New Approach for Sediment Yield Forecasting with a Two-Phase Feedforward Neuron Network-Particle Swarm Optimization Model Integrated with the Gravitational Search Algorithm

Water Resources Management - Predicting sediment yield is an important task for decision-makers in environmental monitoring and water management since the benefits of applying non-linear,...     

Simulation of Shrinkage Porosity Formation During Alloy Solidification

Metallurgical and Materials Transactions A - Porosity due to solidification shrinkage is a troublesome defect in metal casting. It results in low yields and increased costs in production and limits...     

An efficient image retrieval based on an integration of HSV,RLBP, and CENTRIST features using ensemble classifier learning

Multimedia Tools and Applications - Recently, we have been witnessing a tremendous rise in digital image quantities, which in return calls for an adjustment and management system to fulfill...     

Development of Nonlinear Model Based on Wavelet-ANFIS for Rainfall Forecasting at Klang Gates Dam

Rainfall is one of the most complicated effective hydrologic processes in runoff prediction and water management. Artificial neural networks (ANN) have been found efficient, particularly in problems where characteristics of the processes are stochastic and difficult to describe using explicit mathematical models. However, time series prediction based on ANN algorithms is fundamentally difficult and faces some other problems. For this purpose, one method that has been identified as a possible alternative for ANN in hydrology and water resources problems is the adaptive neuro-fuzzy inference system (ANFIS). Nevertheless, the data arising from the monitoring stations and experiment might be corrupted by noise signals owing to systematic and non-systematic errors. This noisy data often made the prediction task relatively difficult. Thus, in order to compensate for this augmented noise, the primary objective of this paper is to develop a technique that could enhance the accuracy of rainfall prediction. Therefore, the wavelet decomposition method is proposed to link to ANFIS and ANN models. In this paper, two scenarios are employed; in the first scenario, monthly rainfall value is imposed solely as an input in different time delays from the time (t) to the time (t-4) into ANN and ANFIS, second scenario uses the wavelet transform to eliminate the error and prepares sub-series as inputs in different time delays to the ANN and ANFIS. The four criteria as Root Mean Square Error (RMSE), Correlation Coefficient (R ²), Gamma coefficient (G), and Spearman Correlation Coefficient (ρ) are used to evaluate the proposed models. The results showed that the model based on wavelet decomposition conjoined with ANFIS could perform better than the ANN and ANFIS models individually.     

Multi-haul quasi network flow model for vertical alignment optimization

The vertical alignment optimization problem for road design aims to generate a vertical alignment of a new road with a minimum cost, while satisfying safety and design constraints. A new model called multi-haul quasi network flow (MH-QNF) for vertical alignment optimization is presented with the goal of improving the accuracy and reliability of previous mixed integer linear programming models. The performance of the new model is compared with two state-of-the-art models in the field: the complete transportation graph (CTG) and the quasi network flow (QNF) models. The numerical results show that, within a 1% relative error, the proposed model is robust and solves more than 93% of test problems compared to 82% for the CTG and none for the QNF. Moreover, the MH-QNF model solves the problems approximately eight times faster than the CTG model.     

Effect of the addition of oligo ester on the rheological properties of bitumen grade 80–100

Many problems have emerged in road construction due to insufficient adhesion and elasticity of asphalt layers as road surface. Adding polymer to bitumen can improve and modify asphalt properties. Oligo ester can be used as an additive to improve bitumen properties. Through reaction of recycled polyethylene terephthalate with dipropyleneglycol, oligo ester was produced during the glycolysis process. In this research, oligo ester was fabricated through glycolysis reaction. Then it was added to the bitumen grade 80–100 in 2 and 5 wt%. The end product was evaluated by standard test methods for ductility, softening point, penetration, and thermal behavior was measured by Thermal Gravimetric, Differential Thermal Gravimetric, and Differential Thermal Analysis. The results revealed that the addition of the above polymers to bitumen reduced penetration property and increased softening point and ductility. Also, thermal behavior of bitumen was modified.     

On braced trapezoidal corrugated steel shear panels: An experimental and numerical study

In this study, a new system consisting of a combination of braces and steel infill panels called the braced corrugated steel shear panel (BCSSP) is presented. To obtain the hysteretic behavior of the proposed system, the quasi-static cyclic performances of two experimental specimens were first evaluated. The finite element modeling method was then verified based on the obtained experimental results. Additional numerical evaluations were carried out to investigate the effects of different parameters on the system. Subsequently, a relationship was established to estimate the buckling shear strength of the system without considering residual stresses. The results obtained from the parametric study indicate that the corrugated steel shear panel (CSSP) with the specifications of a = 30 mm, t = 2 mm, and θ = 90° had the highest energy dissipation capacity and ultimate strength while the CSSP with the specifications of a = 30 mm, t = 2 mm, and θ = 30° had the highest initial stiffness. It can thus be concluded that the latter CSSP has the best structural performance and that increasing the number of corrugations, corrugation angle, and plate thickness and decreasing the sub-panel width generally enhance the performance of CSSPs in terms of the stability of their hysteretic behaviors.     

Brittleness of rock and stability assessment in hard rock tunneling

Brittleness is a characteristic of many geomaterials in which the pre-existing heterogeneities among the mechanical and geometrical properties of the constituent materials, (e.g. grains cementing materials and voids) and loading conditions promote non-homogeneous distribution of the stresses inside the failing mass and eventually along the potential failure plane. This study relates the brittleness of failing hard rocks and tunnels to a strain-dependent brittleness index (I_B) which characterizes the entire failure process of rock (pre- to post-peak), and accounts for the involved mechanisms in inducing inelastic strains (damage) inside the failing rock. The strain-dependent brittleness of rock dictates the mobilized strength around underground excavations, affects their short- and long-term stability, and determines the shape of breakout (failed or inelastic) zone. The ground-support pressure interaction mechanism is also affected by rock brittleness. Brittleness of rock is a time- (loading rate) and size- (geometry) dependent property.