A direct time-domain procedure for the seismic analysis of dam–foundation–reservoir systems using the scaled boundary finite element method
- Authors: Qu, Yanling , Chen, Denghong , Liu, Lei , Ooi, Ean Tat , Eisenträger, Sascha , Song, Chongmin
- Date: 2021
- Type: Text , Journal article
- Relation: Computers and Geotechnics Vol. 138, no. (2021), p.
- Full Text: false
- Reviewed:
- Description: In this paper, a direct time-domain procedure for the seismic analysis of dam–reservoir–foundation interactions is presented based on the scaled boundary finite element method (SBFEM). The SBFEM is a semi-analytical method and requires the discretization of boundary only. The geometric complexity in the bounded dam–reservoir–foundation system is easily handled in the SBFEM using quadtree meshes where each structural component can be discretized independently. The elastic wave fields in the unbounded foundation are rigorously captured through SBFE solutions in terms of displacement unit-impulse response functions, while the acoustic wave propagation in the semi-infinite reservoir is modelled by the SBFE-based doubly asymptotic open boundary. The input of seismic excitations is addressed by incorporating the Domain Reduction Method (DRM) into the SBFEM. Cracks are modelled efficiently and accurately by combining the SBFEM and quadtree meshes. The accuracy and efficiency of the proposed methodology is investigated by studying several benchmarks, Pine Flat dam and Jin'anqiao dam. © 2021 Elsevier Ltd
Assessing trust level of a driverless car using deep learning
- Authors: Karmakar, Gour , Chowdhury, Abdullahi , Das, Rajkumar , Kamruzzaman, Joarder , Islam, Syed
- Date: 2021
- Type: Text , Journal article
- Relation: IEEE Transactions on Intelligent Transportation Systems Vol. 22, no. 7 (2021), p. 4457-4466
- Full Text: false
- Reviewed:
- Description: The increasing adoption of driverless cars already providing a shift to move away from traditional transportation systems to automated ones in many industrial and commercial applications. Recent research has justified that driverless vehicles will considerably reduce traffic congestions, accidents, carbon emissions, and enhance the accessibility of driving to wider cross-section of people and lifestyle choices. However, at present, people's main concerns are about its privacy and security. Since traditional protocol layers based security mechanisms are not so effective for a distributed system, trust value-based security mechanisms, a type of pervasive security, are appearing as popular and promising techniques. A few statistical non-learning based models for measuring the trust level of a driverless are available in the current literature. These are not so effective because of not being able to capture the extremely distributed, dynamic, and complex nature of the traffic systems. To bridge this research gap, in this paper, for the first time, we propose two deep learning-based models that measure the trustworthiness of a driverless car and its major On-Board Unit (OBU) components. The second model also determines its OBU components that were breached during the driving operation. Results produced using real and simulated traffic data demonstrate that our proposed DNN based deep learning models outperform other machine learning models in assessing the trustworthiness of individual car as well as its OBU components. The average precision of detection accuracies for the car, LiDAR, camera, and radar are 0.99, 0.96, 0.81, and 0.83, respectively, which indicates the potential real-life application of our models in assessing the trust level of a driverless car. © 2000-2011 IEEE.
Design of subsea cables/umbilicals for in-service abrasion - part 1 : case studies
- Authors: Reda, Ahmed , Thiedeman, James , Elgazzar, Mohamed , Shahin, Mohamed , Sultan, Ibrahim , McKee, Kristoffer
- Date: 2021
- Type: Text , Journal article
- Relation: Ocean Engineering Vol. 234, no. (2021), p.
- Full Text:
- Reviewed:
- Description: Submarine cables play a vital role in a myriad of industries around the globe, including power transmission and communication. Failure of submarine cables can have significant economic and technical implications worldwide. Current design methods for submarine cables focus on the ultimate limit states that address the cables structural integrity and on-bottom stability. However, abrasion of the outer protective layers (i.e. yarn and extruded sheaths) can progressively lead to damage and failure of submarine cables when the integrity of the armour sheathing is compromised. This paper documents several case studies of severe abrasion of submarine cables/umbilicals and undertaken corrective measures. The paper also presents some guidelines to be considered in the design process of submarine cables concerning abrasion. The findings of this paper suggest that abrasion should be considered a limit state that must be addressed in the design process of submarine cables and umbilicals. A detailed analysis of the underlying abrasion failure mechanisms is presented and explained in a companion paper (i.e., Part II: Mechanisms). © 2021 Elsevier Ltd
Design of subsea cables/umbilicals for in-service abrasion - part 2 : mechanisms
- Authors: Reda, Ahmed , Elgazzar, Mohamed , Thiedeman, James , McKee, Kristoffer , Sultan, Ibrahim , Shahin, Mohamed
- Date: 2021
- Type: Text , Journal article
- Relation: Ocean Engineering Vol. 234, no. (2021), p.
