Introduction of a new continuous time and state space stochastic process in condition prediction

Periodic condition assessments of pavements together with condition predictions are the basis for investment decisions in every pavement management system (PMS). Typical approaches include surveys of distress types every 3–6 years with analysis rating and calculation of condition indices for road safety and/or structural health. Furthermore, advanced PMS prediction models allow a comparison of maintenance alternatives and an optimisation of investment strategies. This paper presents an overview of current survey and rating approaches in Germany, Switzerland and Austria, together with an impact analysis of different methods, utilised deterministic performance functions and condition threshold (trigger) values for all major distress types. The core of this paper is a comparison of common deterministic condition prediction models with discrete stochastic approaches and prediction models based on advanced regression techniques mainly from scientific literature and an innovative stochastic continuous time and continuous state space process (HOFFMANN – Process). All prediction models are applied to real-world data from condition surveys in Austria and the long-term pavement performance Database (USA) at single-section and network level. The paper provides evidence why deterministic prediction approaches are leading to substantial bias in condition distribution and remaining service life as they do not account for the stochastic nature of pavements. Classic Markov-chain approaches do not account for censoring of survey data and neglect changes in transition probabilities with increasing age. Applying common bivariate and multiple regression techniques may also lead to certain bias due to collinearity effects and specification bias. The paper provides mathematical evidence on ways to avoid these shortcomings based on the presented innovative stochastic process leading to a higher reliability in condition assessment, rating and accuracy of condition predictions. The aspects of censoring, distress-specific assignment and optimisation of treatments with this new HOFFMANN-process will be covered in forthcoming papers.     

Performance modelling of a solar road panel prototype using finite element analysis

Performance prediction is a critical step towards the acceptance of a new pavement structure. This is true for both conventional and innovative designs; however, it is particularly important for innovative designs that attempt to redefine pavement design practices. One such innovative design concept is the solar road panel; a road panel with a transparent surface that generates electricity through embedded solar cells. Despite the work completed by multiple organisations towards the development of this concept, questions exist about the viability of these panels as a structural pavement surface. This paper investigates these questions through a finite element modelling approach that assesses a prototype panel's performance on a variety of structural bases. Overall, this paper finds that it is possible to design a solar road panel to withstand traffic loading and that a concrete structural base allows for substantial optimisation to the analysed prototype design.     

Development of a post-flood road maintenance strategy: case study Queensland,Australia

Currently, no road authority takes into account flooding in road deterioration (RD) models; as a result, post-flood rehabilitation treatments may be sub-optimal. This paper proposes a new approach to the development of a post-flood maintenance strategy. The recently developed roughness and rutting-based RD models with flooding, by the current authors, are used as input to predict pavement deterioration after a flood (i.e. assuming a flood in year 1). The HDM-4 model has been used to get the post-flood maintenance strategy with constrained and unconstrained budget, where post-flood rehabilitation starts from year 2. The road groups in state road network of Queensland, Australia, are used as the case study. The unconstrained budget solution aims to keep the network in an excellent condition at a cost of $49.7bn with the possible strongest treatments. The constrained budget strategy uses agency cost and pavement performance as constraints in optimisation and provides a reasonable solution. This strategy requires about $26.1bn in life cycle, which is close to the main road authority of Queensland’s post-flood rehabilitation programme. The paper discusses two other strategies on maximise economic benefits and budget optimisation. It is expected that a road authority would properly investigate its flood-damaged roads before implementation. The paper shows pavement performances with the post-flood strategy. The need for a RD model to predict deterioration after a flood and for post-flood treatment selection is also highlighted.     

Hierarchical asphalt pavement deterioration model for climate impact studies

Quantification of the impacts of projected climate change on road pavement performance is possible using predictive models that correctly consider key causal factors of pavement deterioration. These factors include climate, traffic, properties of materials and the design of pavements. This paper presents a new model developed to predict rutting in asphalt surfacing. In addition to the key causal factors of road deterioration, the developed model takes into account several sources of uncertainties, particularly those inherent in future climate change predictions and model input parameters. The asphalt surfacing rut depth progression model was developed from a hierarchical road network data structure using a Bayesian regression approach resulting in a model for each surfacing group. The model was applied within a Monte Carlo simulation framework to derive probabilistic outputs of pavement rut depth progression and maintenance costs under the pre-determined future climate scenarios. This model is useful for application at both the network and project levels to develop road management strategies and policies.     

