• Geomechanics and Engineering
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• 제19권2호
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• pp.127-139
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• 2019

Available methods to determine the ultimate bearing capacity of shallow foundations may not be accurate enough owing to the complicated failure mechanism and diversity of the underlying soils. Accordingly, applying new methods of artificial intelligence can improve the prediction of the ultimate bearing capacity. The M5' model tree and the genetic programming are two robust artificial intelligence methods used for prediction purposes. The model tree is able to categorize the data and present linear models while genetic programming can give nonlinear models. In this study, a combination of these methods, called the M5'-GP approach, is employed to predict the ultimate bearing capacity of the shallow foundations, so that the advantages of both methods are exploited, simultaneously. Factors governing the bearing capacity of the shallow foundations, including width of the foundation (B), embedment depth of the foundation (D), length of the foundation (L), effective unit weight of the soil (${\gamma}$) and internal friction angle of the soil (${\varphi}$) are considered for modeling. To develop the new model, experimental data of large and small-scale tests were collected from the literature. Evaluation of the new model by statistical indices reveals its better performance in contrast to both traditional and recent approaches. Moreover, sensitivity analysis of the proposed model indicates the significance of various predictors. Additionally, it is inferred that the new model compares favorably with different models presented by various researchers based on a comprehensive ranking system.

• Advances in robotics research
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• 제1권1호
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• pp.101-126
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• 2014

In this article we present modular neural control for a leg-wheel hybrid robot consisting of three legs with omnidirectional wheels. This neural control has four main modules having their functional origin in biological neural systems. A minimal recurrent control (MRC) module is for sensory signal processing and state memorization. Its outputs drive two front wheels while the rear wheel is controlled through a velocity regulating network (VRN) module. In parallel, a neural oscillator network module serves as a central pattern generator (CPG) controls leg movements for sidestepping. Stepping directions are achieved by a phase switching network (PSN) module. The combination of these modules generates various locomotion patterns and a reactive obstacle avoidance behavior. The behavior is driven by sensor inputs, to which additional neural preprocessing networks are applied. The complete neural circuitry is developed and tested using a physics simulation environment. This study verifies that the neural modules can serve a general purpose regardless of the robot's specific embodiment. We also believe that our neural modules can be important components for locomotion generation in other complex robotic systems or they can serve as useful modules for other module-based neural control applications.

• 태양에너지
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• 제20권1호
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• pp.97-108
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• 2000

The purpose of this study is to minimize the heat transfer surface area and cold fluid exit temperature of heat exchanger which applied to the refrigeration and air-conditioning system by utilizing the thermoelectric principle. Both uniform and non-uniform current distribution methods which applied to the analysis of the TE elements that incorporates heat exchanger were investigated. The non-uniform current distribution method had the better coefficient of performance and had the lower cold fluid exit temperature of the TE cooling system than the uniform current distribution method. It was found that if a TE cooling system incorporates a heat exchanger, a non-uniform current distribution should guarantee to the lowest cold fluid exit temperature. Also, the hybrid method (combination of the uniform and non-uniform current distribution method) is investigated to achieve the best results by combining the uniform and non-uniform current distributions. The results show that it can lower the cold fluid exit temperature and reduce the heat transfer surface area for the parallel flow arrangement if we apply the constant current in some entry region and the non-uniform increasing current in the direction of the cold fluid flow afterwards.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제15권7호
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• pp.2399-2413
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• 2021

A recommendation system is an information filter tool, which uses the ratings and reviews of users to generate a personalized recommendation service for users. However, the cold-start problem of users and items is still a major research hotspot on service recommendations. To address this challenge, this paper proposes a high-efficient hybrid recommendation system based on Fuzzy C-Means (FCM) clustering and supervised learning models. The proposed recommendation method includes two aspects: on the one hand, FCM clustering technique has been applied to the item-based collaborative filtering framework to solve the cold start problem; on the other hand, the content information is integrated into the collaborative filtering. The algorithm constructs the user and item membership degree feature vector, and adopts the data representation form of the scoring matrix to the supervised learning algorithm, as well as by combining the subjective membership degree feature vector and the objective membership degree feature vector in a linear combination, the prediction accuracy is significantly improved on the public datasets with different sparsity. The efficiency of the proposed system is illustrated by conducting several experiments on MovieLens dataset.

• International Journal of Naval Architecture and Ocean Engineering
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• 제13권1호
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• pp.321-339
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• 2021

Response Surface Method (RSM) has been widely used for flammable cloud size prediction as it can reduce computational intensity for further Explosion Risk Analysis (ERA) especially during the early design phase of offshore platforms. However, RSM encounters the overfitting problem under very limited simulations. In order to overcome the disadvantage of RSM, Bayesian Regularization Artificial Neural (BRANN)-based model has been recently developed and its robustness and efficiency have been widely verified. However, for ERA during the early design phase, there seems to be room to further reduce the computational intensity while ensuring the model's acceptable accuracy. This study aims to develop an integrated method, namely the combination of Center Composite Design (CCD) method with Bayesian Regularization Artificial Neural Network (BRANN), for flammable cloud size prediction. A case study with constant and transient leakages is conducted to illustrate the feasibility and advantage of this hybrid method. Additionally, the performance of CCD-BRANN is compared with that of RSM. It is concluded that the newly developed hybrid method is more robust and computational efficient for ERAs during early design phase.

