Embodied energy as an environmental impact indicator for basement wall construction

Attempts were made to quantify the environmental impacts of the basement walls of two commercial buildings in London. Four different retaining wall options were designed based on steel and concrete systems for each of the sites. It was considered that excavation would take place with the aid of a one or two anchors system. Evaluation of embodied energy (EE) and CO2 emissions for each of the wall designs and anchoring systems were compared. Results show that there are notable differences in EE between different wall designs. Using the averaged set of Embodied Energy Intensity (EEI) values, the use of recycled steel over virgin steel would reduce the EE of the wall significantly. The difference in anchor designs is relatively insignificant, and therefore the practicality of the design for the specific site should be the deciding factor for anchor types. Generally, the scale of environmental impacts due to constructions is large compared to other aspects in life as demonstrated with the comparisons to car emissions and household energy consumption. Copyright ASCE 2008.

A quality-guided displacement tracking algorithm for ultrasonic elasticity imaging.

Displacement estimation is a key step in the evaluation of tissue elasticity by quasistatic strain imaging. An efficient approach may incorporate a tracking strategy whereby each estimate is initially obtained from its neighbours' displacements and then refined through a localized search. This increases the accuracy and reduces the computational expense compared with exhaustive search. However, simple tracking strategies fail when the target displacement map exhibits complex structure. For example, there may be discontinuities and regions of indeterminate displacement caused by decorrelation between the pre- and post-deformation radio frequency (RF) echo signals. This paper introduces a novel displacement tracking algorithm, with a search strategy guided by a data quality indicator. Comparisons with existing methods show that the proposed algorithm is more robust when the displacement distribution is challenging.