Risk based probabilistic pavement deterioration prediction models for low volume roads

Accurate prediction of pavement performance is important for efficient management of road infrastructure. Pavement performance prediction models developed for low-volume roads are mainly based on deterministic approach. The deterministic prediction models are inadequate to completely capture the deterioration mechanism. Uncertainties may occur in pavement behaviour under changing traffic loads and environment conditions, which may not be realistically represented by deterministic model. The objective of this paper is to develop pavement deterioration prediction models by probabilistic approach, for various distresses observed on low-volume roads in the state of Kerala in India, with the help of existing deterministic models. The major distresses observed on low-volume roads were ravelling, pothole and edge failure. Load-associated distresses were rarely observed on these roads as the maximum cumulative standard axle observed was only one million standard axle (msa). Hence, lack of proper drainage and construction quality (CQ) could be attributed as the major reasons for the pavement deterioration. Progression of deterioration of pavements with age has been studied and the intensity of distresses along with corresponding probabilities was arrived at. The distresses predicted by probabilistic models were compared with those predicted by deterministic models and the actual distress values observed in the field. The prediction models were validated using Mean Absolute Percentage Error, a statistical method for accuracy measurement of forecasting models. A risk analysis was then conducted to select the critical percentile value for each type of distress corresponding to varying pavement age. A sensitivity analysis was also carried out to study the effect of pavement age and CQ on the progression of pavement deterioration.     

Rutting progression models for light duty pavements

Deterioration models allow road managers to assess current condition and to predict future conditions of their road networks. Due to heavy vehicle axle repetitions and the effect of environmental factors, permanent deformation (rutting) develops gradually in the wheel paths and impacts on structural and surface performance of flexible pavements. This paper reports the approach adopted to develop absolute deterministic models for permanent deformation of low volume roads. A representative large sample network (23 highways) of light duty pavements was selected. For each section, time series data from four consecutive condition surveys were collected. Multiple regression analysis was carried out to develop models to predict pavement rutting progression over time as a function of a number of contributing variables. They include traffic loading, pavement strength, climate and drainage condition. For more powerful prediction, family group data-fitting approach was utilised to estimate future rutting progression based on the average rut depth curve for a series of pavements with similar characteristics. This study highlighted that separate family deterioration models are preferred and needed for more realistic results. The paper concludes that the analysis approach used for developing the models confirmed their accuracy and reliability by well-fitting to the validation data with low standard error values. Also, study results show that higher traffic loading, lower pavement strength, poor drainage and climates with high seasonal variation contribute to increasing rutting progression rate.     

Optimisation of maintenance strategy for rural road network using genetic algorithm

The objective of this paper is to develop an optimised maintenance strategy for the rural road network of Kerala state. This is accomplished with the development of a bi-objective deterministic optimisation model which simultaneously satisfies the objectives of both minimisation of total maintenance cost and maximisation of performance of the road network. The model is capable of planning the maintenance activities over a multi-year planning period. The performance of the road network is accounted using the composite index, namely Pavement Condition Index. The constraint-based genetic algorithm was used as the optimisation tool since it very well takes care of the combinatorial nature of the network-level pavement maintenance programming. The applicability of the model is illustrated using a case study for the rural road network of Kerala state in India. The effort made through this research work to develop a suitable Pavement Maintenance and Management System for rural road network can lead to the proper maintenance and upkeep of the rural roads, thereby triggering a positive impact on the Indian economy.     