• 센서학회지
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• 제30권6호
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• pp.376-380
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• 2021

The accurate detection of hydrogen gas molecules is considered to be important for industrial safety. However, the selective detection of the gas using semiconductive metal oxides (SMOs)-based sensors is challenging. Here, we describe the fabrication of H₂ sensors in which a nanocellulose/graphene oxide (GO) hybrid membrane is attached to SnO₂ nanosheets (NSs). One-dimensional (1D) nanocellulose fibrils are attached to the surface of GO NSs (GONC membrane) by mixing GO and nanocellulose in a solution. The as-prepared GONC membrane is employed as a sacrificial template for SnO₂ NSs as well as a molecular sieving membrane for selective H₂ filtration. The combination of GONC membrane and SnO₂ NSs showed substantial selectivity to hydrogen gas (R_air / R_gas > 10 @ 0.8 % H₂, 100 ℃) with noise level responses to interfering gases (H₂S, CO, CH₃COCH₃, C₂H₅OH, and NO₂). These remarkable sensing results are attributed mainly to the molecular sieving effect of the GONC membrane. These results can facilitate the development of a highly selective H₂ detector using SMO sensors.

• Steel and Composite Structures
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• 제48권4호
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• pp.475-483
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• 2023

In this paper, a new energy-efficient semi-active hybrid bulk damper is developed that is cost-effective for use in structural applications. In this work, the possibility of active and semi-active component configurations combined with suitable control algorithms, especially vibration control methods, is explored. The equations of motion for a container bridge equipped with an MDOF Mass Tuned Damper (M-TMD) system are established, and the combination of excitation, adhesion, and control effects are performed by a proprietary package and commercial custom submodel software. Systematic methods for the synthesis of structural components and active systems have been used in many applications because of the main interest in designing efficient devices and high-performance structural systems. A rational strategy can be established by properly controlling the master injection frequency parameter. Simulation results show that the multiscale model approach is achieved and meets accuracy with high computational efficiency. The M-TMD system can significantly improve the overall response of constrained structures by modestly reducing the critical stress amplitude of the frame. This design can be believed to build affordable, safe, environmentally friendly, resilient, sustainable infrastructure and transportation.

• Structural Engineering and Mechanics
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• 제86권5호
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• pp.621-633
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• 2023

Stability analysis and support system estimation of the Beheshtabad water transmission tunnel is investigated in this research. A combination approach based on the rock mass rating (RMR) and rock mass quality index (Q) is used for this purpose. In the first step, 40 datasets related to the petrological, structural, hydrological, physical, and mechanical properties of tunnel host rocks are measured in the field and laboratory. Then, RMR, Q, and height of influenced zone above the tunnel roof are computed and sorted into five general groups to analyze the tunnel stability and determine its support system. Accordingly, tunnel stand-up time, rock load, and required support system are estimated for five sorted rock groups. In addition, various empirical relations between RMR and Q i.e., linear, exponential, logarithmic, and power functions are developed using the analysis of variance (ANOVA). Based on the significance level (sig.), determination coefficient (R²) and Fisher-test (F) indices, power and logarithmic equations are proposed as the optimum relations between RMR and Q. To validate the proposed relations, their results are compared with the results of previous similar equations by using the variance account for (VAF), root mean square error (RMSE), mean absolute percentage error (MAPE) and mean absolute error (MAE) indices. Comparison results showed that the accuracy of proposed RMR-Q relations is better than the previous similar relations and their outputs are more consistent with actual data. Therefore, they can be practically utilized in designing the tunneling projects with an acceptable level of accuracy and reliability.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제17권10호
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• pp.2701-2717
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• 2023

In this paper, a hybrid fuzzy-based method is suggested for determining India's best system for power generation. This suggested approach was created using a fuzzy-based combination of the Giza Pyramids Construction (GPC) and Recalling-Enhanced Recurrent Neural Network (RERNN). GPC is a meta-heuristic algorithm that deals with solutions for many groups of problems, whereas RERNN has selective memory properties. The evaluation of the current load requirements and production profile information system is the main objective of the suggested method. The Central Electricity Authority database, the Indian National Load Dispatch Centre, regional load dispatching centers, and annual reports of India were some of the sources used to compile the data regarding profiles of electricity loads, capacity factors, power plant generation, and transmission limits. The RERNN approach makes advantage of the ability to analyze the ideal power generation from energy data, however the optimization of RERNN factor necessitates the employment of a GPC technique. The proposed method was tested using MATLAB, and the findings indicate that it is effective in terms of accuracy, feasibility, and computing efficiency. The suggested hybrid system outperformed conventional models, achieving the top result of 93% accuracy with a shorter computation time of 6814 seconds.

• Wind and Structures
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• 제38권1호
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• pp.75-91
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• 2024

Reliable wind signal reconstruction can be beneficial to the operational safety of long-span bridges. Non-Gaussian characteristics of wind signals make the reconstruction process challenging. In this paper, non-Gaussian wind signals are converted into a combined prediction of two kinds of features, actual wind speeds and wind angles of attack. First, two decomposition techniques, empirical mode decomposition (EMD) and variational mode decomposition (VMD), are introduced to decompose wind signals into intrinsic mode functions (IMFs) to reduce the randomness of wind signals. Their principles and applicability are also discussed. Then, four artificial intelligence (AI) algorithms are utilized for wind signal reconstruction by combining the particle swarm optimization (PSO) algorithm with back propagation neural network (BPNN), support vector regression (SVR), long short-term memory (LSTM) and bidirectional long short-term memory (Bi-LSTM), respectively. Measured wind signals from a bridge site in a deep-cutting gorge are taken as experimental subjects. The results showed that the reconstruction error of high-frequency components of EMD is too large. On the contrary, VMD fully extracts the multiscale rules of the signal, reduces the component complexity. The combination of VMD-PSO-Bi-LSTM is demonstrated to be the most effective among all hybrid models.