- Full Text:
- Reviewed:
- Description: This paper is the second of two companion papers about the design of subsea cables/umbilicals for in-service abrasion. Several case studies of severe abrasion of submarine cables/umbilicals and corrective measures undertaken have been documented and presented in the first paper (Part I: Case Studies). The mechanisms of failure due to abrasion are explained in this paper. The effect of repeated lateral movement on LLDPE (linear low-density polyethylene) extruded outer sheaths of two cable samples was investigated. In the first test, a cable sample was displaced the equivalent of 12 km over a crushed mineral aggregate while in the second test, a cable was subjected to 3 km of displacement under conditions that replicated the touchdown point of a dynamic cable. The results of the first test indicated that the overall abrasion was low and acceptable. In the second test however, the outer sheath was completely worn through. The authors recommend the thickness of the outer sheath be increased for cables where uniform abrasion is expected, and high abrasion protection units be employed where localized abrasion is expected. Empirical data is provided to support these recommendations. © 2021 Elsevier Ltd
Detection and verification of tropical cyclones and depressions over the South Pacific Ocean basin using ERA-5 reanalysis dataset
- Authors: Yeasmin, Alea , Chand, Savin , Turville, Christopher , Sultanova, Nargiz
- Date: 2021
- Type: Text , Journal article
- Relation: International Journal of Climatology Vol. 41, no. 11 (2021), p. 5318-5330
- Full Text: false
- Reviewed:
- Description: Tropical cyclones (TCs) are one of the most destructive synoptic systems and can cause enormous loss of life and property damages in the South Pacific island nations. The impact of tropical depressions (TDs, i.e. weaker systems that do not develop into TCs) can also be staggering in the region in terms of heavy flooding and landslides, but a lack of complete records often hinders research involving TD impacts. A methodology has been developed here to detect TDs in the ERA-5 reanalysis dataset (the fifth generation ECMWF atmospheric reanalysis of the global climate) using the Okubo–Weiss–Zeta parameter (OWZP) detection scheme. The new South Pacific Enhanced Archive for Tropical Cyclones dataset (SPEArTC), the Dvorak analysis of satellite-based cloud patterns over the South Pacific Ocean basin, and a rainfall dataset for various stations and historical archives have been utilized to validate ERA5-derived TCs and TDs for the period between 1979 and 2019. Results indicate that the OWZP method shows substantial skill in capturing the realistic climatological distribution of TDs (as well as TCs) for the South Pacific Ocean in the ERA5 reanalysis, paving a way forward for future climatological studies involving the impacts of TCs and TDs over the island nations using longer-term reanalyses products such as the 20th-century reanalysis dataset that extends back to the 1850s. © 2021 Royal Meteorological Society
Developing a hybrid model of Jaya algorithm-based extreme gradient boosting machine to estimate blast-induced ground vibrations
- Authors: Zhou, Jian , Qiu, Yingui , Khandelwal, Manoj , Zhu, Shuangli , Zhang, Xiliang
- Date: 2021
- Type: Text , Journal article
- Relation: International Journal of Rock Mechanics and Mining Sciences Vol. 145, no. (2021), p.
