Nonparametric estimators are widely used to estimate the productive efficiency of firms and other organizations, but often without any attempt to make statistical inference. Recent work has provided statistical properties of these estimators as well as methods for making statistical inference, and a link between frontier estimation and extreme value theory has been established. New estimators that avoid many of the problems inherent with traditional efficiency estimators have also been developed; these new estimators are robust with respect to outliers and avoid the well-known curse of dimensionality. Statistical properties, including asymptotic distributions, of the new estimators have been uncovered. Finally, several approaches exist for introducing environmental variables into production models; both two-stage approaches, in which estimated efficiencies are regressed on environmental variables, and conditional efficiency measures, as well as the underlying assumptions required for either approach, are examined. (This abstract was borrowed from another version of this item.)

Estimation and Inference in Nonparametric Frontier Models: Recent Developments and Perspectives

100% recycled hot mix asphalt lab samples were modified with five generic and one proprietary rejuvenators at 12% dose and tested for binder and mixture properties. Waste Vegetable Oil, Waste Vegetable Grease, Organic Oil, Distilled Tall Oil, and Aromatic Extract reduced the Superpave performance grade (PG) from 94–12 of extracted binder to PG 64-22 while waste engine oil required higher dose. All products ensured excellent rutting resistance while providing longer fatigue life when compared to virgin mixtures and most lowered critical cracking temperature. Rejuvenated samples required more compaction energy compared to virgin and some oils reduced moisture resistance slightly.

Influence of Six Rejuvenators on the Performance Properties of Reclaimed Asphalt Pavement (RAP) Binder and 100% Recycled Asphalt Mixtures

The development of a generalised framework for assessing bridge life-cycle performance and cost, with emphasis on analysis, prediction, optimisation and decision-making under uncertainty, is briefly addressed. The central issue underlying the importance of the life-cycle approach to bridge engineering is the need for a rational basis for making informed decisions regarding design, construction, inspection, monitoring, maintenance, repair, rehabilitation, replacement and management of bridges under uncertainty which is carried out by using multi-objective optimisation procedures that balance conflicting criteria such as performance and cost. A number of significant developments are summarised, including time-variant reliability, risk, resilience, and sustainability of bridges, bridge transportation networks and interdependent infrastructure systems. Furthermore, the effects of climate change on the probabilistic life-cycle performance assessment of highway bridges are addressed. Moreover, integrati...

Bridge life-cycle performance and cost: analysis, prediction, optimisation and decision-making

The growing importance of sustainable development as a policy objective has initiated a debate about those suitable frameworks and tools useful for policy makers when making a sustainable decision. Composite indicators (CIs) aggregate multidimensional issues into one index, thus providing comprehensive information. However, it is frequently argued that CIs are too subjective, as their results undesirably depend on the normalization method, a specific weighting scheme, and the aggregation method of sub-indicators. This article applies different combinations of normalization, weighting, and aggregation methods for the assessment of an industrial case study, with the aim of determining the best scheme for constructing CIs. The applied methodology gradually aggregates sustainable development indicators into sustainability sub-indices and, finally, to a composite sustainability index. The normalization methods included in this analysis are: minimum–maximum, distance to a reference, and the percentages of annual differences over consecutive years. Equal weightings, the ‘benefit of the doubt’ approach, and budget allocation process were used for determining the weights of individual indicators and sustainability sub-indices. The linear, geometric, and non-compensatory multi-criteria approaches (NCMCs) were used as aggregation methods. The NCMC is modified to fit the two-level aggregation, then to sub-indices, and finally to a composite sustainable index. Also, a penalty criterion is introduced into the evaluation process with the aim of motivating the company to move towards sustainable development. The results are analyzed by variance-based sensitivity analysis. According to the results the recommended scheme for CIs’ construction is: distance to a reference–benefit of the doubt–linear aggregation.

