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Because pervious concrete pavement (PCP) has a porous structure and can percolate water to an underground layer, it has been proposed as a stormwater best management practice (BMP), an environmentally friendly product, and sustainable paving materials. This porosity makes PCP susceptible to freeze-thaw damage in cold climates. Therefore, PCP has not been widely applied and investigated in such a climate. Long-term performance data are rarely available, and no performance model has been developed for PCP to date. The main objective of this research is to integrate expert knowledge (using the Markov-chain process) and experimental data (PCP field investigations) to build a performance model for PCP through incorporation of the Bayesian technique. The combination of these sources of data is an efficient and effective approach to build a performance model for a new type of pavement, such as PCP, which has not had a long-term performance database. As a result, a robust linear performance model is developed and applied to predict the service life of PCP. The service life of PCP is estimated to be approximately nine years using the developed performance model. In general, the expert knowledge leads to more conservative results rather than experimental data. DOI: 10.1061/(ASCE)TE.1943-5436.0000363. © 2012 American Society of Civil Engineers.

Pervious concrete pavement (PCP) has the potential to provide significant technical, economical, and environmental benefits. To better understand the beneficial impacts of PCP, its performance must be comprehensively evaluated and quantified. To evaluate the PCP performance, its pavement condition should be assessed. The primary aim of this research is to evaluate the PCP condition through incorporation of an adequate panel rating method. Two panel rating methods are evaluated for this purpose: visual digital inspection and in situ inspection. Field investigations are also conducted to record the surface distresses by describing their severity and density using the Ministry of Transportation of Ontario protocol. Finally, a model is developed and validated which relates mean panel rating (i.e., surface distress rating) to a pavement distress. It is concluded that the only significant variable (i.e., distress) in this model is raveling.

Pervious concrete pavement (PCP) is listed as one of the best stormwater management practices by the Environmental Protection Agency (EPA) due to its capacity to reduce impervious areas, capability to improve water quality, reduce runoff quantity, eliminate the need for detention basins, protect downstream channels, and reduce flooding (save lives and properties). Moreover, it is designated a “Green Asset” since it allows stormwater to seep through pavement to recharge ground water level. PCP may potentially improve skid resistance, reduce noise, decrease heat, and reduce water contamination. However, since PCP must have adequate permeability (porous structure), it can potentially be susceptible to freeze–thaw damage particularly in cold climates. To deploy the PCP in a transportation network, its performance should be thoroughly evaluated. For this purpose, the condition of PCP has to be defined and determined over time. Since PCP has not been widely investigated in cold climates such as Canada, no condition indices have been defined for it. This paper proposes a combined condition index for PCP to ensure that it will perform adequately over time. To develop a combined condition index, both a panel rating approach and objective measurements are employed to assess surface distresses and permeability rates of specific PCP sections. The panel rating method is applied due to the lack of long-term performance data to evaluate PCP sections’ conditions with regards to surface distress (e.g., raveling, spalling, and cracking) and functional performance (i.e., permeability rate). The objective measurements of surface distress and permeability rate (functional performance) of the same PCP sections are performed through the application of the adjusted Ministry of Transportation of Ontario (MTO) protocol and a permeameter, respectively. Regression analysis is used to relate surface objective measurements to the surface panel rating. As a result of statistical analyses, the panel rating method is successfully applied only in surface distress rating. Accordingly, a combined condition index is developed using surface distress ratings and objective functional performance measures.

Pervious concrete pavement (PCP) has the potential to provide significant sustainability benefits. To understand better the significance of PCP, its performance should be evaluated comprehensively throughout its service life. Because PCP performance has not been investigated thoroughly, no condition indices have been developed for it. This paper aims to develop an overall combined condition index to investigate PCP performance. To develop the index, a specific panel rating method and field performance investigations were conducted. The panel subjectively evaluated surface conditions and permeability rates of various PCP sections. The field investigations involved objective measurements of surface conditions and permeability rates of the same PCP sections. It is not practical to conduct a panel rating for PCP condition evaluation in the future; conducting field investigations (objective assessments) is more practical and cost-effective. Thus, regression analyses were applied to relate objective surface condition measures to subjective surface condition ratings. After objective surface condition measures were evaluated, subjective surface condition ratings were computed using the regression relationship. Permeability rates were combined with subjective surface condition ratings using adequate weighting factors to obtain the overall combined condition index.

Development of an adequate performance model based on an appropriate condition index has been a major challenge for engineers particularly in case of a new type of design such as pervious concrete pavement structures (PCPSs) which suffer from limited long term performance data sets especially in colder climates. Soft computing techniques are significantly efficient at dealing with subjective, incomplete, and limited data. This paper proposes three most effective soft computing methods: fuzzy sets, the Latin Hypercube Simulation technique, and the Markov Chain process. A novel comprehensive condition index based on severity, density, and weighting factors of distresses occurring on PCPS has been developed incorporating fuzzy sets. A combination of homogeneous and nonhomogeneous Markov Chain has been applied to develop performance models. Transition probability matrices are presented using probability distribution functions rather than single values. A simulation technique is then used to incorporate the probability distribution function operations to compute the future condition of the pavements. The future performance of the pavements is expressed by both single expected values and suitable probability distribution functions. Ultimately, a probabilistic versus deterministic performance curve is presented.

Since the use of pervious concrete pavement structures (PCPSs) is essentially still in the trial stage in Canada, long-term and quantitative pavement condition data are not available. The existing approaches applied to assess pervious concrete pavement structure (PCPS) conditions are ad hoc and suffer from methodological limitations. A fuzzy set technique is proposed herein as an efficient tool for dealing with qualitative and incomplete pavement condition data on distress types, severities, densities, and weighting factors. Using this method, a comprehensive fuzzy condition index was developed based on Ministry of Transportation of Ontario (MTO) methodology and using fuzzy pavement condition data. This fuzzy condition index was converted to a single value that allowed for comparisons of pavement conditions using several ranking techniques. A case study of 24 PCPS sites was utilized to demonstrate how the fuzzy representations of the condition index compared with associated single values. It is shown that this approach can effectively provide extensive condition indices for PCPSs and rank them accordingly, using only limited and imprecise pavement condition data.

Turnouts are of the most critical components of railway track which are prone to high static and dynamic forces leading to more intense degradation. They require more inspection than other parts of a railway track as they are potential safety hazards. As a result, turnout degradation processes are crucial to be understood by infrastructure manager to plan for their maintenance and renewal in advance. Two approaches have been introduced in the literature to achieve a thorough understanding of degradation processes in turnouts. The first one acts to develop degradation models based on influential parameters and historical data and then to predict degradation processes in the future; while the second one tries to improve inspection through using new concepts and technologies leading turnout condition data to be better captured over time. The purpose of this paper is to review all available resources regarding these two approaches and provide a guide for further research into turnout studies.