Slope Instability of the Earthen Levee in Boston, UK: Numerical Simulation and Sensor Data Analysis

The paper presents a slope stability analysis for a heterogeneous earthen levee in Boston, UK, which is prone to occasional slope failures under tidal loads. Dynamic behavior of the levee under tidal fluctuations was simulated using a finite element model of variably saturated linear elastic perfectly plastic soil. Hydraulic conductivities of the soil strata have been calibrated according to piezometers readings, in order to obtain correct range of hydraulic loads in tidal mode. Finite element simulation was complemented with series of limit equilibrium analyses. Stability analyses have shown that slope failure occurs with the development of a circular slip surface located in the soft clay layer. Both models (FEM and LEM) confirm that the least stable hydraulic condition is the combination of the minimum river levels at low tide with the maximal saturation of soil layers. FEM results indicate that in winter time the levee is almost at its limit state, at the margin of safety (strength reduction factor values are 1.03 and 1.04 for the low-tide and high-tide phases, respectively); these results agree with real-life observations. The stability analyses have been implemented as real-time components integrated into the UrbanFlood early warning system for flood protection

Derivation of near-optimal pump schedules for water distribution by simulated annealing

The scheduling of pumps for clean water distribution is a partially discrete non-linear problem with many variables. The scheduling method described in this paper typically produces costs within 1% of a linear program-based solution, and can incorporate realistic non-linear costs that may be hard to incorporate in linear programming formulations. These costs include pump switching and maximum demand charges. A simplified model is derived from a standard hydraulic simulator. An initial schedule is produced by a descent method. Two-stage simulated annealing then produces solutions in a few minutes. Iterative recalibration ensures that the solution agrees closely with the results from a full hydraulic simulation

Development of soft computing and applications in agricultural and biological engineering

Soft computing is a set of “inexact” computing techniques, which are able to model and analyze very complex problems. For these complex problems, more conventional methods have not been able to produce cost-effective, analytical, or complete solutions. Soft computing has been extensively studied and applied in the last three decades for scientific research and engineering computing. In agricultural and biological engineering, researchers and engineers have developed methods of fuzzy logic, artificial neural networks, genetic algorithms, decision trees, and support vector machines to study soil and water regimes related to crop growth, analyze the operation of food processing, and support decision-making in precision farming. This paper reviews the development of soft computing techniques. With the concepts and methods, applications of soft computing in the field of agricultural and biological engineering are presented, especially in the soil and water context for crop management and decision support in precision agriculture. The future of development and application of soft computing in agricultural and biological engineering is discussed

TBM pressure models: observations, theory and practice

Mechanized tunnelling in soft ground has evolved significantly over the last 20 years. However, the interaction between the tunnel boring machine (TBM) and the ground is often understood through idealized concepts, focused mostly on the machine actions in detriment of the reactions from the ground. These concepts cannot be used to explain several mechanisms that have been observed during the construction of mechanized tunnels. Therefore, this paper presents the path from field observations to the theoretical developments to model the TBM-ground interaction more realistically. Some ideas on how these developments can be applied into practice are presented. Finally, a discussion is proposed about how an effective collaboration between academia and industry can alleviate the current concentration of knowledge in the state of practice

Meta-heuristic algorithms in car engine design: a literature survey

Meta-heuristic algorithms are often inspired by natural phenomena, including the evolution of species in Darwinian natural selection theory, ant behaviors in biology, flock behaviors of some birds, and annealing in metallurgy. Due to their great potential in solving difficult optimization problems, meta-heuristic algorithms have found their way into automobile engine design. There are different optimization problems arising in different areas of car engine management including calibration, control system, fault diagnosis, and modeling. In this paper we review the state-of-the-art applications of different meta-heuristic algorithms in engine management systems. The review covers a wide range of research, including the application of meta-heuristic algorithms in engine calibration, optimizing engine control systems, engine fault diagnosis, and optimizing different parts of engines and modeling. The meta-heuristic algorithms reviewed in this paper include evolutionary algorithms, evolution strategy, evolutionary programming, genetic programming, differential evolution, estimation of distribution algorithm, ant colony optimization, particle swarm optimization, memetic algorithms, and artificial immune system

Developing a distributed electronic health-record store for India

The DIGHT project is addressing the problem of building a scalable and highly available information store for the Electronic Health Records (EHRs) of the over one billion citizens of India