•An optimization strategy was conducted to obtain a final set of ANN model.•The developed ANN model exhibited superior performance than current codes and empirical equations.•An explicit equation ...based on Artificial Neural Networks were derived for practical application.
In this paper an Artificial Neural Network (ANN) model is developed for the prediction of the ultimate compressive load of rectangular Concrete Filled Steel Tube (CFST) columns, taking into account load eccentricity. To this end, an experimental database of CFST specimens from the literature has been compiled, totaling 1224 individual tests, both under concentric and under eccentric loading. Except for eccentricity, other parameters taken into consideration include the cross section width, height and thickness, the steel yield limit, the concrete strength and the column length. Both short and long specimens were evaluated. The architecture of the proposed ANN model was optimally selected, according to predefined performance metrics. The developed model was then compared against available design codes. It was found that its accuracy was significantly improved while maintaining a stable numerical behavior. The explicit equation that describes mathematically the ANN is offered in the paper, for easier implementation and evaluation purposes.
Landslides as major geo-hazards in Sweden adversely impact on nearby environments and socio-economics. In this paper, a landslide susceptibility map using a proposed subdivision approach for a large ...area in southwest Sweden has been produced. The map has been generated by means of an artificial neural network (ANN) model developed using fourteen causative factors extracted from topographic and geomorphologic, geological, land use, hydrology and hydrogeology characteristics. The landslide inventory map includes 242 events identified from different validated resources and interpreted aerial photographs. The weights of the causative factors employed were analyzed and verified using accepted mathematical criteria, sensitivity analysis, previous studies, and actual landslides. The high accuracy achieved using the ANN model demonstrates a consistent criterion for future landslide susceptibility zonation. Comparisons with earlier susceptibility assessments in the area show the model to be a cost-effective and potentially vital tool for urban planners in developing cities and municipalities.
•An ANN model for landslide susceptibility was developed and optimized.•A novel subdivision approach for landslide hazard assessment was introduced.•Detailed discussion comparing with previous studies and projects.•A cost-effective high-resolution map for decision makers and planners was generated.
As an important safety-critical cyber-physical system (CPS), the braking system is essential to the safe operation of the electric vehicle. Accurate estimation of the brake pressure is of great ...importance for automotive CPS design and control. In this paper, a novel probabilistic estimation method of brake pressure is developed for electrified vehicles based on multilayer artificial neural networks (ANNs) with Levenberg-Marquardt backpropagation (LMBP) training algorithm. First, the high-level architecture of the proposed multilayer ANN for brake pressure estimation is illustrated. Then, the standard backpropagation (BP) algorithm used for training of the feed-forward neural network (FFNN) is introduced. Based on the basic concept of BP, a more efficient training algorithm of LMBP method is proposed. Next, real vehicle testing is carried out on a chassis dynamometer under standard driving cycles. Experimental data of the vehicle and the powertrain systems are collected, and feature vectors for FFNN training collection are selected. Finally, the developed multilayer ANN is trained using the measured vehicle data, and the performance of the brake pressure estimation is evaluated and compared with other available learning methods. Experimental results validate the feasibility and accuracy of the proposed ANN-based method for braking pressure estimation under real deceleration scenarios.
•Cumulative biogas production was studied for food, fruits and vegetables wastes.•Database was built on mixed values of eight variables seen in literature.•Different artificial neural network (ANN) ...topologies were implemented and assessed.•The predictions showed more than 85% correctness using the complete database.
Biogas can be generated from many types of biomass residues. These biomasses have different characteristics which makes standardization of process conditions difficult. Thus, the influence of different conditions has been explored for several biogas production scenarios using numerical models. In the present work, an experimental study of biogas production from food waste was carried out in triplicate in a batch reactor at 37 °C with an organic loading rate (OLR) equal to 5, 10 and 20 g VS/(l.d) after 21 days. A database was also built using values presented in the literature in order to develop a numerical model using artificial neural networks (ANN), for food waste (FW), fruit and vegetables waste (FVW) or blends of both in codigestion (CD), reactor/feed type, volatile solid (VS), pH, OLR, hydraulic retention time, temperature and reactor volume, as input variables, and the cumulative biogas production as output. The response surfaces of the ANN model were found to be useful for defining the optimum region in biogas production; when applied to the training, testing and validation datasets, the model showed acceptable values of coefficient of determination (0.9929, 0.8486 and 0.6167 for the input parameters respectively). It was found that the production of biogas was the highest when temperatures was within the range of thermophilic conditions, with a local maximum for mesophilic conditions. Optimized biodigestion of CD or FW allows higher VS content (around 15–20%) than for FVW (lower than 10%). It was also observed that biodigestion of FVW leads to the highest cumulative biogas production (around twice the value found for FW and CD).
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•A novel method is proposed for predicting cemented paste backfill (CPB) strength.•Artificial neural network (ANN) and particle swarm optimization (PSO) are combined.•Dataset is ...collected from 396 unconfined compressive strength tests.•PSO was efficient in the architecture-tuning of the ANN.•The optimum ANN model was accurate at predicting CPB strength with R = 0.979.
