Beyond words: Testing alignment among inter-organizational performance measures

Purpose: This paper seeks to measure in a quantitative way the degree of alignment among a set of performance measures between two organizations. Design/methodology/approach: This paper extends Venkatraman's test of coalignment to assess the alignment of a set of performance measures governing a contractual inter-organizational relationship. The authors applied the test and present coefficients of misalignment across three sets of measures: those used by a service provider involved in the research, those used by customers contracting the services, and those documented in 11 contracts studied. Findings: Results confirmed a high degree of alignment between target and actual operational performance in the contracts. The alignment of customers' financial objectives and contracts' operational metrics was low. Calculations show poor alignment between the objectives of the provider and the contribution received from the contracts. Research limitations/implications: Some limitations of the conclusions include the small sample of contracts used in the calculations. Further research should include not only actual contracts, but also failed ones. Practical implications: It is possible that misaligned goals, represented in misaligned performance measures, lead to tensions in intra-firm relationships. If these tensions are not addressed properly the relationship could be unstable or terminated prematurely. This method of measuring alignment could detect early potential dangers in intra-firm relationships. Originality/value: This paper extends Venkatraman's test of coalignment to assess the alignment of a set of performance measures governing a contractual inter-organizational relationship. Management researchers and business professionals may use this methodology when exploring degrees of alignment of performance measures in intra-functional and inter-firm relationships. © Emerald Group Publishing Limited.

Towards robust part tracking in an automated manufacturing process using RFID

Automated Identification and in particular, Radio Frequency Identification (RFID) promises to assist with the automation of mass customised production processes. RFID has long been used to gather a history or trace of part movements, but the use of it as an integral part of the control process is yet to be fully exploited. Such use places stringent demands on the quality of the sensor data and the method used to interpret that data. in particular, this paper focuses on the issue of correctly identifying, tracking and dealing with aggregated objects with the use of RFID. The presented approach is evaluated in the context of a laboratory manufacturing system that produces customised gift boxes. Copyright © 2005 IFAC.

Centrifuge modelling and dynamic testing of municipal solid waste (MSW) landfills

An investigation into the seismic behaviour of municipal solidwaste (MSW) landfills by dynamic centrifuge testing was undertaken. This paper presents physical modelling of MSW landfills for dynamic centrifuge testing, with regard to the following research areas: 1. amplification characteristics of municipal solid waste; 2. tension induced in geomembranes placed on landfill slopes due to earthquake loading; 3. damage to landfill liners due to liquefaction of foundation soil. A model waste, that has engineering properties similar to MSW, is presented. A model geomembrane that can be used in centrifuge tests is also presented. Results of dynamic centrifuge tests with the model geomembrane showed that an earthquake loading induces additional permanent tension (∼25%) in the geomembrane. © 2006 Taylor & Francis Group, London.

Experience using MEMS-based accelerometers in dynamic testing

Measurement of acceleration in dynamic tests is carried out routinely, and in most cases, piezoelectric accelerometers are used at present. However, a new class of instruments based on MEMS technology have become available and are gaining use in many applications due to their small size, low mass and low-cost. This paper describes a centrifuge lateral spreading experiment in which MEMS and piezoelectric accelerometers were placed at similar depths. Good agreement was obtained when the instruments were located in dense sands, but significant differences were observed in loose, liquefiable soils. It was found that the performance of the piezoelectric accelerometer is poor at low frequency, and that the relative phase difference between the piezoelectric and MEMS accelerometer varies significantly at low frequency. © 2010 Taylor & Francis Group, London.

Visual retrieval of concrete crack properties for automated post-earthquake structural safety evaluation

The safety of post-earthquake structures is evaluated manually through inspecting the visible damage inflicted on structural elements. This process is time-consuming and costly. In order to automate this type of assessment, several crack detection methods have been created. However, they focus on locating crack points. The next step, retrieving useful properties (e.g. crack width, length, and orientation) from the crack points, has not yet been adequately investigated. This paper presents a novel method of retrieving crack properties. In the method, crack points are first located through state-of-the-art crack detection techniques. Then, the skeleton configurations of the points are identified using image thinning. The configurations are integrated into the distance field of crack points calculated through a distance transform. This way, crack width, length, and orientation can be automatically retrieved. The method was implemented using Microsoft Visual Studio and its effectiveness was tested on real crack images collected from Haiti.