- Full Text:
- Reviewed:
- Description: Blasting is still being considered to be one the most important applicable alternatives for conventional excavations. Ground vibration generated due to blasting is an undesirable phenomenon which is harmful for the nearby structures and should be prevented. In this regard, a novel intelligent approach for predicting blast-induced PPV was developed. The distinctive Jaya algorithm and high efficient extreme gradient boosting machine (XGBoost) were applied to obtain the goal, called the Jaya-XGBoost model. Accordingly, 150 sets of data composed of 13 controllable and uncontrollable parameters are chosen as input independent variables and the measured peak particle velocity (PPV) is chosen as an output dependent variable. Also, the Jaya algorithm was used for optimization of hyper-parameters of XGBoost. Additionally, six empirical models and several machine learning models such as XGBoost, random forest, AdaBoost, artificial neural network and Bagging were also considered and applied for comparison of the proposed Jaya-XGBoost model. Accuracy criteria including determination coefficient (R2), root-mean-square error (RMSE), mean absolute error (MAE), and the variance accounted for (VAF) were used for the assessment of models. For this study, 150 blasting operations were analyzed. Also, the Shapley Additive Explanations (SHAP) method is used to interpret the importance of features and their contribution to PPV prediction. Findings reveal that the proposed Jaya-XGBoost emerged as the most reliable model in contrast to other machine learning models and traditional empirical models. This study may be helpful to mining researchers and engineers who use intelligent machine learning algorithms to predict blast-induced ground vibration. © 2021 Elsevier Ltd
Estimation of the TBM advance rate under hard rock conditions using XGBoost and Bayesian optimization
- Authors: Zhou, Jian , Qiu, Yingui , Zhu, Shuangli , Armaghani, Danial , Khandelwal, Manoj , Mohamad, Edy
- Date: 2021
- Type: Text , Journal article
- Relation: Underground Space Vol. 6, no. 5 (Oct 2021), p. 506-515
- Full Text:
- Reviewed:
- Description: The advance rate (AR) of a tunnel boring machine (TBM) under hard rock conditions is a key parameter in the successful implementation of tunneling engineering. In this study, we improved the accuracy of prediction models by employing a hybrid model of extreme gradient boosting (XGBoost) with Bayesian optimization (BO) to model the TBM AR. To develop the proposed models, 1286 sets of data were collected from the Peng Selangor Raw Water Transfer tunnel project in Malaysia. The database consists of rock mass and intact rock features, including rock mass rating, rock quality designation, weathered zone, uniaxial compressive strength, and Brazilian tensile strength. Machine specifications, including revolution per minute and thrust force, were considered to predict the TBM AR. The accuracies of the predictive models were examined using the root mean squares error (RMSE) and the coefficient of determination (R-2) between the observed and predicted yield by employing a five-fold cross-validation procedure. Results showed that the BO algorithm can capture better hyper-parameters for the XGBoost prediction model than can the default XGBoost model. The robustness and generalization of the BO-XGBoost model yielded prominent results with RMSE and R-2 values of 0.0967 and 0.9806 (for the testing phase), respectively. The results demonstrated the merits of the proposed BO-XGBoost model. In addition, variable importance through mutual information tests was applied to interpret the XGBoost model and demonstrated that machine parameters have the greatest impact as compared to rock mass and material properties.
Failure analysis of articulated paddings at crossing interface between crossing cable and crossed pipeline
- Authors: Reda, Ahmed , Elgazzar, Mohamed , Sultan, Ibrahim , Shahin, Mohamed , McKee, Kristoffer
- Date: 2021
- Type: Text , Journal article
- Relation: Applied Ocean Research Vol. 115, no. (2021), p.
- Full Text: false
- Reviewed:
- Description: While subsea crossings are undesirable for many reasons, they are unavoidable due to the sheer density of subsea assets. The use of articulated paddings is a cost-effective and practical method to achieve the required vertical separation between the crossing and the crossed pipelines or cables, though, not without limitations. In this paper, the failure of articulated padding at several points along a subsea cable in operation was investigated. The articulated padding has experienced partial fractures at numerous crossing locations and in some places has fallen off the cable completely. A complete failure mode analysis was conducted where several possible modes of failure were considered in detail. In-place finite element (FE) analyses of the articulated padding components and the corresponding environment were also performed. The FE modelling concluded that the original design loads were significantly lower than the expected worst-case load scenarios. To replicate the failure mode, two abrasion tests were also conducted and the results of which were studied. It was concluded that the predominant failure mode (partial fracture to the articulated padding discs) was likely a combination of the increased dynamic loads, excessive lateral movement causing unexpected levels of fretting, unbalanced free span causing unexpected stress concentration factors and reduction in material mechanical properties. All above factors have contributed to the root cause of the system failure and instigated the predominant mode of failure “partial fracture”. © 2021
Improving expansive clay subgrades using recycled glass : resilient modulus characteristics and pavement performance
- Authors: Yaghoubi, Ehsan , Yaghoubi, Mohammadjavad , Guerrieri, Maurice , Sudarsanan, Nithin
- Date: 2021
- Type: Text , Journal article
- Relation: Construction and Building Materials Vol. 302, no. (2021), p.