Cemented paste backfill (CPB) has been widely used to prevent and mitigate hazards produced during the excavation of underground stopes. In practice, the strength of CPB is often an essential parameter for successful stope design. We propose an intelligent technique in this study for predicting the unconfined compressive strength (UCS) of CPB. This intelligent technique is a combination of the artificial neural network (ANN) and particle swarm optimization (PSO). The ANN was used for non-linear relationships modelling and PSO was used for the ANN architecture-tuning. Inputs of the ANN were selected to be the tailings type, the cement-tailings ratio, the solids content, and the curing time. A total of 396 CPB specimens under different combination of influencing variables were tested for the preparation of the dataset. The results showed that PSO was efficient for the ANN architecture-tuning. Also, comparison of the predicted UCS values with experimental values showed that the optimum ANN model was very accurate at predicting CPB strength.
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•Optimized TiO2 photocatalyst prepared at 5.42 pH and 1.75 g/L dosage.•The ANN modelling perfectly fitted the photocatalytic degradation reaction.•The designed model fits well with ...Pseudo-first-order and Langmuir Isotherms.
The epidemic growth of the pharmaceuticals industries over the years in order to meet the human demands had exerted substantial pressure on the global environment, particularly water pollutions crisis. Herein, the photocatalytic degradation of phenol was investigated via commercial TiO2 nanoparticles. The Artificial Neural Network (ANN) and Response Surface Methodology (RSM) was employed for scrutinizing the suitable modelling and optimized condition of the TiO2 nanoparticles in yielding a profound rate of phenol removal. The parameters of investigation involved pH, phenol concentration, catalyst doses and degradation time. The RSM data shows the profound rate of phenol removal ˜ 99.48% was achieved by TiO2 NPs in a designed photocatalytic system that set at 5.42 pH, 15.21 mg/L phenol concentration, 1.75 g/L TiO2 dosage and 540 min irradiation time. The designed system fits well with the Pseudo-First-Order and the Langmuir isotherm model with R2 > 0.999. On the other hand, the ANN study revealed that the predicated model was perfectly fitted with the experimental data giving the highest value of R2. This work provides an insight into two different statistical modelling and optimization which could provide exposure for developing an optimized nanomaterial towards the removal of hazardous pollutant.
•Design of a multilayer perceptron serving as a troubled-cell indicator.•The proposed indicator is free of problem-dependent parameters.•Correct classification of cells with smooth extrema.•Testing ...with scalar and systems of conservation laws in one-dimension.•Comparison with minmod-type TVB limiter.
High-resolution schemes for conservation laws need to suitably limit the numerical solution near discontinuities, in order to avoid Gibbs oscillations. The solution quality and the computational cost of such schemes strongly depend on their ability to correctly identify troubled-cells, namely, cells where the solution loses regularity. Motivated by the objective to construct a universal troubled-cell indicator that can be used for general conservation laws, we propose a new approach to detect discontinuities using artificial neural networks (ANNs). In particular, we construct a multilayer perceptron (MLP), which is trained offline using a supervised learning strategy, and thereafter used as a black-box to identify troubled-cells. The proposed MLP indicator can accurately identify smooth extrema and is independent of problem-dependent parameters, which gives it an advantage over traditional limiter-based indicators. Several numerical results are presented to demonstrate the robustness of the MLP indicator in the framework of Runge–Kutta discontinuous Galerkin schemes, and its performance is compared with the minmod limiter and the minmod-based TVB limiter.
•Crack identification using Artificial Neural Networks (ANN).•Optimization of ANN parameters using Jaya algorithm.•Dynamic and Static analysis using eXtended IsoGeometric Analysis (XIGA).•This ...application is found to be robust and accurate for crack identification.
This paper presents an effective method for crack identification to improve the training of Artificial Neural Networks (ANN) parameters using Jaya algorithm. Dynamic and static datasets are introduced using eXtended IsoGeometric Analysis (XIGA) to improve the accuracy of the proposed application based on the frequency and strain measurements. Based on the concept used in our previous works, XIGA provided more accurate results for fracture mechanics applications than other modelling techniques. Therefore, XIGA datasets of cracked plate are used to improve ANN technique for static and dynamic analyses. Model updating of the cracked plate is considered by introducing the mass of accelerometers and identifying Young’s modulus of the plate and stiffness of springs using Jaya algorithm. The difference between measured and calculated frequencies is used as an objective function to calibrate the XIGA model. The crack length is predicted using an adaptive approach without any previous knowledge based on the data provided from a numerical model. Jaya algorithm is used to optimize the most important parameters of ANN. Several numerical examples with different crack scenarios and different boundary conditions are studied in order to evaluate the proposed approach. The results show that the proposed application is able to predict all considered scenarios and accurately identify the crack length. Experimental data of cracked plates are used to validate the numerical predictions. Hence, this application is found to be robust and accurate for crack identification in plates.
•Introduce artificial neural network for regional seismic risk assessment of skewed bridges.•Develop multi-dimensional fragilities for California bridges via artificial neural network.•Reduce ...computational efforts for developing bridge-class fragility curves.•Estimate the seismic vulnerability of skewed bridges.
Recent researches are directed towards the regional seismic risk assessment of structures based on a bridge inventory analysis. The framework for traditional regional risk assessments consists of grouping the bridge classes and generating fragility relationships for each bridge class. However, identifying the bridge attributes that dictate the statistically different performances of bridges is often challenging. These attributes also vary depending on the demand parameter under consideration. This paper suggests a multi-parameter fragility methodology using artificial neural network to generate bridge-specific fragility curves without grouping the bridge classes. The proposed methodology helps identify the relative importance of each uncertain parameter on the fragility curves. Results from the case study of skewed box-girder bridges reveal that the ground motion intensity measure, span length, and column longitudinal reinforcement ratio have a significant influence on the seismic fragility of this bridge class.
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