Immediate effects of some corrective maintenance interventions on flexible pavements

Different maintenance interventions have different ability to address distresses on flexible pavements. Understanding the maintenance effects can benefit pavement maintenance decision-making. In this study, the immediate maintenance effects on roughness and rutting of three interventions including overlay, overlay with an additional base layer and mill and fill were studied and compared. A method was introduced to validate maintenance effect models, using the pavement management information from Virginia Department of Transportation. The method included a data mining process to extract data and apply regression analysis of maintenance effect models. The outliers in the analysis were detected and removed using the method of Cook’s distance. It was found that the immediate maintenance effects of overlay with base layer were greatest and mill and fill was least when treating pavements with moderate roughness (50–100 in/mi (≈ 0.8–1.6 m/km)). However, mill and fill was more useful for treating pavements with high roughness (>100 in/mi (≈1.6 m/km)). Furthermore, suggestions were proposed on data collection for road authorities to improve the prediction of maintenance effects.     

Local calibration of rigid pavement performance models using resampling methods

The performance prediction models in the Pavement-ME design software are nationally calibrated using in-service pavement material properties, pavement structure, climate and truck loadings, and performance data obtained from the Long-Term Pavement Performance programme. The nationally calibrated models may not perform well if the inputs and performance data used to calibrate those do not represent the local design and construction practices. Therefore, before implementing the new M-E design procedure, each state highway agency (SHA) should evaluate how well the nationally calibrated performance models predict the measured field performance. The local calibrations of the Pavement-ME performance models are recommended to improve the performance prediction capabilities to reflect the unique conditions and design practices. During the local calibration process, the traditional calibration techniques (split sampling) may not necessarily provide adequate results when limited number of pavement sections are available. Consequently, there is a need to employ statistical and resampling methodologies that are more efficient and robust for model calibrations given the data related challenges encountered by SHAs. The main objectives of the paper are to demonstrate the local calibration of rigid pavement performance models and compare the calibration results based on different resampling techniques. The bootstrap is a non-parametric and robust resampling technique for estimating standard errors and confidence intervals of a statistic. The main advantage of bootstrapping is that model parameters estimation is possible without making distribution assumptions. This paper presents the use of bootstrapping and jackknifing to locally calibrate the transverse cracking and IRI performance models for newly constructed and rehabilitated rigid pavements. The results of the calibration show that the standard error of estimate and bias are lower compared to the traditional sampling methods. In addition, the validation statistics are similar to that of the locally calibrated model, especially for the IRI model, which indicates robustness of the local model coefficients.     

Investigation of pedestrian crashes on two-way two-lane rural roads in Ethiopia

Understanding pedestrian crash causes and contributing factors in developing countries is critically important as they account for about 55% of all traffic crashes. Not surprisingly, considerable attention in the literature has been paid to road traffic crash prediction models and methodologies in developing countries of late. Despite this interest, there are significant challenges confronting safety managers in developing countries. For example, in spite of the prominence of pedestrian crashes occurring on two-way two-lane rural roads, it has proven difficult to develop pedestrian crash prediction models due to a lack of both traffic and pedestrian exposure data. This general lack of available data has further hampered identification of pedestrian crash causes and subsequent estimation of pedestrian safety performance functions. The challenges are similar across developing nations, where little is known about the relationship between pedestrian crashes, traffic flow, and road environment variables on rural two-way roads, and where unique predictor variables may be needed to capture the unique crash risk circumstances. This paper describes pedestrian crash safety performance functions for two-way two-lane rural roads in Ethiopia as a function of traffic flow, pedestrian flows, and road geometry characteristics. In particular, random parameter negative binomial model was used to investigate pedestrian crashes. The models and their interpretations make important contributions to road crash analysis and prevention in developing countries. They also assist in the identification of the contributing factors to pedestrian crashes, with the intent to identify potential design and operational improvements.     

A risk-based optimisation methodology for pavement management system of county roads

A pavement management system (PMS) is a strategic and systematic process to maintain and upgrade the road network. When funding is limited, it is very important to identify the best mix of road preservation projects that provides the maximum benefits to society in terms of overall life cycle cost of the road network. The most common factors that play an important role for identifying projects are the following: budget, traffic volume, Present Serviceability Index (PSI) and risk associated with selecting treatment types. This research develops an optimisation methodology for county paved roads that identify the best mix of preservation projects within budget, maximising traffic (passengers and trucks traffic) on treated roads, maximising the weighted average PSI, and minimising the risk. This methodology will facilitate a statewide implementation of PMS for counties in the state of Wyoming.     

