Deterioration modeling is an important analytical component in infrastructure asset management. It concerns the prediction of performance and remaining service life of assets of different designs under omnifarious working environments. For long-term prediction, it also requires to characterize maintenance effectiveness because maintenance activities do not necessarily bring an asset to a completely renewed status. Deterioration modeling research has for decades been largely focusing on the modeling of the natural deterioration process per se, whereas the modeling of maintenance effectiveness is only a recent focus of investigation, mainly in pavement research. In practice, the asset conditions immediately before and after a given maintenance treatment both are not often known. This has made the modeling of maintenance effectiveness and long-term deterioration prediction a challenging task. To bridge the gap, this paper presents a novel approach that integrates the modeling of deterioration and maintenance effectiveness into one process. The natural deterioration of asset performance is modeled as a continuous-time Markov chain, whereas the effectiveness of a maintenance measure is modeled as a discrete-time Markov chain. To account for missing condition data before and after the maintenance event, the paper also develops a robust statistical method based on Markov chain Monte Carlo simulation. A real-life case study on a municipal sewer pipe system is carried out for demonstration of the proposed integrated modeling approach. The functional deterioration of sewer pipes and the effectiveness of flushing operations that target to bring flow capacity to intact state are modeled. Influences of pipe length, diameter, slope, and sewershed area are examined. The present work is a valuable step toward development of evidence-based risk-informed asset management framework.

Robustness assessment is an important component for performance-based progressive collapse design. However, existing methods either do not consider the cascading failure feature of progress collapse, or fail to recognize the randomness in material, geometrical and loading parameters. This paper presents a novel robustness assessment methodology for progressive collapse design of reinforced concrete frames. The proposed methodology includes several novelties: First, it uses a new risk-based robustness index recently developed by the authors. The index quantifies the whole spectrum of risk caused by initiating hazardous events. Second, it includes a unique directional simulation technique, making probabilistic nonlinear pushdown analysis a computationally affordable task. Finally, the assessment can assist in determining if an enhancement design is warranted for such a low-probability-high-consequence event. The study examined four different frame designs to evaluate the effectiveness of seismic and progressive collapse design provisions. The Alternate Path Method (APM) was shown to improve structural robustness significantly, although achieved with considerable additional cost. Ductility designed for seismic loading was also shown to be beneficial for structural robustness against progressive collapse.

This study presents reliability analyses of soil nail walls (SNWs) against global and sliding stability failures in layered ground. The load and resistance factor design (LRFD) has been studied and advocated for SNW design over the past few years. However, the majority of those studies were exclusively focused on SNWs in single-layer ground and few have investigated the case of layered ground. In this paper, the reliability is evaluated using the first-order reliability method (FORM) for SNWs that are designed based on traditional working stress design (WSD) methods. Based on analyses of a great variety of combinations of two soils, a stiff clay layer and a dense sand layer, reliability indices calibrated from traditional WSD exceed 3.0 for global stability designs but may be lower than 3.0 for sliding stability designs. This study also reveals that the sequence and the thickness ratio of layers are two important factors for the reliability of both global and sliding stability modes. SNWs in layer...

Cost overrun and schedule delay are pervasive in large public infrastructure projects in Canada and there is an ongoing debate over the root causes of these delivery risks and how best to mitigate them. Since public-private partnerships (P3s) have been long touted as an effective mitigating measure, this study aimed to provide comparative empirical evidence to explore this claim. By collecting cost and schedule data at three key milestones (decision to build, contract awarding and substantial completion) from 39 traditional projects and 27 P3 projects across Canada between 2004 and 2016, the study develops an innovative longitudinal profiling approach to inform the debate about the causes of cost overruns in large public projects. Statistical analyses of the collected data unambiguously demonstrate that P3s outperform non-P3 traditional delivery models during the construction stage in both cost overrun and schedule delay. However, substantial budget underestimation is found in the strategic decision-making process of both models of project delivery. Results from the study also provide solid objective grounds for the development of advanced value-for-money assessment methodology.