Comparative Analysis of Shear Strength Prediction Models for Reinforced Concrete Slab-Column Connections

This research aims at comparative analysis of shear strength prediction at slab-column connection, unifying machine learning, design codes and Finite Element Analysis. Current design codes (CDCs) of ACI 318-19 (ACI), Eurocode 2 (EC2), Compressive Force Path (CFP) method, Feed Forward Neural Network (FNN) based Artificial Neural Network (ANN), PSO-based FNN (PSOFNN), and BAT algorithm-based BATFNN are used. The study is complemented with FEA of slab for validating the experimental results and machine learning predictions.In the case of hybrid models of PSOFNN and BATFNN, mean square error is used as an objective function to obtain the optimized values of the weights, that are used by Feed Forward Neural Network to perform predictions on the slab data. Seven different models of PSOFNN, BATFNN, and FNN are trained on this data and the results exhibited that PSOFNN is the best model overall. PSOFNN has the best results for SCS=1 with highest value of R as 99.37% and lowest of MSE, and MAE values of 0.0275%, and 1.214% respectively which are better than the best FNN model for SCS=4 having the values of R, MSE, and MAE as 97.464%, 0.0492%, and 1.43%, respectively.Comment: 34 Pages,25 Figure

Earthquake Prediction

Among the countless natural disasters, earthquakes are capable to inflict vast devastation to a large number of buildings and constructions at the blink of an eye. Lack of knowledge and awareness on earthquake as well as its comeback is conspicuous and results in disaster; leading to bitter memories. Therefore, earthquake forecast has been a polemical study theme that has defied even the most intelligent of minds. In this chapter, an attempt was made to do an extensive overview in the area of the earthquake prediction as well as classifying them into the main strategies comprising short‐, immediate‐, and long‐term prediction. An example of each strategy was carried out by mentioning their corresponding approaches/algorithms, such as ΔCFS, CN, MSc, M8, ANN, FFBPANN, KNN, GRNN, RBF, and LMBP; depending on the importance of each strategy. Based on these, it was concluded that, after the Tohoku‐Oki earthquake with M9.0, the current orientation of the Headquarters for earthquake Research Promotion of MEXT in Japan declare that, their mission would be long‐term statistical forecast of seismicity. Even, it is claimed that they do not emphasize on short‐term forecasting. Besides, intermediate‐term estimations are not capable to be used for prevention of all damages and protect all human life, but they may be utilized to undertake certain affordable activities to decrease damage, losses, and modify postdisaster relief. And, despite the long‐term prediction is more concerned by researchers, there is no certain satisfactory level to content them. De facto, the made covenant of 1970 that investigators will be capable to forecast/predict ground excitations within a decade, still remains unmet

Hybrid Advanced Optimization Methods with Evolutionary Computation Techniques in Energy Forecasting

More accurate and precise energy demand forecasts are required when energy decisions are made in a competitive environment. Particularly in the Big Data era, forecasting models are always based on a complex function combination, and energy data are always complicated. Examples include seasonality, cyclicity, fluctuation, dynamic nonlinearity, and so on. These forecasting models have resulted in an over-reliance on the use of informal judgment and higher expenses when lacking the ability to determine data characteristics and patterns. The hybridization of optimization methods and superior evolutionary algorithms can provide important improvements via good parameter determinations in the optimization process, which is of great assistance to actions taken by energy decision-makers. This book aimed to attract researchers with an interest in the research areas described above. Specifically, it sought contributions to the development of any hybrid optimization methods (e.g., quadratic programming techniques, chaotic mapping, fuzzy inference theory, quantum computing, etc.) with advanced algorithms (e.g., genetic algorithms, ant colony optimization, particle swarm optimization algorithm, etc.) that have superior capabilities over the traditional optimization approaches to overcome some embedded drawbacks, and the application of these advanced hybrid approaches to significantly improve forecasting accuracy

Prospectus, December 3, 1990

https://spark.parkland.edu/prospectus_1990/1026/thumbnail.jp

Modeling of Soil Erosion and Sediment Transport

The Special Issue entitled “Modeling of Soil Erosion and Sediment Transport” focuses on the mathematical modeling of soil erosion caused by rainfall and runoff at a basin scale, as well as on the sediment transport in the streams of the basin. In concrete terms, the quantification of these phenomena by means of mathematical modeling and field measurements has been studied. The following mathematical models (software) were used, amongst others: AnnAGNPS, SWAT, SWAT-Twn, TUSLE, WRF-Hydro-Sed, CORINE, LCM-MUSLE, EROSION-3D, HEC-RAS, SRC, WA-ANN. The Special Issue contains 14 articles that can be classified into the following five categories: Category A: “Soil erosion and sediment transport modeling in basins”; Category B: “Inclusion of soil erosion control measures in soil erosion models”; Category C: “Soil erosion and sediment transport modeling in view of reservoir sedimentation”; Category D: “Field measurements of gully erosion”; Category E: “Stream sediment transport modeling”. Most studies presented in the Special Issue were applied to different basins in Europe, America, and Asia, and are the result of the cooperation between universities and/or research centers in different countries and continents, which constitutes an optimistic fact for the international scientific communication

Shaping the Security Environment

Newport Paper No. 29, Shaping the Security Environment, edited by Derek S. Reveron, makes an important contribution to an unfolding debate on the global role of U.S. military forces in an era of transnational terrorism, failed or failing states, and globalization. Reveron, professor of national security decision making at the Naval War College, looks beyond the current conflicts in which the United States is involved to raise fundamental questions concerning the regional diplomatic roles of America’s combatant commanders (COCOMs) and, more generally, the entire array of non-warfighting functions that have become an increasingly important part of the day-to-day life of the American military as it engages a variety of partners or potential partners around the world.https://digital-commons.usnwc.edu/usnwc-newport-papers/1027/thumbnail.jp