Modeling of clear-water contraction scour for an abutment bridge in a compound channel

Bridge collapse has dramatic consequences in transportation system. Besides losing of life, disruption in service results tremendous effects on the economic growth of the countries. Contraction scour is a common and major cause of bridge failure. Designing the bridge foundation safely needs an accurate estimation of scour depth, underestimation may lead to bridge failure while over estimation will lead to excessive construction cost. Abutment bridges most commonly are used for bridges overcomparatively small channels. Reliability, strength and economy are the main reasons to increase concerning in Abutment Bridges. Commonly, in the compound channels, Abutment Bridgesare protrudedinto the main channel. Consequently, contraction scour expands in the main channel. Prior design approaches treated abutments as being solid structure locating in a floodplain or main channel, individually. The main deficiency of previous studies is that they do not accurately simulated the actual constriction features of Abutment Bridge in a compound channel with a complex geometries. Subsequently, the data and observations lead to unrealistically scour depth estimates. The main objective of the current research is to provide reliable prediction of geometrical characteristics for protruded abutment bridge in the compound channel on contraction scour depth and its’ location. The study required extensive experimentation conducted with laboratory flume, and abutments of realistic design that were subjected to the contraction scour for a range of channel constriction, channel geometries, and embankment protection layers. The experiments on clear-water conditions under steady flow at threshold velocity were conducted at an Abutment Bridge with approach embankment configured in a range of erodiblity conditions: fixed embankment on erodible and then far less-erodible floodplain; riprap, gabion-mattress, and non-erodible embankment on readily erodible floodplain. Flow depth was kept constant for all of the experiments with thecohesionless uniform sediment. A methodology is developed to predict the maximum contraction scour depth and its’location along the compound channel. Outcomes of verifying the method show that proposed method gives reasonable maximum contraction scour depth and location predictions. The results indicate that the contraction degree, abutments’ protrusionfrom floodplain into the main channel, soil, and protection layer properties really affect the final contraction scour depth and its’ location. Results allow promoting the Abutment Bridges’ design and consequently increasing economical and public safety by decreasing the bridges’ construction cost, saving additional maintenance charges, increasing bridges’ stability, and preventing loss of lives.However, application of the currently developed methodology are limited to laboratory conditions. Site verifications are necessary in the future study

Enhancing the Method of Decentralized Multi-Purpose Reuse of Wastewater in Urban Area

The reuse of treated wastewater is attractive as a communal source of excess water source in water-scarce counties and nations. The expansion of the urban population and the increase in the coverage of water supply networks and sewage networks will raise the amount of municipal sewage. This can turn into a new-fangled water resource. In the current research, the new campus city was selected as the first case study to design a wastewater reuse and recycling system. Accordingly, one of the most important innovations in the proposed research is the unique applied dimensions, in addition to its first-time performance, and the application of the Geo-land method in wastewater recycling as the theoretical dimension of the design. Clustering the decentralized reuse of wastewater for urban areas showed that significant parts of residential areas are located in the first high priority group. Urban planners can consider the results in establishing a comprehensive plan to prioritize the decentralized use of wastewater in the urban area

Enhancing the Method of Decentralized Multi-Purpose Reuse of Wastewater in Urban Area

The reuse of treated wastewater is attractive as a communal source of excess water source in water-scarce counties and nations. The expansion of the urban population and the increase in the coverage of water supply networks and sewage networks will raise the amount of municipal sewage. This can turn into a new-fangled water resource. In the current research, the new campus city was selected as the first case study to design a wastewater reuse and recycling system. Accordingly, one of the most important innovations in the proposed research is the unique applied dimensions, in addition to its first-time performance, and the application of the Geo-land method in wastewater recycling as the theoretical dimension of the design. Clustering the decentralized reuse of wastewater for urban areas showed that significant parts of residential areas are located in the first high priority group. Urban planners can consider the results in establishing a comprehensive plan to prioritize the decentralized use of wastewater in the urban area

Forecasting pan evaporation with an integrated artificial neural network quantum-behaved particle swarm optimization model: a case study in Talesh, Northern Iran