New concepts for the assessment of concrete slab interfacial effects in pavement design and analysis

For many years, concrete pavement construction, whether new or overlay, has been done with a variety of layer interfaces ranging from strongly cemented having a high degree of shear strength to completely unstabilised having only internal frictional resistance between the individual particles. In this regard, both past and present design methodologies have been limited in their capability to address the bond between the slab and the underlying layers – essentially considering either unbonded or fully bonded conditions for design purposes. However, this limitation ignores a wide range of partially bonded conditions that can exist between these two limits that may consist of a variety of combinations of different levels of friction and adhesion. For most instances of design, unbonded conditions are principally hypothetical where qualification of the amount of adhesive strength and frictional restraint that develops along the interface between the slab and the underlying layer is the key to the characterisation of slab behaviour resulting in varying degrees of partial bond. This paper addresses a framework to model the effects of the concrete pavement slab–subbase interface for design purposes based on research relative to these and other factors as they may pertain to the prediction of short- and long-term performance.     

Tailoring performance evaluation to specific industrial contexts – application to sustainable mass customisation enterprises

This paper proposes an approach for measuring performance improvement through prioritising and aggregating performance indicators (PIs). The proposed approach helps practitioners build, in a formal way, their performance measurement systems. The novelty of the methodology lies in: (i) adapting PIs to specific industrial contexts according to companies concerns and (ii) supporting the decision-making process by providing a holistic and limited number of aggregated PIs. The context considered in the current research is sustainable mass customisation (SMC). Accordingly, a set of SMC indicators are used to illustrate the applicability of the methodology. The implementation and illustration with a real case showed some evidence of its usefulness and pointed out several improvement rooms.     

International roughness index models for HMA overlay treatment of flexible and composite pavements

Timely rehabilitation and preservation of pavement systems are imperative to minimising agency's costs and maximising benefits. Reasonable estimates of treatment life and pavement life extension can be made possible by developing reliable treatment performance models. Louisiana Department of Transportation and Development initiated a three-phased study to develop pavement treatment performance models in support of cost-effective selection of pavement treatment type and the time of treatment. As a result of the study, international roughness index (IRI) models for overlay treatment of composite and flexible pavements were developed. Various factors affecting the IRI of overlay treatment were identified. Climatic indices pertaining to Louisiana were developed which exhibited strong statistical significance along with the other variables as used in the IRI models. The developed IRI models provided good agreement between the measured and predicted IRI values with the majority of data within 5% of prediction error. The models could be used as a good pavement management tool for pavement maintenance and rehabilitation actions.     

Critical assessment 31: on the modelling of tertiary creep in single-crystal superalloys

Modelling of tertiary creep in single-crystal superalloys – operative over the majority of the temperature/stress regime of relevance – is assessed. Traditional, empirical approaches are useful for the prediction of component life; a modern trend is for more physically faithful models which account for microstructure and alloy composition. Calculations are made using different methods, to identify advantages/disadvantages, in an effort to approach best practice. The predictions are tested against experimental data. The creep resistance can be estimated to within approximately one-half of a Larson–Miller parameter, from an input of alloy composition, deformation conditions, and estimates of microstructural parameters, rate laws (and parameters therein), and physical constants. Different merit indices are considered to identify the most appropriate for compositional optimisation.     

Calibration of HDM-4 models for Indian conditions of flexible pavement having modified bitumen in wearing course

India is developing her national highway network through widening and rehabilitation of existing highways along with the construction of expressways in different phases, since 1999. Unprecedented growth of road traffic, high variations in pavement temperature and need of long lasting pavements have increased the use of modified bitumen specifically in wearing courses of many flexible pavement road sections of national highway network in entire country. Crumb rubber-modified bitumen (CRMB) and polymer-modified bitumen (PMB) of different grades are mostly used modified binders under different climatic and environmental conditions. During the design life, bituminous road sections show different rates of initiation and propagation of distresses under varying traffic and climatic conditions. In this study, an effort has been made to calibrate the internationally recognised Highway Development & Management (HDM-4) road deterioration models for the selected flexible pavement sections over time with traffic. The different road distresses are modelled using HDM-4 tool for the newly constructed flexible pavement sections of Indian national highway network having modified binder in bituminous concrete (BC) mixes which are located in different regions of the country. Pavement condition data of 23 in-service flexible pavement sections were collected for three consecutive years starting from 2011 to end of the year 2013. Data collected from the study were analysed for calibration and validation of HDM-4 distress models for similar climatic conditions, pavement compositions and traffic loading characteristics. The results of this study are useful for developing pavement maintenance management strategies for Indian national highway network with similar climatic conditions, pavement compositions and traffic characteristics.     