- Full Text: false
- Reviewed:
- Description: The scarcity of sound soils, especially in urban areas, often forces engineers to construct the pavement on problematic subgrade soils such as expansive clays. The associated cost involved in replacing the existing problematic soil is avoided by adopting treatment techniques. In this study, a type of high plasticity expansive clay was mixed with 10, 20, and 30% sand-size recycled glass (RG) as a non-chemical soil treatment approach. An extensive investigation comprising experimental works, numerical modeling, and pavement performance analysis was undertaken. After determination of the physical properties of clay and RG, resilient modulus characteristics of clay and the three clay-RG mixtures were carried out through an experimental program. Subsequently, the obtained resilient modulus data sets were incorporated into a finite element analysis program in order to analyze the stress-strain response of pavement models founded on clay and RG-treated subgrades. The compressive and tensile strains achieved through the analysis of the pavement models under traffic loads were next used to compare each pavement model with respect to fatigue and rutting performances. The experimental results showed up to a 113% increase in resilient modulus of clay by the addition of 30% RG. The outcomes of the analysis on pavement systems modeled using the experimental input showed a considerable reduction in compressive and tensile strains by treating the clay subgrade with RG. Consequently, the strain reduction exhibited a significant increase in fatigue life and rutting life of pavements founded on RG treated clay subgrades. The outcomes of this research aim to encourage the construction industry to consider the utilization of environmentally clean recycled aggregates, such as RG, for improving subgrades with problematic soils and hence, promote sustainable construction materials and approaches. © 2021 Elsevier Ltd
Intelligent modeling of blast-induced rock movement prediction using dimensional analysis and optimized artificial neural network technique
- Authors: Yu, Zhi , Shi, Xiaohu , Miao, Xiaohu , Zhou, Jian , Khandelwal, Manoj
- Date: 2021
- Type: Text , Journal article
- Relation: International Journal of Rock Mechanics and Mining Sciences Vol. 143, no. (2021), p.
- Full Text:
- Reviewed:
- Description: For maximum metal recovery, considering the movement of ore and waste during the blasting process in loading design is meaningful for reducing ore loss and ore dilution in an open-pit mine. The blast-induced rock movement (BIRM) can be directly measured; nevertheless, it is time-consuming and relative expensive. To solve this problem, a novel intelligent prediction model was proposed by using dimensional analysis and optimized artificial neural network technique in this paper based on the BIRM monitoring test in Husab Uranium Mine, Namibia and Phoenix Mine, USA. After using dimensional analysis, five input variables and one output variable were determined with both considering the dimension and physical meaning of each dimensionless variable. Then, artificial neural network technique (ANN) technique was utilized to develop an accurate prediction model, and a metaheuristic algorithm namely the Equilibrium Optimizer (EO) algorithm was applied to search the optimal hyper-parameter combination. For comparison aims, a linear model and a non-linear regression model were also performed, and the comparison results show that the provided hybrid ANN-based model can yield better prediction performance. As a result, it can be concluded that the developed intelligent model in this article has the potential to predict BIRM during bench blasting, and the analysis method and modeling process in this paper can provide a reference for solving other engineering problems. © 2021 Elsevier Ltd. **Please note that there are multiple authors for this article therefore only the name of the first 5 including Federation University Australia affiliate “Manoj Khandelwal” is provided in this record**
Low amplitude fatigue performance of sandstone, marble, and granite under high static stress
- Authors: Du, Kun , Su, Rui , Zhou, Jian , Wang, Shaofeng , Khandelwal, Manoj
- Date: 2021
- Type: Text , Journal article
- Relation: Geomechanics and Geophysics for Geo-Energy and Geo-Resources Vol. 7, no. 3 (2021), p.
