber morphology are not yet fully understood. In this paper, the aim was finding optimal electrospinning parameters in order to obtain the smallest fiber diameter by using Taguchi's methodology. The nanofibers produced by electrospinning a solution of Thermoplastic Polyurethane (TPU) in Dimethylformamide (DMF). Polymer concentration and process parameters were considered as the effective factors. Taguchi's L9 orthogonal design (4 parameters, 3 levels) was applied to the experiential design. Optimal electrospinning conditions were determined using the signal-to-noise (S/N) ratio with Minitab 17 software. The morphology of the nanofibers was studied by a Scanning Electron Microscope (SEM). Thereafter, a tensile tester machine was used to assess mechanical properties of nanofibrous scaffolds. The analysis of DoE experiments showed that TPU concentration was the most significant parameter. An optimum combination to reach smallest diameters was yielded at 12 wt% polymer concentration, 16 kV of the supply voltage, 0.1 ml/h feed rate and 15 cm tip-to-distance. An empirical model was extracted and verified using confirmation test. The average diameter of nanofibers at the optimum conditions was in the range of 242.10 to 257.92 nm at a confidence level 95% which was in close agreement with the predicted value by the Taguchi technique. Also, the mechanical properties increased with decreasing fibers diameter. This study demonstrated Taguchi method was successfully applied to the optimization of electrospinning conditions for TPU nanofibers and the presented scaffold can mimic the structure of Extracellular Matrix (ECM).

• Composites Research
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• v.35 no.3
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• pp.182-187
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• 2022

In this paper, the thickness of a multilayer PCB was predicted through an empirical formulation based on the physical properties of the prepreg used in multilayer PCB. Since the thickness of prepreg reduction when manufacturing a PCB due to the physical properties and copper foil residual rate, it is necessary to accurately predict the thickness of the PCB through the thickness empirical formulation. To determine the density of the prepreg, the mass and thickness of the prepreg were measured. To manufacture the CCL, the prepreg and copper foil were laminated using a hot press machine, and the thickness was measured using a microscope and micrometer. An 8-layerd PCB was designed with different circuit densities to measure the change in the thickness with the copper foil residual ratio, and the proposed empirical formulation was verified by comparing the measured thickness with the value obtained using the empirical formulation. As a result, the errors for the CCL and multilayer PCB were 2.56% and 4.48%, respectively, which demonstrated the reliability of the empirical formulation.

• Smart Structures and Systems
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• v.30 no.3
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• pp.333-338
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• 2022

Planning activity is complex process assuming the term "complexity" as a group of elements interconnected each other. The common knowledge about city planning underlines its main aim as: figuring the present, imaging the future, governing every day the territory and the way people use and live it at different scales. When considering the strength of technological opportunities and the spreading of ICT and IoT devices within everyday life, that mean within the life of cities, the complex nature of the urban system increases with the intensification of information and their connections. Recent orientations about urban and regional planning try to carry the discipline to a more flexible approach in respect to the hyperdeterminant role of direct technical applications. This passage is a fundamental aspect considering the faster and faster modifications of social and economic assets at the global and local scale. At the same time, the "environment question" became more and more relevant at the worldwide scale within the 2015 UN 2030 Agenda for Sustainable Development. Another relevant aspect about the recent urban planning orientations regards the role of the different subjects that are part of the planning process. Approaching the government of smart cities means to define how every subject, with different roles (public or private), could enrich the knowledge of the functioning of the "urban machine" and the awareness of participation of people and city users in the quality of urban life. In the paper author starts defining recent approaches in urban planning, then the nature of the city as a complex system is analyzed from the point of view of planners and of the different subjects that act in the city. Then the smart city is introduced as a further level of complexity and finally author propose the basic element of a Planning Support System.

• Geomechanics and Engineering
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• v.30 no.2
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• pp.187-199
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• 2022

Kriging metamodel, as a flexible machine learning method for approximating deterministic analysis models of an engineering system, has been widely used for efficiently estimating slope reliability in recent years. However, the autocorrelation function (ACF), a key input to Kriging that affects the accuracy of reliability estimation, is usually selected based on empiricism. This paper proposes an adaption of the Kriging method, named as Genetic Algorithm optimized Whittle-Matérn Kriging (GAWMK), for addressing this issue. The non-classical two-parameter Whittle-Matérn (WM) function, which can represent different ACFs in the Matérn family by controlling a smoothness parameter, is adopted in GAWMK to avoid subjectively selecting ACFs. The genetic algorithm is used to optimize the WM model to adaptively select the optimal autocorrelation structure of the GAWMK model. Monte Carlo simulation is then performed based on GAWMK for a subsequent slope reliability analysis. Applications to one explicit analytical example and two slope examples are presented to illustrate and validate the proposed method. It is found that reliability results estimated by the Kriging models using randomly chosen ACFs might be biased. The proposed method performs reasonably well in slope reliability estimation.

• Geomechanics and Engineering
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• v.30 no.3
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• pp.259-272
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• 2022

Rockburst is a dynamic, multivariate, and non-linear phenomenon that occurs in underground mining and civil engineering structures. Predicting rockburst is challenging since conventional models are not standardized. Hence, machine learning techniques would improve the prediction accuracies. This study describes decision based uncertainty models to predict rockburst in underground engineering structures using gradient boosting algorithms (GBM). The model input variables were uniaxial compressive strength (UCS), uniaxial tensile strength (UTS), maximum tangential stress (MTS), excavation depth (D), stress ratio (SR), and brittleness coefficient (BC). Several models were trained using different combinations of the input variables and a 3-fold cross-validation resampling procedure. The hyperparameters comprising learning rate, number of boosting iterations, tree depth, and number of minimum observations were tuned to attain the optimum models. The performance of the models was tested using classification accuracy, Cohen's kappa coefficient (k), sensitivity and specificity. The best-performing model showed a classification accuracy, k, sensitivity and specificity values of 98%, 93%, 1.00 and 0.957 respectively by optimizing model ROC metrics. The most and least influential input variables were MTS and BC, respectively. The partial dependence plots revealed the relationship between the changes in the input variables and model predictions. The findings reveal that GBM can be used to anticipate rockburst and guide decisions about support requirements before mining development.

• Advances in concrete construction
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• v.13 no.1
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• pp.83-99
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• 2022

The two, NBD and SCB tests using gypsum circular discs each containing a single notch have been experimentally accomplished in a rock mechanics laboratory. These specimens have also been numerically modelled by a two-dimensional particle flow which is based on Discrete Element Method (DEM). Each testing specimen had a thickness of 5 cm with 10 cm in diameter. The specimens' lengths varied as 2, 3, and 4 cm; and the specimens' notch angles varied as 0°, 45° and 90°. Similar semi-circular gypsum specimens were also prepared each contained one edge notch with angles 0° or 45°. The uniaxial testing machine was used to perform the experimental tests for both NBD and SCB gypsum specimens. At the same time, the numerical simulation of these tests were performed by PFC2D. The experimental results showed that the failure mechanism of rocks is mainly affected by the orientations of joints with respect to the loading directions. The failure mechanism and fracturing patterns of the gypsum specimens are directly related to the final failure loading. It has been shown that the number of induced tensile cracks showing the specimens' tensile behavior, and increases by decreasing the length and angle of joints. It should be noted that the fracture toughness of rocks' specimens obtained by NBD tests was higher than that of the SCB tests. The fracture toughness of rocks usually increases with the increasing of joints' angles but increasing the joints' lengths do not change the fracture toughness. The numerical solutions and the experimental results for both NDB and SCB tests give nearly similar fracture patterns during the loading process.