The efficiency of pavement lifecycle planning highly depends on the accuracy of condition predictions. Therefore, transportation agencies strive to maximize the impact of the limited budget through investment decisions empowered by accurate deterioration modeling. For this purpose, family deterioration models were developed based on clustering techniques to overcome the limitations of data availability. However, most of the existing pavement clustering approaches rely on the subjective opinion of experts not only on the selection of factors contributing to deterioration but also in classifying the selected factors. Also, the impact of clustering algorithms and their configurations on the accuracy of deterioration models were marginally investigated in previous studies. To this end, we developed a clustering method and incorporated a wide mixture of categorical and continuous contributors. Then, we created a process to find the optimal configuration of clusters. Finally, we implemented the devised methodology on a large-scale case study. The comparison of our results with past studies revealed an improvement in the accuracy of the condition predictions. Consequently, this study provides a tool for accurately predicting the maintenance needs of pavements and improves the efficiency of lifecycle planning.

Transportation agencies constantly strive to tackle the challenge of limited budgets and continuously deteriorating highway infrastructure. They look for optimal solutions to make intelligent maintenance and repair investments. Condition prediction of highway assets and, in turn, prediction of their maintenance needs are key elements of effective maintenance optimization and prioritization. This paper proposes a novel risk-based framework that expands the potential of available data by considering the probabilistic susceptibility of assets in the prediction process. It combines a risk score generator with machine learning to forecast the hotspots of multiple defects while considering the interrelations between defects. With this, we developed a scalable algorithm, Multi-asset Defect Hotspot Predictor (MDHP), and then demonstrated its performance in a real-world case. In the case study, MDHP predicted the hotspots of three defects on paved ditches, considering the interrelation between paved ditches and five nearby assets. The results demonstrate an acceptable accuracy in predicting hotspots while highlighting the interrelation between adjacent assets and their contribution to future defects. Overall, this study offers a scalable approach with contribution in data-driven multi-asset maintenance planning with potential benefits to a broader range of linear infrastructures such as sewers, water networks, and railroads.

Daily flow variation through waste water collection systems (WWCS) should be considered in the proper design of the network. Traditionally, design procedures are carried out by estimation of maximum hourly flow, whereby sewers diameters are calculated. Empirical formulas such as Babbitt, Gifft, and Harmon are used to calculate maximum hourly flow. Based on these formulas, Kmax, which is proportional to population, is used to calculate maximum hourly sewage flow rate, by multiplying Kmax by mean daily sewage flow value. Then, diameters of sewers are estimated using this flow estimation. Recently, flow measurement is carried out in a greater extent compared to the past. This has been possible by vast development of sewer network construction and advances in flow measurement devices technology. Therefore, more information is available to detect flow variation through sewers in WWCS. Nevertheless, use of these data helps designers to obtain real flow estimation, traditional procedures are immensely in use by engineers due to lack of such data. Recent studies have unveiled some disparities between real flow data and common empirical formulas. In this paper, empirical procedures have been compared to each other by reviewing relevant key literature. Then, results of using empirical formulas were compared with the real measured flow data from residential, commercial, but industrial zones in greater Tehran and Isfahan WWCS. Consequently, significant differences between real data and empirical formulas, especially in zones with lower populations, were revealed, which demonstrated that Kmax derived from empirical formulas is not applicable for both Tehran and Isfahan cities. A design, which is based on real-measured data, was recommended and carried out for these two WWCSs.