- Full Text:
- Reviewed:
- Description: Abstract: Fatigue tests under high static pre-stress loads can provide meaningful results to better understand the time-dependent failure characteristics of rock and rock-like materials. However, fatigue tests under high static pre-stress loads are rarely reported in previous literature. In this study, the rock specimens were loaded with a high static pre-stress representing 70% and 80% of the uniaxial compressive strength (UCS), and cyclic fatigue loads with a low amplitude (i.e., 5%, 7.5% and 10% of the UCS) were applied. The results demonstrate that the fatigue life decreased as the static pre-stress level or amplitude of fatigue loads increased for different rock types. The high static pre-stress affected the fatigue life greatly when the static pre-stress was larger than the damage stress of rocks in uniaxial compression tests. The accumulative fatigue damage exhibited three stages during the fatigue failure process, i.e., crack initiation, uniform velocity, and acceleration, and the fatigue modulus showed an “S-type” change trend. The lateral and volumetric strains had a much higher sensitivity to the cyclic loading and could be used to predict fatigue failure characteristics. It was observed that volumetric strain εv = 0 is a threshold for microcracks coalescence and is an important value for estimating the fatigue life. Article highlights: Fatigue mechanical performance of high static pre-stressed rocks were evaluated.The results demonstrate that the fatigue life decreased as the static pre-stress level increased and the static pre-stress affected the fatigue life more than the amplitude of fatigue loads.The volumetric strain of zero before fatigue loading is a threshold for fatigue failure of rocks under high static stress. © 2021, The Author(s), under exclusive licence to Springer Nature Switzerland AG. **Please note that there are multiple authors for this article therefore only the name of the first 5 including Federation University Australia affiliate “Manoj Khandelwal” is provided in this record**
Maximising the efficiency of Menard pressuremeter testing in cohesive materials by a cookie-cutter drilling technique
- Authors: Tolooiyan, Ali , Dyson, Ashley , Karami, Mojtaba , Shaghaghi, Tahereh , Ghadrdan, Mohsen , Tang, Zhan
- Date: 2021
- Type: Text , Journal article
- Relation: Engineering Geology Vol. 287, no. (Jun 2021), p. 106096
- Full Text: false
- Reviewed:
- Description: Menard pressuremeter testing has been widely used in geotechnical engineering applications for 40 years and is an important technique in determining in-situ horizontal stress distributions. In this study, Menard pressuremeter testing is combined with a "cookie-cutter" insertion technique to determine horizontal stresses for a soft-rock in an operational Australian mine. The method presents an alternative to the Self-Bored Pressuremeter, with cookie-cutter drill rods allowing for sample recovery and further laboratory testing. The method accommodates for the presence of gravel and hard layered materials that present a risk of damage to cutting shoes of Self-Bored Pressuremeter devices. The combination of a sonic drill rig, coupled with the cookie cutter rods produces a close tolerance pocket resulting in "pseudo self boring pressuremeter tests". The undrained shear strength, unload-reload shear modulus and in-situ horizontal stress are presented from pressuremeter tests conducted in the region for the first time. The undrained shear strength was observed in the range of 0.47-0.57 MPa, the unload-reload shear modulus between 17.43 and 18.25 MPa, the lift-off pressure in the range of 0.35-0.61 MPa. The K-0 of coal was equal to 1, with interseam materials ranging from 2.1 to 3.5. Results of the cookie-cutter insertion method are compared with conventional drilling methods, with the cookie-cutter insertion test providing results in good agreement with both advanced triaxial laboratory tests and FEM numerical analysis. Cookie-cutter pressuremeter tests were conducted on cohesive soils at Australia's second-largest open-pit mine, with pressuremeter test results presented for Victorian brown coal for the first time.
Numerical study on the compression-bending response of grouted connections in offshore structures
- Authors: Chen, Tao , Fang, Qi , Zhang, Chihai , Li, Weichao , Xiao, Zhigang
- Date: 2021
- Type: Text , Journal article
- Relation: Journal of Constructional Steel Research Vol. 185, no. (2021), p.
