A research-based approach to exploring the adoption of novel UK-based concrete test methods in Chinese construction sector

As part of a UK-China science bridge project - a UK government funded initiative linking leading universities and businesses in selective partnering countries in 2009 a collaborative research programme was initiated between Queen's University and the Research Institute of High Performance Concrete (part of the Central Research Institute of Building and Construction) in Beijing.

For further details email b.magee@ulster.ac.uk

The effect of relationship management on project performance in construction

Few studies to date have paid attention to relationship management when addressing performance problems. To bridge the knowledge gap, a questionnaire survey is conducted to analyze the impact of relationship management on project performance in construction. The analysis reveals that the deterioration of working relationships may increase the likelihood of poor performance. Poor performance can be effectively reduced by improving some aspects of working relationships. Collaborative working contributes to performance improvement, in which long-term collaboration is more effective than short-term collaboration. In addition to the questionnaire survey, a series of expert interviews provide a deeper insight into effective relationship management.

A Novel Approach for Axle Detection in Bridge Weigh-In-Motion Systems

Bridge weigh-in-motion (B-WIM), a system that uses strain sensors to calculate the weights of trucks passing on bridges overhead, requires accurate axle location and speed information for effective performance. The success of a B-WIM system is dependent upon the accuracy of the axle detection method. It is widely recognised that any form of axle detector on the road surface is not ideal for B-WIM applications as it can cause disruption to the traffic (Ojio & Yamada 2002; Zhao et al. 2005; Chatterjee et al. 2006). Sensors under the bridge, that is Nothing-on-Road (NOR) B-WIM, can perform axle detection via data acquisition systems which can detect a peak in strain as the axle passes. The method is often successful, although not all bridges are suitable for NOR B-WIM due to limitations of the system. Significant research has been carried out to further develop the method and the NOR algorithms, but beam-and-slab bridges with deep beams still present a challenge. With these bridges, the slabs are used for axle detection, but peaks in the slab strains are sensitive to the transverse position of wheels on the beam. This next generation B-WIM research project extends the current B-WIM algorithm to the problem of axle detection and safety, thus overcoming the existing limitations in current state-of–the-art technology. Finite Element Analysis was used to determine the critical locations for axle detecting sensors and the findings were then tested in the field. In this paper, alternative strategies for axle detection were determined using Finite Element analysis and the findings were then tested in the field. The site selected for testing was in Loughbrickland, Northern Ireland, along the A1 corridor connecting the two cities of Belfast and Dublin. The structure is on a central route through the island of Ireland and has a high traffic volume which made it an optimum location for the study. Another huge benefit of the chosen location was its close proximity to a nearby self-operated weigh station. To determine the accuracy of the proposed B-WIM system and develop a knowledge base of the traffic load on the structure, a pavement WIM system was also installed on the northbound lane on the approach to the structure. The bridge structure selected for this B-WIM research comprised of 27 pre-cast prestressed concrete Y4-beams, and a cast in-situ concrete deck. The structure, a newly constructed integral bridge, spans 19 m and has an angle of skew of 22.7°.