Accurate simulation of evaporation plays an important role in the efficient management of water Resources. Generally, evaporation is measured using the direct method where Class A pan-evaporimeter is used, and an indirect method that includes empirical equations. However, despite its widespread usage, Class A pan-evaporimeter method can be affected by human and instrumentation errors. Empirical equations, on the other hand, are generally linked to the different climatic factors that should provide initial or boundary conditions in the mathematical equations that affect the rate of evaporation. Considering these challenging, heuristic soft computing approaches that do not need key information about the physics of evaporation. In this study, a Quantum-behaved Particle Swarm Optimization algorithm, embedded into a multi-layer perceptron technique, is developed to estimate the evaporation rates over a daily forecast horizon. The measured evaporation data from 2012–2014 for Talesh meteorological station located in Northern Iran are employed. The predictive accuracy of the MLP-QPSO model is evaluated with existing methods: i.e. a hybrid MLP-PSO and a standalone MLP model. The results are evaluated in respect to statistical performance criterion: the mean absolute error, root mean square error (RMSE), Willmott's Index and the Nash–Sutcliffe coefficient. In conjunction with these metrics, Taylor diagrams are also utilized to assess the level of agreement between the forecasted and observed evaporation data. Evidently, the hybrid MLP-QPSO model is confirmed to be an optimal forecasting tool applied for estimating daily pan evaporation, outperforming both the hybrid MLP-PSO and the standalone model.In light of these results, the present study justifies the potential utility of the hybrid MLP-QPSO model to be applied for estimating daily evaporation rates in North of Iran

Sugarcane growth prediction based on meteorological parameters using extreme learning machine and artificial neural network

Management strategies for sustainable sugarcane production need to deal with the increasing complexity and variability of the whole sugar system. Moreover, they need to accommodate the multiple goals of different industry sectors and the wider community. Traditional disciplinary approaches are unable to provide integrated management solutions, and an approach based on whole systems analysis is essential to bring about beneficial change to industry and the community. The application of this approach to water management, environmental management and cane supply management is outlined, where the literature indicates that the application of extreme learning machine (ELM) has never been explored in this realm. Consequently, the leading objective of the current research was set to filling this gap by applying ELM to launch swift and accurate model for crop production data-driven. The key learning has been the need for innovation both in the technical aspects of system function underpinned by modelling of sugarcane growth. Therefore, the current study is an attempt to establish an integrate model using ELM to predict the concluding growth amount of sugarcane. Prediction results were evaluated and further compared with artificial neural network (ANN) and genetic programming models. Accuracy of the ELM model is calculated using the statistics indicators of Root Means Square Error (RMSE), Pearson Coefficient (r), and Coefficient of Determination (R2) with promising results of 0.8, 0.47, and 0.89, respectively. The results also show better generalization ability in addition to faster learning curve. Thus, proficiency of the ELM for supplementary work on advancement of prediction model for sugarcane growth was approved with promising results

A Hybrid Intelligence Approach to Enhance the Prediction Accuracy of Local Scour Depth at Complex Bridge Piers

Local scour depth at complex piers (LSCP) cause expensive costs when constructing bridges. In this study, a hybrid artificial intelligence approach of random subspace (RS) meta classifier, based on the reduced error pruning tree (REPTree) base classifier, namely RS-REPTree, was proposed to predict the LSCP. A total of 122 laboratory datasets were used and portioned into training (70%: 85 cases) and validation (30%: 37 cases) datasets for modeling and validation processes, respectively. The statistical metrics such as mean absolute error (MAE), root mean squared error (RMSE), correlation coefficient (R), and Taylor diagram were used to check the goodness-of-fit and performance of the proposed model. The capability of this model was assessed and compared with four state-of-the-art soft-computing benchmark algorithms, including artificial neural network (ANN), support vector machine (SVM), M5P, and REPTree, along with two empirical models, including the Florida Department of Transportation (FDOT) and Hydraulic Engineering Circular No. 18 (HEC-18). The findings showed that machine learning algorithms had the highest goodness-of-fit and prediction accuracy (0.885 &lt; R &lt; 0.945) in comparison to the other models. The results of sensitivity analysis by the proposed model indicated that pile cap location (Y) was a more sensitive factor for LSCP among other factors. The result also depicted that the RS-REPTree ensemble model (R = 0.945) could well enhance the prediction power of the REPTree base classifier (R = 0.885). Therefore, the proposed model can be useful as a promising technique to predict the LSCP.Validerad;2020;Nivå 2;2020-02-04 (johcin)</p