- Full Text: false
- Reviewed:
- Description: Grouted connections (GCs) are widely used to link the substructures and towers for offshore wind turbines and transfer various loads from the turbine and tower to the substructure. Extremely complex stresses are developed in these composite connections formed with steel pipes and grout fillings. This paper presents a numerical study, using ABAQUS, on the mechanical response of GCs under axial compression coupled with bending. The effect of axial load ratio on the moment-rotation responses, contact stress between steel pipes and grout, as well as compositions of flexural capacity. Further study is conducted on the stress distribution in the grouted connection under axial compression coupled with bending, and the stress distribution was derived with python script. Analysis shows that, axial load ratio plays a non-negligible role and the distribution of contact stress differs from that recommended by current design guide. These should be considered with caution in industrial design. In addition, this paper also proposes a method to derive the contact stress from numerical model constructed with Abaqus. The results show that contact pressure and shear keys provide the main components for the flexural capacity and the sum of Mp and Mshear-keys accounts for about 75% of the total capacity. And the values of the non-uniform coefficient of shear keys ηsk are all larger than 0.75 which shows that the force distribution between shear keys is extremely uneven. The outputs of this paper provide a further understanding of the GCs subject axial compression coupled with bending. © 2021 Elsevier Ltd
Robust modelling of implicit interfaces by the scaled boundary finite element method
- Authors: Dsouza, Shaima , Pramod, A. L. N. , Ooi, Ean Tat , Song, Chongming , Natarajan, Sundararajan
- Date: 2021
- Type: Text , Journal article
- Relation: Engineering Analysis with Boundary Elements Vol. 124, no. (2021), p. 266-286
- Full Text: false
- Reviewed:
- Description: In this paper, we propose a robust framework based on the scaled boundary finite element method to model implicit interfaces in two-dimensional differential equations in nonhomegeneous media. The salient features of the proposed work are: (a) interfaces can be implicitly defined and need not conform to the background mesh; (b) Dirichlet boundary conditions can be imposed directly along the interface; (c) does not require special numerical integration technique to compute the bilinear and the linear forms and (d) can work with an efficient local mesh refinement using hierarchical background meshes. Numerical examples involving straight interface, circular interface and moving interface problems are solved to validate the proposed technique. Further, the presented technique is compared with conforming finite element method in terms of accuracy and convergence. From the numerical studies, it is seen that the proposed framework yields solutions whose error is O(h2) in L2 norm and O(h) in the H1 semi-norm. Further the condition number increases with the mesh size similar to the FEM. © 2021 Elsevier Ltd
Slope stability analysis using deterministic and probabilistic approaches for poorly defined stratigraphies
- Authors: Ghadrdan, Moshen , Dyson, Ashley , Shaghaghi, Tahereh , Tolooiyan, Ali
- Date: 2021
- Type: Text , Journal article
- Relation: Geomechanics and Geophysics for Geo-Energy and Geo-Resources Vol. 7, no. 1 (2021), p.
- Full Text: false
- Reviewed:
- Description: The stability of slopes directly affects human lives, the environment, and the economy. Inaccurate geological profiles within numerical slope stability models can lead to potentially catastrophic consequences when model conditions do not appropriately reflect real-life stratigraphy. In cases where localised deposits are prevalent, probabilistic methods are often necessary to accommodate for unknown or poorly defined stratigraphies. Currently, there are no commercial geotechnical software packages that simulate probabilistic constitutive behaviour of materials within finite element methods for large-scale stratigraphic analysis. Instead, commercially available probabilistic methods such as the random limit equilibrium method are incapable of incorporating non-linear constitutive soil behaviour. For this reason, advanced constitutive models are seldom coupled with probabilistically varying soil layers or spatially variable soil parameters. The objective of this research is the implementation of a simplified method for probabilistic stratigraphic analysis within a commercially available FE environment, providing a technique to assess the effects of stratigraphic uncertainty on slope stability. The proposed method is presented, highlighting the impact of localised thin layers of soft material as well as their frequency and location on the slope of an operational open-pit mine. The significance of these stratigraphic effects is presented through a case study of Australia’s second-largest open-pit mine, at which the stability of a collapsed coal slope is analysed. To improve the reliability of the finite element method for slope stability assessment, the Monte Carlo approach has been incorporated to consider varying shear strength distributions for models incorporating advanced constitutive behaviour. Thicker probabilistically generated deposits of silty material resulted in increased slope Factors of Safety. Similarly, greater proportions of silty deposits within a predominantly clayey interseam produced larger safety factors than slopes without localised thin silty layers. Stratigraphic analysis indicated that the Factor of Safety was most sensitive to localised silt layers at depths greater than 83 m below ground level. © 2020, Springer Nature Switzerland AG.
