Size effects in the conical indentation of an elasto-plastic solid

The size effect in conical indentation of an elasto-plastic solid is predicted via the Fleck and Willis formulation of strain gradient plasticity (Fleck, N.A. and Willis, J.R., 2009, A mathematical basis for strain gradient plasticity theory. Part II: tensorial plastic multiplier, J. Mech. Phys. Solids, 57, 1045-1057). The rate-dependent formulation is implemented numerically and the full-field indentation problem is analyzed via finite element calculations, for both ideally plastic behavior and dissipative hardening. The isotropic strain-gradient theory involves three material length scales, and the relative significance of these length scales upon the degree of size effect is assessed. Indentation maps are generated to summarize the sensitivity of indentation hardness to indent size, indenter geometry and material properties (such as yield strain and strain hardening index). The finite element model is also used to evaluate the pertinence of the Johnson cavity expansion model and of the Nix-Gao model, which have been extensively used to predict size effects in indentation hardness. © 2012 Elsevier Ltd.

Dual-mode thin film bulk acoustic wave resonators for parallel sensing of temperature and mass loading

Thin film bulk acoustic wave resonator (FBAR) devices supporting simultaneously multiple resonance modes have been designed for gravimetric sensing. The mechanism for dual-mode generation within a single device has been discussed, and theoretical calculations based on finite element analysis allowed the fabrication of FBARs whose resonance modes have opposite reactions to temperature changes; one of the modes exhibiting a positive frequency shift for a rise of temperature whilst the other mode exhibits a negative shift. Both modes exhibit negative frequency shift for a mass load and hence by monitoring simultaneously both modes it is possible to distinguish whether a change in the resonance frequency is due to a mass load or temperature variation (or a combination of both), avoiding false positive/negative responses in gravimetric sensing without the need of additional reference devices or complex electronics. © 2012 Elsevier B.V.

Social sensing in the field: Challenges in detecting social interactions in construction sites

Construction industry is a sector that is renowned for the slow uptake of new technologies. This is usually due to the conservative nature of this sector that relies heavily on tried and tested and successful old business practices. However, there is an eagerness in this industry to adopt Building Information Modelling (BIM) technologies to capture and record accurate information about a building project. But vast amounts of information and knowledge about the construction process is typically hidden within informal social interactions that take place in the work environment. In this paper we present a vision where smartphones and tablet devices carried by construction workers are used to capture the interaction and communication between workers in the field. Informal chats about decisions taken in the field, impromptu formation of teams, identification of key persons for certain tasks, and tracking the flow of information across the project community, are some pieces of information that could be captured by employing social sensing in the field. This information can not only be used during the construction to improve the site processes but it can also be exploited by the end user during maintenance of the building. We highlight the challenges that need to be overcome for this mobile and social sensing system to become a reality. © 2012 ACM.

Visual pattern recognition models for remote sensing of civil infrastructure

As-built models have been proven useful in many project-related applications, such as progress monitoring and quality control. However, they are not widely produced in most projects because a lot of effort is still necessary to manually convert remote sensing data from photogrammetry or laser scanning to an as-built model. In order to automate the generation of as-built models, the first and fundamental step is to automatically recognize infrastructure-related elements from the remote sensing data. This paper outlines a framework for creating visual pattern recognition models that can automate the recognition of infrastructure-related elements based on their visual features. The framework starts with identifying the visual characteristics of infrastructure element types and numerically representing them using image analysis tools. The derived representations, along with their relative topology, are then used to form element visual pattern recognition (VPR) models. So far, the VPR models of four infrastructure-related elements have been created using the framework. The high recognition performance of these models validates the effectiveness of the framework in recognizing infrastructure-related elements.

Detecting air pockets for architectural concrete quality assessment using visual sensing

Air pockets, one kind of concrete surface defects, are often created on formed concrete surfaces during concrete construction. Their existence undermines the desired appearance and visual uniformity of architectural concrete. Therefore, measuring the impact of air pockets on the concrete surface in the form of air pockets is vital in assessing the quality of architectural concrete. Traditionally, such measurements are mainly based on in-situ manual inspections, the results of which are subjective and heavily dependent on the inspectors’ own criteria and experience. Often, inspectors may make different assessments even when inspecting the same concrete surface. In addition, the need for experienced inspectors costs owners or general contractors more in inspection fees. To alleviate these problems, this paper presents a methodology that can measure air pockets quantitatively and automatically. In order to achieve this goal, a high contrast, scaled image of a concrete surface is acquired from a fixed distance range and then a spot filter is used to accurately detect air pockets with the help of an image pyramid. The properties of air pockets (the number, the size, and the occupation area of air pockets) are subsequently calculated. These properties are used to quantify the impact of air pockets on the architectural concrete surface. The methodology is implemented in a C++ based prototype and tested on a database of concrete surface images. Comparisons with manual tests validated its measuring accuracy. As a result, the methodology presented in this paper can increase the reliability of concrete surface quality assessment