Influence of pavement condition on horizontal curve safety

Crash statistics suggest that horizontal curves are the most vulnerable sites for crash occurrence. These crashes are often severe and many involve at least some level of injury due to the nature of the collisions. Ensuring the desired pavement surface condition is one potentially effective strategy to reduce the occurrence of severe accidents on horizontal curves. This study sought to develop crash injury severity models by integrating crash and pavement surface condition databases. It focuses on developing a causal relationship between pavement condition indices and severity level of crashes occurring on two-lane horizontal curves in Texas. In addition, it examines the suitability of the existing Skid Index for safety maintenance of two-lane curves. Significant correlation is evident between pavement condition and crash injury severity on two-lane undivided horizontal curves in Texas. Probability of a crash becoming fatal is appreciably sensitive to certain pavement indices. Data suggested that road facilities providing a smoother and more comfortable ride are vulnerable to severe crashes on horizontal curves. In addition, the study found that longitudinal skid measurement barely correlates with injury severity of crashes occurring on curved portions. The study recommends exploring the option of incorporating lateral friction measurement into Pavement Management System (PMS) databases specifically at curved road segments.     

Quantifying the safety effects of pavement friction improvements – results from a large-scale study

It is fairly well understood that there is link between pavement friction and safety, or more specifically, the probability of wet-weather skidding crashes. However, robust knowledge on the comparative quantitative effects on safety for specific treatments that improve pavement friction, which can assist pavement engineers in economically justifying and prioritising treatments, is sparse. The large-scale study on which this paper is based aimed to firm up this knowledge for a variety of low-cost treatments and road classes, using state-of-the-art methodology and substantial, high-quality data-sets. This was a retrospective study for pavement safety performance, looking back at crash data before and after treatments was implemented. Both flexible and rigid pavement treatments were analysed and crash modification factors were estimated for several target crash types and road classes. The majority of the friction improvement treatments considered under this effort are typically used for pavement preservation or minor rehabilitation purposes. Although pavement engineers recognise that these treatments generally improve safety, they are not typically installed by highway agencies explicitly for safety improvement objectives. The combined results for most treatment types confirmed nevertheless the safety benefits for wet-road crashes, with a few exceptions. For dry road crashes, there was some evidence of the deleterious effects of speed adaptation to new surfaces in that crashes increased for a few treatments on some road types.     

Optimisation of machining parameters by criterion of maximum productivity

The optimisation of machining parameters for machine tools using the criterion of maximum productivity rate is not a new problem – many unresolved issues remain. The intensification of machining processes leads to changes in the productivity rate; analytically defining optimal machining parameters for the maximum productivity rate in real-world manufacturing processes is, therefore, an important problem that needs to be addressed. This paper aims to formulate a mathematical model for the optimisation of cutting processes on machine tools based on the criterion of maximum productivity rate. The mathematical model is based on technological data, machining and reliability parameters of the machine tool units. Several practical applications are discussed.     

履带车辆不平路面越野平均速度预测方法研究

为了研究路面不平度对履带车辆越野平均速度的影响规律,本文建立了履带车辆多体动力学模型,提出了振动响应指标并通过试验验证了模型的可信性;根据试验设计进行仿真计算,建立振动响应与车速、路面不平度间的近似模型;以振动响应指标的门限值为约束条件,采用目标寻优方法拟合了路面不平度与车速间的数学关系,提出了随机不平路面条件下的越野速度预测方法。应用该方法计算了车辆通过某试验场综合路面,结果表明：所建模型可以反映路面不平度对车速的影响,为车辆机动性预测提供了有效的量化手段。