Application of the Cluster Analysis in Computational Paleography

This chapter presents a method to determine the actual version of a script used in constructing of a script relic from unknown origin. The glyphs belong to graphemes as models are realized in the relics as symbols. Some group of glyphs may transform their shape (shapeshifting) through time which produces various versions of scripts that use different glyphs to express the same grapheme. These glyph variants can be identified from extant relics, mainly from historical abecedaries that are used as references. Our algorithm can determine whether or not an abecedary is related to the symbols of a relic from unknown origin by means of the canonical decomposition of the glyphs and symbols. From there an aggregated value called fingerprint is created and it is unique for each relic. The fingerprints then are evaluated by clustering technique using various metrics. As the result of performing comparative evaluations the Minkowski metric provides the most interpretable clustering structure. The results of the evaluations, conclusions, and future work are also presented.

Reliability-Based Criteria for the Decision of the Compensation of Corroded Flexural Reinforcement

Corrosion of reinforcement is considered as the major cause of most deteriorated concrete structures. As reinforcement corrodes, the load carrying capacity is affected and hence, the probability of failure increases. At the time of inspection of deteriorated structures, engineers are faced with the problem whether the available steel is enough to secure the safety of the structural member. This chapter addresses this problem based on reliability-based approach to evaluate the safety of the deteriorated members due to uniform corrosion under bending. A methodology is proposed to facilitate the determination of the member reliability index based on basic material properties and current loading. A step-by-step procedure is proposed based on charts developed in this study according to the Egyptian code provisions.

A Review of Soft Computing Methods Application in Rock Mechanic Engineering

Engineering behavior of rock mass is controlled by many factors, related to its nature and the environmental conditions. Determining all the parameters, ranking their weights, and clarifying their relative effects are very difficult tasks to accomplish. To overcome these difficulties, many researchers have employed soft computing methods in rock mechanics engineering. The soft computing methods have taken an important role in rock mechanics, and their abilities to address uncertainties, insufficient information and ambiguous linguistic expressions stand out in treating complex natural rock mass. This chapter briefly will review the development of soft computing techniques in rock mechanics engineering, especially in predicting of rock engineering classification system and mechanical properties of rock material and rock mass, determination weathering degree of rock material, evolution of rock performance, blasting and, rock slope stability. In addition, the future of the development and application of soft computing in rock mechanics engineering is discussed.

Simulation of Temperature and Precipitation under the Climate Change Scenarios

This study aims to discuss the potentials of machine learning methods such as artificial neural network (ANN), least squares support vector machine (LSSVM), and relevance vector machine (RVM) in downscaling of simulations of a general circulation model (GCM) for monthly temperature and precipitation of the Demirkopru Dam located in the Aegean region of Turkey. The predictors are obtained from ERA-Interim re-analysis data. The best performed downscaling model is integrated into European Centre Hamburg Model (ECHAM5) with A2 future scenario. The results are then discussed to assess the probable climate change effects on temperature and precipitation.

Liquefaction Modelling of Granular Soils using Discrete Element Method

The damage induced by seismic events is well known among the civil engineering, geological and seismological community. Seismologists and geologists who study this hazard at a deeper level are concerned more with the history and cause of earthquake events rather than their effects. When seismic energy is released during an earthquake it passes from the bedrock, then through the soil and to the sub structure through which it is transmitted to the superstructure. Liquefaction phenomenon is a consequence of earthquake induced pore water pressure in the soil due to this released energy. Various deterministic and probabilistic based methods have been developed in the recent past after various case histories. It is crucial to understand the performance of critical structures such as pipelines, road networks, nuclear reactors etc during liquefaction. The current chapter majorly focuses on the liquefaction assessment using numerical modeling.