SPEED: A deep learning assisted privacy-preserved framework for intelligent transportation systems
- Authors: Usman, Muhammad , Jan, Mian , Jolfaei, Alireza
- Date: 2021
- Type: Text , Journal article
- Relation: IEEE Transactions on Intelligent Transportation Systems Vol. 22, no. 7 (2021), p. 4376-4384
- Full Text: false
- Reviewed:
- Description: Roadside cameras in an Intelligent Transportation System (ITS) are used for various purposes, e.g., monitoring the speed of vehicles, violations of laws, and detection of suspicious activities in parking lots, streets, and side roads. These cameras generate big multimedia data, and as a result, the ITS faces challenges like data management, redundancy, and privacy breaching in end-to-end communication. To solve these challenges, we propose a framework, called SPEED, based on a multi-level edge computing architecture and machine learning algorithms. In this framework, data captured by end-devices, e.g., smart cameras, is distributed among multiple Level-One Edge Devices (LOEDs) to deal with data management issue and minimize packet drop due to buffer overflowing on end-devices and LOEDs. The data is forwarded from LOEDs to Level-Two Edge Devices (LTEDs) in a compressed sensed format. The LTEDs use an online Least-Squares Support-Vector Machines (LS-SVMs) model to determine distribution characteristics and index values of compressed sensed data to preserve its privacy during transmission between LTEDs and High-Level Edge Devices (HLEDs). The HLEDs estimate the redundancy in forwarded data using a deep learning architecture, i.e., a Convolutional Neural Network (CNN). The CNN is used to detect the presence of moving objects in the forwarded data. If a movement is detected, the data is forwarded to cloud servers for further analysis otherwise discarded. Experimental results show that the use of a multi-level edge computing architecture helps in managing the generated data. The machine learning algorithms help in addressing issues like data redundancy and privacy-preserving in end-to-end communication. © 2000-2011 IEEE.
Stress–strain relationship of sandstone under confining pressure with repetitive impact
- Authors: Wang, Shiming , Xiong, Xianrui , Liu, Yunsi , Zhou, Jian , Khandelwal, Manoj
- Date: 2021
- Type: Text , Journal article
- Relation: Geomechanics and Geophysics for Geo-Energy and Geo-Resources Vol. 7, no. 2 (2021), p.
- Full Text:
- Reviewed:
- Description: Abstract: A series of triaxial repetitive impact tests were conducted on a 50-mm-diameter split Hopkinson pressure bar testing device to reveal the characteristics of dynamic stress–strain of sandstone under confining pressure, and the confining pressure in this study was set as 5 and 10 MPa. The results showed that sandstone is very sensitive to confining pressure and strain rate. As the confining pressure and strain rate increases, the dynamic strength, critical strain and absorbed energy also increases, however with the increases in number of impacts, they decrease. With impact numbers increases, the stress–strain curve of sandstone gradually transits from a Class I to a Class II. The dynamic statistical damage constitutive model used in the paper can describe the dynamic response of sandstone under confining pressure with repetitive impact. Various influencing factors, such as material characteristics, confining pressure, strain rate and damage on the dynamic mechanical behavior of sandstone are also fully considered in the model. The damage curve changes from concave to convex as the F/ F increase. When the F/ F exceed 0.5, the damage curve appears convex, and the damage is obvious. By comparing with the variation of the reflected wave waveform with the impact numbers, it is found that damage evolution law of the rock under confining pressure with the impact numbers is similar to that of the reflected wave waveform with the impact numbers, can reflect the damage degree of the rock specimen without other auxiliary equipment, which has been verified. Article Highlights: The stress-strain curve of sandstone under confining pressure with repeated impact changes from Class I to Class II, and it will become less obvious as the confining pressure increases.The constitutive model used in the article can well describe the dynamic mechanical properties, strain rate effect and its turning point of rock under confining pressure with repeated impact.The damage curve changes from concave to convex, and the damage evolution law is similar to that of the reflected wave waveform with the impact numbers. © 2021, The Author(s), under exclusive licence to Springer Nature Switzerland AG. **Please note that there are multiple authors for this article therefore only the name of the first 5 including Federation University Australia affiliate “Manoj Khandelwal” is provided in this record**
Temperature and duration impact on the strength development of geopolymerized granulated blast furnace slag for usage as a construction material
- Authors: Arulrajah, Arul , Maghool, Farshid , Yaghoubi, Mohammadjavad , Phetchuay, Chayakrit , Horpibulsuk, Suksun
- Date: 2021
- Type: Text , Journal article
- Relation: Journal of Materials in Civil Engineering Vol. 33, no. 2 (2021), p.
