A System for Measurement of Isokinetic Strength for Estimation of Load Handling Capacity

This note describes a method for measurement of isokinetic strength for prediction of manual materials handling capacity of individuals. The apparatus used in this methodology and measurement technique are illustrated in detail.     

Estimation of Aeroelastic Parameters of Bridge Decks Using Neural Networks

A new method of estimating flutter derivatives using artificial neural networks is proposed. Unlike other computational fluid dynamics based numerical analyses, the proposed method estimates flutter derivatives utilizing previously measured experimental data. One of the advantages of the neural networks approach is that they can approximate a function of many dimensions. An efficient method has been developed to quantify the geometry of deck sections for neural network input. The output of the neural network is flutter derivatives. The flutter derivatives estimation network, which has been trained by the proposed methodology, is tested both for training sets and novel testing sets. The network shows reasonable performance for the novel sets, as well as outstanding performance for the training sets. Two variations of the proposed network are also presented, along with their estimation capability. The paper shows the potential of applying neural networks to wind force approximations.     

Seismic safety of lifelines—an optimum design method

Lifeline systems are vulnerable to two types of hazards arising from potential earthquake sources. These are the hazard of a fault-rupture strike on elements of a lifeline system and the hazard of overstress induced in different elements of the system because of the ground vibration. An optimum design method is presented in this paper for the design of a lifeline system for a maximum accepted probability of failure because of any of the two modes of failure. The method may be used to determine an optimum path between a number of fixed points which represent supply or demand stations in the system.     

Sustainable Basin-Scale Water Allocation with Hydrologic State-Dependent Multi-Reservoir Operation Rules

This study extends the PSO-MODSIM model, integrating particle swarm optimization (PSO) algorithm and MODISM river basin decision support system (DSS) to determine optimal basin-scale water allocation, in two aspects. The first is deriving hydrologic state-dependent (conditional) operating rules to better account for drought and high-flow periods, and the second is direct, explicit consideration of sustainability criteria in the model’s formulation to have a better efficiency in basin-scale water allocation. Under conditional operating rules, the operational parameters of reservoir target storage levels and their priority rankings were conditioned on the hydrologic state of the system in a priority-based water allocation scheme. The role of conditional operating rules and policies were evaluated by comparing water shortages associated with objective function values under unconditional and conditional operating rules. Optimal basin-scale water allocation was then evaluated by incorporating reliability, vulnerability, reversibility and equity sustainability indices into the PSO objective function. The extended model was applied for water allocation in the Atrak River Basin, Iran. Results indicated improved distribution of water shortages by about 7.5% using conditional operating rules distinguishing dry, normal and wet hydrologic states. Alternative solutions with nearly identical objective function values were found with sustainability indices included in the model.     

Performance Assessment of a Coupled Particle Swarm Optimization and Network Flow Programming Model for Optimum Water Allocation

Water resources allocation problems are mainly categorized in two classes of simulation and optimization. In most cases, optimization problems due to the number of variables, constraints and nonlinear feasible search space are known as a challenging subject in the literature. In this research, by coupling particle swarm optimization (PSO) algorithm and a network flow programming (NFP) based river basin simulation model, a PSO-NFP hybrid structure is constructed for optimum water allocation planning. In the PSO-NFP model, the NFP core roles as the fast inner simulation engine for finding optimum values for a large number of water discharges in the network links (rivers and canals) and nodes (reservoirs and demands) while the heuristic PSO algorithm forms the outer optimization cover to search for the optimum values of reservoirs capacities and their storage priorities. In order to assess the performance of the PSO-NFP model, three hypothetical test problems are defined, and their equivalent nonlinear mathematical programs are developed in LINGO and the results are compared. Finally, the PSO-NFP model is applied in solving a real river basin water allocation problem. Results indicate that the applied method of coupling PSO and NFP has an efficient ability for handling river basin-scale water resources optimization problems.     

Linear principal transformation: toward locating features in N-dimensional image space

Projection Functions have been widely used for facial feature extraction and optical/handwritten character recognition due to their simplicity and efficiency. Because these transformations are not one-to-one, they may result in mapping distinct points into one point, and consequently losing detailed information. Here, we solve this problem by defining an N-dimensional space to represent a single image. Then, we propose a one-to-one transformation in this new image space. The proposed method, which we referred to as Linear Principal Transformation (LPT), utilizes Eigen analysis to extract the vector with the highest Eigenvalue. Afterwards, extrema in this vector were analyzed to extract the features of interest. In order to evaluate the proposed method, we performed two sets of experiments on facial feature extraction and optical character recognition in three different data sets. The results show that the proposed algorithm outperforms the observed algorithms in the paper and achieves accuracy from 1.4 % up to 14 %, while it has a comparable time complexity and efficiency.     