Sustainability performance evaluation in industry by composite sustainability index

Value-added application of waste PET based additives in bituminous mixtures containing high percentage of reclaimed asphalt pavement (RAP)

The development of a benchmarking tool for monitoring progress towards sustainable transportation in New Zealand

Maintenance decision making that selects appropriate maintenance strategies for a road network is an important and complex part of infrastructure asset management (IAM). Multiobjective optimization (MOO) can help in simplifying a decision making problem with multiple objectives and trading off objectives by identifying efficient solutions. Therefore, MOO is a helpful tool in the decision-making process. The main objective of this study is to analyze the optimization problem involved in practical decision-making processes using MOO and to identify efficient solutions in the context of maintenance decision making. To accomplish this aim, this paper (1) introduces decision making in IAM along with a discussion of previous applications of optimization; (2) discusses the mathematical formulation of optimization problems in the context of IAM decision making; (3) proposes an optimization method known as dichotomic approach (DA) to solve the optimization problems of decision making by identifying efficie...

Multiobjective Optimization for Maintenance Decision Making in Infrastructure Asset Management

Following the extensive use of sustainability rating tools for buildings, similar tools for civil infrastructure have emerged. This study presents four industry-based infrastructure sustainability ...

Comparison of project performance assessed by infrastructure sustainability rating tools

Current road roughness deterioration modelling and analysis tends to focus on the prediction of roughness progression in terms of change in the international roughness index (IRI) over time. Since the IRI is simply a summary index of the actual roughness, which simulates the response of a specific type of vehicle (quarter-car), it is difficult to identify the factors that contribute to the deterioration of road roughness. Understanding the factors that lead to the deterioration of roads and identifying the actual mode of road roughness deterioration will help road controlling authorities refine their specifications on road roughness requirements for road design, construction and maintenance to reduce their adverse influence on roughness. This research project looked at an alternative method to analyse and define the roughness deterioration modes of different pavement sections of the New Zealand road network by analysing the characteristics of the longitudinal profile of the road surface using wavelet analysis. This characterisation process was used to analyse the effects of pavement type, traffic loading, environment and maintenance regime on the deterioration of road roughness and ultimately should lead to the development of a strategy for maintaining road roughness of different pavement types commonly found in New Zealand road networks.

Identifying pavement deterioration by enhancing the definition of road roughness

This thesis presents the results of developing road pavement deterioration models for the State Highway network in New Zealand pavement deterioration models are an integral part of pavement management systems, which are used to forecast long-term maintenance needs and funding requirements on a road network. 
As part of this research, a Long-term Pavement Performance (LTPP) programme has been established on 63 sections of the State Highways. These sections are representative of typical road sections and climatic conditions on New Zealand roads. Data collection on these sections is undertaken on an annual basis and consists of high accuracy manual measurements. These measurements include road roughness, rutting, visual defect identification and strength testing with a Falling Weight Deflectometer. 
Based on the LTPP data, new model formats for New Zealand conditions were developed including a crack initiation model and a three-stage rut progression model. The rut progression model consists of three stages, initial densification, stable rut growth and a probabilistic model to predict accelerated rut progression. The continuous probabilistic model developed predicts the initiation of pavement failure events such as crack initiation and accelerated rutting. It has been found that this model type has a strong agreement with actual pavement behaviour as it recognises a distribution of failure on roads rather than failure occurring at an particular point in time, namely, a year. 
The modelling of rut progression in the three stages including, initial densification, stable rut progression and accelerated rutting has resulted in a significant increased understanding of this defect, especially for thin flexible chip seal pavements. It has been established that the in-service performance of these pavements is relatively predictable. However, incorporating both the in-service performance and the failure of pavements into one model was unrealistic. Therefore, by having the different stages of rutting, resulted into a more accurate forecasting of this defect. 
Although this research has covered the two priority pavement models including cracking and rutting prediction, it has established the model framework for other pavement models to be developed. As more data become available, further work can be undertaken to refine the models and to extend the research into the performance of alternative construction materials.

Theunis F. P. Henning

Papers

The development of a benchmarking tool for monitoring progress towards sustainable transportation in New Zealand

Multiobjective Optimization for Maintenance Decision Making in Infrastructure Asset Management

Comparison of project performance assessed by infrastructure sustainability rating tools

Identifying pavement deterioration by enhancing the definition of road roughness

The development of pavement deterioration models on the state highway network of New Zealand