- Full Text: false
- Reviewed:
- Description: Through the process of extracting iron from iron ore, a by-product is generated known as granulated blast furnace slag (GBFS). Traditional stabilization methods such as cement stabilization are not entirely sustainable options. This research investigates the engineering properties of geopolymer-stabilized GBFS and their viability for usage as a construction material. A combination of sodium hydroxide (NaOH) and sodium silicate (Na2SiO3) was used as the liquid alkaline activator (L) along with low-carbon pozzolanic binders, namely, fly ash (FA) and slag (S). The L was prepared with a Na2SiO3:NaOH ratio of 70 30 and binders were added up to 30%. The effect of different curing regimes on the strength of geopolymerized GBFS was evaluated using scanning electron microscopy (SEM) and unconfined compressive strength (UCS) tests. The effect of both the temperature and duration of curing had a vital role in the strength development of the mixtures. The test results indicated that the combination of FA+S as a geopolymer binder could perform better than FA or S alone. With the lowest UCS value of 7.8 MPa and highest value of 43 MPa, all the geopolymer-stabilized GBFS were found to be suitable for a variety of civil and construction applications. © 2020 American Society of Civil Engineers.
The effects of initial static deviatoric stress on liquefaction and pre-failure deformation characteristics of saturated sand under cyclic loading
- Authors: Liu, Zhiyong , Qian, Jiangu , Yaghoubi, Mohammadjavad , Xue, Jianfeng
- Date: 2021
- Type: Text , Journal article
- Relation: Soil Dynamics and Earthquake Engineering Vol. 149, no. (2021), p.
- Full Text: false
- Reviewed:
- Description: Initial static deviatoric stress can affect the liquefaction behaviour and deformation characteristics of saturated sand under cyclic loading. A series of cyclic triaxial tests were performed on a saturated sand consolidated under a constant vertical stress but different lateral stresses. The dependence of failure mechanism, liquefaction resistance, and stiffness evolution of the sand on initial static deviatoric stress ratio was investigated on medium dense to dense sand samples (Dr = 0.44 to 0.82). The pre-failure deformation and the direction of strain accumulation were analysed under different static deviatoric stress ratios. The results indicate that under cyclic loading with stress reversal condition, the effective mean stress in the samples could reduce to zero, which leads to cyclic mobility failure and completely loss of stiffness due to liquefaction. The stress state after cyclic loading could be above critical state line. Under non reversal loading condition, however, the effective mean stress cannot reduce to zero and therefore the samples fail under shear due to large strain accumulation. In this condition, the stress state at the end of the cyclic loading is approximately at the critical state line and hardly affected by initial deviatoric stress. The failure resistance of medium dense to dense sand is not greatly affected by initial static deviatoric stress until it is large enough to meet non-reverse loading condition. The ratio of axial stain to excess pore water pressure accumulation (or Δϵacc a/Δϵacc v) increases with the average static deviatoric stress ratio ηav. The strain accumulation direction roughly follows the flow rule of Modified Cam Clay model, independent of relative density and failure mechanism. © 2021 Elsevier Ltd
Vehicle trajectory clustering based on dynamic representation learning of internet of vehicles
- Authors: Wang, Wei , Xia, Feng , Nie, Hansong , Chen, Zhikui , Gong, Zhiguo
- Date: 2021
- Type: Text , Journal article
- Relation: IEEE Transactions on Intelligent Transportation Systems Vol. 22, no. 6 (2021), p. 3567-3576
- Full Text:
- Reviewed:
- Description: With the widely used Internet of Things, 5G, and smart city technologies, we are able to acquire a variety of vehicle trajectory data. These trajectory data are of great significance which can be used to extract relevant information in order to, for instance, calculate the optimal path from one position to another, detect abnormal behavior, monitor the traffic flow in a city, and predict the next position of an object. One of the key technology is to cluster vehicle trajectory. However, existing methods mainly rely on manually designed metrics which may lead to biased results. Meanwhile, the large scale of vehicle trajectory data has become a challenge because calculating these manually designed metrics will cost more time and space. To address these challenges, we propose to employ network representation learning to achieve accurate vehicle trajectory clustering. Specifically, we first construct the k-nearest neighbor-based internet of vehicles in a dynamic manner. Then we learn the low-dimensional representations of vehicles by performing dynamic network representation learning on the constructed network. Finally, using the learned vehicle vectors, vehicle trajectories are clustered with machine learning methods. Experimental results on the real-word dataset show that our method achieves the best performance compared against baseline methods. © 2000-2011 IEEE. **Please note that there are multiple authors for this article therefore only the name of the first 5 including Federation University Australia affiliate “Feng Xia” is provided in this record**