Adaptive Network-Based Fuzzy Inference Systems Coupled with Genetic Algorithms for Predicting Soil Permeability Coefficient

This paper extends hybrid-type optimization models of genetic algorithm adaptive network-based fuzzy inference system (GA-ANFIS) for predicting the soil permeability coefficient (SPC) of different types of soil. In these models, GA optimizes parameters of a subtractive clustering technique that controls the structure of the ANFIS model’s fuzzy rule base. Simultaneously, a hybrid leaning algorithm is employed in the ANFIS, as a trained fuzzy inference system (FIS), which optimally determines the parameter sets of the examined FISs in ANFIS. Using an updated large database of SPCs consisting of 338 fine-grained, 178 mixed and 94 granular soil samples, GA-ANFIS framework constructs different models of predicting the permeability coefficient of respectively fine-grained, mixed and granular soils. A fuzzy C-mean technique has been used to cluster the entire data samples of each type of soil and divide them uniformly into training and testing data sets. Different prediction models of SPC have been trained and tested for each of the three soil types, and the appropriate models have been selected. The selected models have been compared with ANN and modified-by-GA empirical prediction models. Results show that the constructed GA-ANFIS models outperform the other models in terms of the prediction accuracy and the generalization capability.     

Treatability of Alternative Fuel Oxygenates Using Advanced Oxidation, Air Stripping, and Carbon Adsorption

Methyl tert-butyl ether (MTBE) is a gasoline oxygenate that has become a significant threat to groundwater supplies across the United States. Due to its physiochemical properties it has proven difficult and costly to remove from contaminated sites. This study was conducted to determine whether the alternative oxygenates (AO)—diisopropyl ether (DIPE), ethyltert-butyl ether (ETBE), tert-amyl methyl ether (TAME), tert-butyl alcohol (TBA), and ethanol (EtOH)—present a more efficient and less costly option from a remediation standpoint. Air stripping, carbon adsorption, and ultraviolet/H2O2 and O3/H2O2 advanced oxidation processes were examined at pilot scale to develop design parameters from which technical and economic comparisons were made for each alternative oxygenate versus MTBE. The experimental results showed that the ether AOs—DIPE, TAME, and ETBE—were each more efficiently and more economically treated than MTBE. The alternative alcohol oxygenates—TBA and EtOH—were less efficiently and less economically treated by the processes studied. The paper details the effects of primary process parameters and properties of individual oxygenates on process efficiency.     

Gas holdup in two phase gas–liquid and three phase gas–liquid–solid production and transportation

The gas holdup is an important parameter that is needed for design and development of surface facilities and transportation pipelines in the field of petroleum engineering. There is no general model for prediction of this parameter in different systems and under different conditions. As a result, development of accurate and general models for prediction of this parameter in various situations is of great importance. This study presents new experimental gas holdup data in the kerosene+CO₂ and kerosene+N₂ systems. The experimental data were measured by using a bubble column setup. Moreover, a computer-based model namely PSO-ANFIS model is also developed for prediction of the gas holdup in different systems. A total of 818 experimental gas holdup data in various systems were utilized including the newly measured experimental data in the present work as well as experimental data from several published works in the literature. Results showed that the developed PSO-ANFIS model is accurate for prediction of experimental data with an R² value of 0.998 and average absolute relative deviation (AARD%) of 3.4%.     

Trace Metal Occurrence and Mobility Assessment in Stream Sediments near the Sungun Porphyry Copper Deposit in Northwest Iran

The Sungun porphyry copper deposit is located 100?km northeast of Tabriz in the Azarbaijan province of Iran. The total concentration and chemical fractionation of metals and metalloids (As, Se, Cd, Cr, Ni, Co, and Zn) in sediment downstream of the Sungun mine was investigated. The degree of contamination was evaluated using the sediment pollution index (SPI) and enrichment factor. The concentrations of As, Cd, Se, and Zn in the contaminated sediments were elevated due to anthropogenic activities. According to the SPI, surface sediments in the ephemeral rivers flowing in the vicinity of the Sungun porphyry copper deposit are classified as natural sediment with no ecological risk, while sediments that received contaminated effluents are classified as slightly polluted. Sequential extraction indicated that Co was principally associated with the iron and manganese oxide fraction, while other elements (As, Zn, Cr, Se, and Ni) were mainly distributed in residual phases. Cadmium is the only element that has a high potential for mobility and bioavailability in the sediments and, of the investigated elements, it poses the greatest potential risk to the local aquatic ecosystems. The chemical fractionation pattern of elements appears to be influenced by industrial effluents. Although the bioavailable fraction of most contaminant metals is low due to the freshness of the sediments, over time, the oxidation of sulfide minerals in these sediments could contribute contaminant elements in soluble form.