Developing Workforce Resourcing Best Practice for Subcontractors in Christ…

Research papers, The University of Auckland Library

The skills agenda has grown in prominence within the construction industry. Indeed, skill shortages have been recognised as a perennial problem the construction industry faces, especially after a major disaster. In the aftermath of the Christchurch earthquakes, small and medium construction companies were at the forefront of rebuilding efforts. While the survival of these companies was seen to be paramount, and extreme events were seen to be a threat to survival, there is a dearth of research centring on their resourcing capacity following a disaster. This research aims to develop workforce resourcing best practice guidelines for subcontractors in response to large disaster reconstruction demands. By using case study methods, this research identified the challenges faced by subcontracting businesses in resourcing Christchurch recovery projects; identified the workforce resourcing strategies adopted by subcontracting businesses in response to reconstruction demand; and developed a best practice guideline for subcontracting businesses in managing the workforce at the organisational and/or project level. This research offers a twofold contribution. First, it provides an overview of workforce resourcing practices in subcontracting businesses. This understanding has enabled the development of a more practical workforce resourcing guideline for subcontractors. Second, it promotes evidence-informed decision-making in subcontractors’ workforce resourcing. Dynamics in workforce resourcing and their multifaceted interactions were explicitly depicted in this research. More importantly, this research provides a framework to guide policy development in producing a sustainable solution to skill shortages and establishing longterm national skill development initiatives. Taken together, this research derives a research agenda that maps under-explored areas relevant for further elaboration and future research. Prospective researchers can use the research results in identifying gaps and priority areas in relation to workforce resourcing.

On the Relationship Between Geospatial Liquefaction-Model Performance and …

Research papers, University of Canterbury Library

Geospatial liquefaction models aim to predict liquefaction using data that is free and readily-available. This data includes (i) common ground-motion intensity measures; and (ii) geospatial parameters (e.g., among many, distance to rivers, distance to coast, and Vs30 estimated from topography) which are used to infer characteristics of the subsurface without in-situ testing. Since their recent inception, such models have been used to predict geohazard impacts throughout New Zealand (e.g., in conjunction with regional ground-motion simulations). While past studies have demonstrated that geospatial liquefaction-models show great promise, the resolution and accuracy of the geospatial data underlying these models is notably poor. As an example, mapped rivers and coastlines often plot hundreds of meters from their actual locations. This stems from the fact that geospatial models aim to rapidly predict liquefaction anywhere in the world and thus utilize the lowest common denominator of available geospatial data, even though higher quality data is often available (e.g., in New Zealand). Accordingly, this study investigates whether the performance of geospatial models can be improved using higher-quality input data. This analysis is performed using (i) 15,101 liquefaction case studies compiled from the 2010-2016 Canterbury Earthquakes; and (ii) geospatial data readily available in New Zealand. In particular, we utilize alternative, higher-quality data to estimate: locations of rivers and streams; location of coastline; depth to ground water; Vs30; and PGV. Most notably, a region-specific Vs30 model improves performance (Figs. 3-4), while other data variants generally have little-to-no effect, even when the “standard” and “high-quality” values differ significantly (Fig. 2). This finding is consistent with the greater sensitivity of geospatial models to Vs30, relative to any other input (Fig. 5), and has implications for modeling in locales worldwide where high quality geospatial data is available.