Automated Computation of the Fundamental Matrix for Vision-Based Construction Site Applications

Estimating the fundamental matrix (F), to determine the epipolar geometry between a pair of images or video frames, is a basic step for a wide variety of vision-based functions used in construction operations, such as camera-pair calibration, automatic progress monitoring, and 3D reconstruction. Currently, robust methods (e.g., SIFT + normalized eight-point algorithm + RANSAC) are widely used in the construction community for this purpose. Although they can provide acceptable accuracy, the significant amount of required computational time impedes their adoption in real-time applications, especially video data analysis with many frames per second. Aiming to overcome this limitation, this paper presents and evaluates the accuracy of a solution to find F by combining the use of two speedy and consistent methods: SURF for the selection of a robust set of point correspondences and the normalized eight-point algorithm. This solution is tested extensively on construction site image pairs including changes in viewpoint, scale, illumination, rotation, and moving objects. The results demonstrate that this method can be used for real-time applications (5 image pairs per second with the resolution of 640 × 480) involving scenes of the built environment.

Automated sparse 3D point cloud generation of infrastructure using its distinctive visual features

The commercial far-range (>10 m) spatial data collection methods for acquiring infrastructure’s geometric data are not completely automated because of the necessary manual pre- and/or post-processing work. The required amount of human intervention and, in some cases, the high equipment costs associated with these methods impede their adoption by the majority of infrastructure mapping activities. This paper presents an automated stereo vision-based method, as an alternative and inexpensive solution, to producing a sparse Euclidean 3D point cloud of an infrastructure scene utilizing two video streams captured by a set of two calibrated cameras. In this process SURF features are automatically detected and matched between each pair of stereo video frames. 3D coordinates of the matched feature points are then calculated via triangulation. The detected SURF features in two successive video frames are automatically matched and the RANSAC algorithm is used to discard mismatches. The quaternion motion estimation method is then used along with bundle adjustment optimization to register successive point clouds. The method was tested on a database of infrastructure stereo video streams. The validity and statistical significance of the results were evaluated by comparing the spatial distance of randomly selected feature points with their corresponding tape measurements.

Automated vision tracking of project related entities

Tracking of project related entities such as construction equipment, materials, and personnel is used to calculate productivity, detect travel path conflicts, enhance the safety on the site, and monitor the project. Radio frequency tracking technologies (Wi-Fi, RFID, UWB) and GPS are commonly used for this purpose. However, on large-scale sites, deploying, maintaining and removing such systems can be costly and time-consuming. In addition, privacy issues with personnel tracking often limits the usability of these technologies on construction sites. This paper presents a vision based tracking framework that holds promise to address these limitations. The framework uses videos from a set of two or more static cameras placed on construction sites. In each camera view, the framework identifies and tracks construction entities providing 2D image coordinates across frames. Combining the 2D coordinates based on the installed camera system (the distance between the cameras and the view angles of them), 3D coordinates are calculated at each frame. The results of each step are presented to illustrate the feasibility of the framework.

Testing in harsh conditions: Tracking resources on construction sites with machine vision

When tracking resources in large-scale, congested, outdoor construction sites, the cost and time for purchasing, installing and maintaining the position sensors needed to track thousands of materials, and hundreds of equipment and personnel can be significant. To alleviate this problem a novel vision based tracking method that allows each sensor (camera) to monitor the position of multiple entities simultaneously has been proposed. This paper presents the full-scale validation experiments for this method. The validation included testing the method under harsh conditions at a variety of mega-project construction sites. The procedure for collecting data from the sites, the testing procedure, metrics, and results are reported. Full-scale validation demonstrates that the novel vision tracking provides a good solution to track different entities on a large, congested construction site.