Tr en In many countries, incredible investments have been made in constructing roads that require conducting periodic evaluation and timely maintenance and rehabilitation (M&R) plan to keep the network operating under acceptable level of service. The timely M&R plan necessitates accurately predicting pavement performance, which is an essential element of road infrastructure asset management systems or Pavement Management Systems (PMS). Consequently, there is always a need to develop and to update performance prediction models embedded in PMS applications. This study focuses in developing distress prediction models for flexible pavements located in non-freeze climatic zone, which represent most of developing countries such as Egypt, using data extracted from the Long-Term Pavement Performance (LTPP) program. Ten distress performance prediction models were developed in this study for both wet- and dry-non freeze climatic zones, which are alligator (Fatigue) cracking, longitudinal cracking, transverse cracking, ravelling, bleeding, and rut depth models. These models can play an important role assisting decision makers in predicting pavement performance, identifying M&R needs, rational budget planning and resource allocation. In many countries, incredible investments have been made in constructing roads that require conducting periodic evaluation and timely maintenance and rehabilitation (M&R) plan to keep the network operating under acceptable level of service. The timely M&R plan necessitates accurately predicting pavement performance, which is an essential element of road infrastructure asset management systems or Pavement Management Systems (PMS). Consequently, there is always a need to develop and to update performance prediction models embedded in PMS applications. This study focuses in developing distress prediction models for flexible pavements located in non-freeze climatic zone, which represent most of developing countries such as Egypt, using data extracted from the Long-Term Pavement Performance (LTPP) program. Ten distress performance prediction models were developed in this study for both wet- and dry-non freeze climatic zones, which are alligator (Fatigue) cracking, longitudinal cracking, transverse cracking, ravelling, bleeding, and rut depth models. These models can play an important role assisting decision makers in predicting pavement performance, identifying M&R needs, rational budget planning and resource allocation.

There is a growing global interest in preserving transportation infrastructure. This necessitates routine evaluation and timely maintenance of road networks. The effectiveness of pavement management systems (PMSs) heavily relies on accurate pavement deterioration models. However, there are limited comparative studies on modeling approaches for rural roads in arid climatic conditions using the same datasets for training and testing. This study compares three approaches for developing a pavement condition index (PCI) model as a function of pavement age: classical regression, machine learning, and deep learning. The PCI is a pavement management index widely adopted by many road agencies. A dataset on pavement age and distress was collected over a twenty-year period to develop reliable predictive models. The results demonstrate that the regression model, machine learning model, and the deep learning model achieved a coefficient of determination ($R^2$) of 0.973, 0.975, and 0.978, respectively. While these values are technically equal, the average bias for the deep learning model (1.14) was significantly lower than that of the other two models, signaling its superiority. Additionally, the trend predicted by the deep learning model showed more distinct phases of PCI deterioration with age than the machine learning model. The latter exhibited a wider range of PCI deterioration rates over time compared to the regression model. The deep learning model outperforms a recently developed regression model for a similar region. These findings highlight the potential of using deep learning to estimate pavement surface conditions accurately and its efficacy in capturing the PCI-age relationship.

Municipalities and transportation departments worldwide are striving to keep road pavement conditions acceptable, thus enhancing pavement sustainability. Although the pavement condition index (PCI) is widely used to assess distress conditions, traditional visual surveys used for PCI estimation can be laborious, expensive, and time-consuming. The international roughness index (IRI) can be measured more economically and conveniently than PCI; however, it does not directly indicate the surface condition of the pavement. In this study, a PCI–IRI correlation is proposed for urban roads located in the New Beni-Suef region, Egypt. For this purpose, a total of 44 km of urban roads was divided into homogenous sections. A visual distress survey was conducted to measure PCI considering typical distress patterns. The IRI values for the same sections were measured using an ultrasonic distance sensor mounted on an automobile. An exponential model was proposed to capture the relationship between IRI and PCI. With a coefficient of determination of 0.82, the exponential model seems to outperform reported IRI-PCI correlations. Model validation, along with a comparison to the existing models, supports its applicability to a wide range of roads. The proposed model provides a cost-effective means for accurately predicting PCI based on IRI, which is particularly useful for pavement maintenance management programs on limited budgets.

Pavement evaluation represents the critical foundation of effective pavement management systems. Developing a sophisticated deterioration index is crucial for authorities to strategically allocate maintenance and rehabilitation budgets. Advanced countries have implemented various single and combined condition indices based on extensive pavement databases. Unfortunately, developing nations often lack comprehensive condition data, resulting in underdeveloped evaluation metrics and consequently, suboptimal budget allocation for preservation activities. This study develops an integrated overall deterioration index (ODI) of pavement using weighted combinations of four key assessment categories. The first category examines pavement condition index (PCI), including various cracking patterns, deformations, surface defects, patching/potholes, and edge deterioration, surface texture, load bearing capacity, layer thickness, material properties, drainage conditions, climate change, pavement age, and traffic loading. The second focuses on international roughness index (IRI) metrics such as roughness, and comfort levels. The third addresses falling weight deflectometer (FWD) including deflection measurements. The fourth examines friction number (FN) like skid resistance. The weighting methodology employs fuzzy analytical hierarchy process through expert panel evaluations. Analysis reveals PCI as the predominant factor (weight: 0.439), with IRI and FWD ranking second and third (weights: 0.15 and 0.318 respectively). These weightings demonstrate engineering validity across the case study. The resulting composite index offers a holistic assessment of pavement deterioration applicable to maintenance planning frameworks. A model performance was made using the long-term pavement performance (LTPP) and it proved the validity of the model.

Traditional Pavement Condition Index (PCI) assessments are highly resource-intensive, demanding substantial time and labor while generating significant carbon emissions through extensive field operations. To address these sustainability challenges, this research presents an innovative methodology utilizing Gene Expression Programming (GEP) to determine PCI values based on International Roughness Index (IRI) measurements from Iraqi road networks, offering an environmentally conscious and resource-efficient approach to pavement management. The study incorporated 401 samples of IRI and PCI data through comprehensive visual inspection procedures. The developed GEP model exhibited exceptional predictive performance, with coefficient of determination (R²) values achieving 0.821 for training, 0.858 for validation, and 0.8233 overall, successfully accounting for approximately 82–85% of PCI variance. Prediction accuracy remained robust with Mean Absolute Error (MAE) values of 12–13 units and Root Mean Square Error (RMSE) values of 11.209 and 11.00 for training and validation sets, respectively. The lower validation RMSE suggests effective generalization without overfitting. Strong correlations between predicted and measured values exceeded 0.90, with acceptable relative absolute error values ranging from 0.403 to 0.387, confirming model effectiveness. Comparative analysis reveals GEP outperforms alternative regression methods in generalization capacity, particularly in real-world applications. This sustainable methodology represents a cost-effective alternative to conventional PCI evaluation, significantly reducing environmental impact through decreased field operations, lower fuel consumption, and minimized traffic disruption. By streamlining pavement management while maintaining assessment reliability and accuracy, this approach supports environmentally responsible transportation systems and aligns contemporary sustainability goals in infrastructure management.