Adaptive Fuzzy Modeling using Orthonormal Basis Functions for Network Traffic Flow Control

In this chapter, we present some Fuzzy training algorithms, such as the Fuzzy LMS (Least Mean Squares) and Fuzzy RLS (Recursive Least Squares) predictors. We use concepts of multifractal analysis to present and validate a Fuzzy LMS predictor based on the autocorrelation function of a multifractal model. We evaluate the efficiency of these algorithms when applied to bandwidth allocation tasks. We also present adaptive predictive OBF (Orthonormal Basis Functions)-Fuzzy models. To this end, we model traffic traces using OBF functions obtained through multifractal analysis. Further, we insert these functions into OBF-Fuzzy models trained with the adaptive training algorithms. Updating the Fuzzy model parameters, we predict future values of real traffic traces. We also present a comparison of prediction performance of different adaptive Fuzzy algorithms including OBF-Fuzzy models. Finally, we verify the performance of the OBF-Fuzzy algorithms in modeling the buffer queueing in a communication network and controlling traffic flow rates.

Challenges Faced in Enhancing the Performance and Scalability in Parallel Computing Architecture

Now a day the architecture of high performance systems are improving with more and more processor cores on the chip. This has both benefits as well as challenges. The benefit is running more task simultaneously which reduces the running time of the program or application. The challenges are what is the maximum limit of the number of cores in the given chip, how the existing and future software will make use of all the cores, what parallel programming language to choose, what are the memory and cache coherence issues involved when we increase the number of cores, how to solve the power and performance issues, how the cores are connected and how they are communicating to solve a single problem, workload distribution and load balancing issues in terms of scalability. There is a practical limit for speedup and scalability of number of cores on the chip which needs to be analyzed. So this chapter will focus on the introduction and overviews of parallel computing and the challenges faced in enhancing the performance and scalability in parallel computing architecture.

Reliability Design of Footings in Cohesionless Soils using Soft Computing Metamodelings

An extensive database of full-scale field load tests was used to build predictive models to determine the bearing capacity of footings under axial compression in cohesionless soils. Based on this database, soft computing techniques, i.e., the multivariate adaptive regression splines (MARS) and artificial neural networks (ANN) are adopted for comparison for surrogate model building on bearing capacity. The performances of the two computing techniques are compared. A reliability-based design of footings was then presented. It allows one to obtain the probability that the ultimate limit state was exceeded for a given soil variability.

Molecular Dynamics Simulation of Asphaltic Material

An attempt is made to understand the chemical composition, oxidation mechanisms, and property changes of asphalt binders before and after oxidative aging using molecular dynamics (MD) simulations. Unoxidized and oxidized asphalts are subjected to different compressive and tensile stress rates, and moisture contents at room temperature. Results show that density, energy, and viscosity of the oxidized asphalt are higher than the unoxidized asphalt, indicating hardening and rheological property changes of asphalt after oxidation. Both the unoxidized and oxidized asphalts deform more and fail faster with an increase in stress rates, especially under tensile stress. The oxidized asphalt is stronger than the unoxidized asphalt under mechanical stress. Moisture inclusion affects viscosity more by decreasing the viscosity of the oxidized asphalt faster compared to the unoxidized asphalt. The viscosity of the oxidized asphalt is lower than that of the unoxidized asphalt above 5% moisture inclusion. This indicates that oxidized asphalt pavement might be exposed to more moisture-induced damage.

Prediction of Temperature in Buildings using Machine Learning Techniques

Energy efficiency is a trend due to ecological and economic benefits. Within this field, energy efficiency in buildings sector constitutes one of the main concerns due to the fact that approximately 40% of total world energy consumption corresponds to this sector. Climate control in buildings has the potential to increase its energy efficiency planning strategies for the heating, ventilation and air conditioning (HVAC) machines. These planning strategies may include a stage for long term indoor temperature forecasting. This chapter entails the use of four prediction models (NAÏVE, MLR, MLP, FIS and ANFIS) to forecast temperature in an office building using a temporal horizon of several hours. The obtained results show that the MLP outperforms the other analyzed models. Finally, the obtained predictors are deeply analyzed to obtain information about the influence of the HVAC settings in the building temperature.