Visual Pattern Recognition Models for Remote Sensing of Civil Infrastructure

As-built models have been proven useful in many project-related applications, such as progress monitoring and quality control. However, they are not widely produced in most projects because a lot of effort is still necessary to manually convert remote sensing data from photogrammetry or laser scanning to an as-built model. In order to automate the generation of as-built models, the first and fundamental step is to automatically recognize infrastructure-related elements from the remote sensing data. This paper outlines a framework for creating visual pattern recognition models that can automate the recognition of infrastructure-related elements based on their visual features. The framework starts with identifying the visual characteristics of infrastructure element types and numerically representing them using image analysis tools. The derived representations, along with their relative topology, are then used to form element visual pattern recognition (VPR) models. So far, the VPR models of four infrastructure-related elements have been created using the framework. The high recognition performance of these models validates the effectiveness of the framework in recognizing infrastructure-related elements.

Civil and Environmental Engineering Challenges for Data Sensing and Analysis

The objective of this study was to identify challenges in civil and environmental engineering that can potentially be solved using data sensing and analysis research. The challenges were recognized through extensive literature review in all disciplines of civil and environmental engineering. The literature review included journal articles, reports, expert interviews, and magazine articles. The challenges were ranked by comparing their impact on cost, time, quality, environment and safety. The result of this literature review includes challenges such as improving construction safety and productivity, improving roof safety, reducing building energy consumption, solving traffic congestion, managing groundwater, mapping and monitoring the underground, estimating sea conditions, and solving soil erosion problems. These challenges suggest areas where researchers can apply data sensing and analysis research.

Grand Challenges for Data Sensing and Analysis

A diverse group of experts proposed the 9 grand challenges outlined in this booklet. This expert task force was assembled by the ASCE TCCIT Data Sensing and Analysis (DSA) Committee and endorsed by the TRB AFH10(1) Construction IT joint subcommittee at the request of their membership. The task force did not rank the challenges selected, nor did it endorse particular approaches to meeting them. Rather than attempt to include every important goal for data sensing and analysis, the panel chose opportunities that were both achievable and sustainable to help people and the planet thrive. The panel’s conclusions were reviewed by several subject-matter experts. The DSA is offering an opportunity to comment on the challenges by contacting the task force chair via email at becerik@usc.edu.

Mode-localized displacement sensing

We report the construction of a new class of micromachined displacement sensors that employ the phenomenon of vibration-mode localization for monitoring minute inertial displacements. It is demonstrated both theoretically and experimentally that the eigenstate-shifted output signal of such mode-localized displacement sensors may be as high as 1000 times greater than corresponding resonant-frequency variations that serve as the output in the more traditional vibratory resonant micromechanical displacement/motion sensors. The high parametric sensitivities attainable in such mode-localized displacement sensors, together with their inherent advantages of improved environmental robustness and electrical tunability, suggest an alternative approach in achieving improved sensitivity and stability in high-resolution displacement transduction. © 1992-2012 IEEE.

A Novel Passive UHF RFID Real Time Location Sensing System

Ubiquitous in-building Real Time Location Systems (RTLS) today are limited by costly active radio frequency identification (RFID) tags and short range portal readers of low cost passive RFID tags. We, however, present a novel technology locates RFID tags using a new approach based on (a) minimising RFID fading using antenna diversity, frequency dithering, phase dithering and narrow beam-width antennas, (b) measuring a combination of RSSI and phase shift in the coherent received tag backscatter signals and (c) being selective of use of information from the system by, applying weighting techniques to minimise error. These techniques make it possible to locate tags to an accuracy of less than one metre. This breakthrough will enable, for the first time, the low-cost tagging of items and the possibility of locating them at relatively high precision.

SOI sensing technologies for harsh environment

This paper reviews and addresses certain aspects of Silicon-On-Insulator (SOI) technologies for a harsh environment. The paper first describes the need for specialized sensors in applications such as (i) domestic and other small-scale boilers, (ii) CO2 Capture and Sequestration, (iii) oil & gas storage and transportation, and (iv) automotive. We describe in brief the advantages and special features of SOI technology for sensing applications requiring temperatures in excess of the typical bulk silicon junction temperatures of 150oC. Finally we present the concepts, structures and prototypes of simple and smart micro-hotplate and Infra Red (IR) based emitters for NDIR (Non Dispersive IR) gas sensors in harsh environments. © 2012 IEEE.