Coupled analysis of building damage due to tunnelling

Excavation works in urban areas require a preliminary risk damage assessment. In historical cities, the prediction of building response to settlements is necessary to reduce the risk of damage of the architectural heritage. The current method used to predict the building damage due to ground deformations is the Limiting Tensile Strain Method (LTSM). This method is based on an uncoupled soil-structure analysis, in which the building is modelled as an elastic beam subject to imposed greenfield settlements and the induced tensile strains are compared with a limit value for the material. This approach neglects many factors which play an important rule in the response of the structure to tunneling induced settlements. In this paper, the possibility to apply a settlement risk assessment derived from the seismic vulnerability approach is considered. The parameters that influence the structural response to settlements can be defined through numerical coupled analyses which take into account the nonlinear behaviour of masonry and the soil-structure interaction.

Gain-switched dynamics of tapered waveguide bow-tie lasers

The use of tapered waveguide lasers and amplifiers for enhanced picosecond pulse generation has led to order-of-magnitude peak power and pulse energy improvements. Monolithic pulse generation schemes have so far relied on a double-tapered bow-tie structure. The modeling of tapered lasers has so far been limited to steady-state operation or has lacked experimental comparison. This paper considers both experimentally and theoretically the gain-switched performance of bow-tie lasers of various taper angles. The role of transverse-mode spatial hole burning in tapered waveguide lasers is thereby investigated.

Variability of nanoindentation responses of bone and artificial bone-like composites

Nanoindentation is a popular technique for measuring the intrinsic mechanical response of bone and has been used to measure a single-valued elastic modulus. However, bone is a composite material with 20-80 nm hydroxyapatite plates embedded in a collagen matrix, and modern instrumentation allows for measurements at these small length scales. The present study examines the indentation response of bone and artificial gelatin-apatite nanocomposite materials across three orders of magnitude of lengthscale, from nanometers to micrometers, to isolate the composite phase contributions to the overall response. The load-displacement responses were variable and deviated from the quadratic response of homogeneous materials at small depths. The distribution of apparent elastic modulus values narrowed substantially with increasing indentation load. Indentation of particulate nanocomposites was simulated using finite element analysis. Modeling results replicated the convergence in effective modulus seen in the experiments. It appears that the apatite particles are acting as the continuous ("matrix") phase in bone and nanocomposites. Copyright © 2004 by ASME.

Data-based robust multiobjective optimization of interconnected processes: energy efficiency case study in papermaking.

Reducing energy consumption is a major challenge for "energy-intensive" industries such as papermaking. A commercially viable energy saving solution is to employ data-based optimization techniques to obtain a set of "optimized" operational settings that satisfy certain performance indices. The difficulties of this are: 1) the problems of this type are inherently multicriteria in the sense that improving one performance index might result in compromising the other important measures; 2) practical systems often exhibit unknown complex dynamics and several interconnections which make the modeling task difficult; and 3) as the models are acquired from the existing historical data, they are valid only locally and extrapolations incorporate risk of increasing process variability. To overcome these difficulties, this paper presents a new decision support system for robust multiobjective optimization of interconnected processes. The plant is first divided into serially connected units to model the process, product quality, energy consumption, and corresponding uncertainty measures. Then multiobjective gradient descent algorithm is used to solve the problem in line with user's preference information. Finally, the optimization results are visualized for analysis and decision making. In practice, if further iterations of the optimization algorithm are considered, validity of the local models must be checked prior to proceeding to further iterations. The method is implemented by a MATLAB-based interactive tool DataExplorer supporting a range of data analysis, modeling, and multiobjective optimization techniques. The proposed approach was tested in two U.K.-based commercial paper mills where the aim was reducing steam consumption and increasing productivity while maintaining the product quality by optimization of vacuum pressures in forming and press sections. The experimental results demonstrate the effectiveness of the method.

Data-based robust multiobjective optimization of interconnected processes: Energy efficiency case study in papermaking

Reducing energy consumption is a major challenge for energy-intensive industries such as papermaking. A commercially viable energy saving solution is to employ data-based optimization techniques to obtain a set of optimized operational settings that satisfy certain performance indices. The difficulties of this are: 1) the problems of this type are inherently multicriteria in the sense that improving one performance index might result in compromising the other important measures; 2) practical systems often exhibit unknown complex dynamics and several interconnections which make the modeling task difficult; and 3) as the models are acquired from the existing historical data, they are valid only locally and extrapolations incorporate risk of increasing process variability. To overcome these difficulties, this paper presents a new decision support system for robust multiobjective optimization of interconnected processes. The plant is first divided into serially connected units to model the process, product quality, energy consumption, and corresponding uncertainty measures. Then multiobjective gradient descent algorithm is used to solve the problem in line with user's preference information. Finally, the optimization results are visualized for analysis and decision making. In practice, if further iterations of the optimization algorithm are considered, validity of the local models must be checked prior to proceeding to further iterations. The method is implemented by a MATLAB-based interactive tool DataExplorer supporting a range of data analysis, modeling, and multiobjective optimization techniques. The proposed approach was tested in two U.K.-based commercial paper mills where the aim was reducing steam consumption and increasing productivity while maintaining the product quality by optimization of vacuum pressures in forming and press sections. The experimental results demonstrate the effectiveness of the method. © 2006 IEEE.