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.

The aim of this study was to investigate the effect of traffic congestion in urbanized areas (parking lots and highways) on stormwater quality. Three separate locations in Egypt’s heavily urbanized and populous Giza Governorate were picked for the purpose of monitoring and evaluating the stormwater quality: Faisal (A), El Dokki (B), and Hadayek El-Ahram (C), with catchment areas of 10,476, 7566, and 9870 m², and with monthly average daily traffic (MADT) values of 47,950, 20,919, and 27,064 cars, respectively. The physio-chemical and heavy metal stormwater quality parameters of six water samples were investigated and compared with Egypt’s water criteria and the World Health Organization (WHO) guidelines. The water quality index (WQI) and the irrigation water quality indices were used to assess the uses of stormwater. The results showed that the WQI varied from 426 to 929, with an average of (661 $\pm$ 168), indicating that the stormwater was contaminated at each location under examination and needed pretreatment in order to be useful. As a result, the allowed stormwater quality standards were exceeded for heavy metals such as Al, Cr, Cd, Fe, and Cu. The indicators of the stormwater quality for irrigation—the total dissolved solids (TDS), sodium adsorption ratio (SAR), soluble sodium percentage (SSP), permeability index (PI), magnesium adsorption ratio (MAR), and Kelley’s ratio (KR)—show excellent stormwater for irrigation, while the total hardness (TH) and residual sodium bicarbonate (RSBC) indicate poor irrigation water quality. It is advised to sweep the streets to remove particle-bound pollution before it reaches storm drain water, and to put in place an adequate stormwater sewerage system to catch rainwater.

This research investigates the effect of heavy rain on highway traffic volume and average speed, and proposes a recharging well harvesting system as an alternative freshwater source in the context of climate change. The Cairo Autostorad highway was taken as a case study. The highway climate data were collected, and traffic was measured using Metrocount equipment during the period from 2008 to 2020. The results show that the studied road is about 12 km long, and about 40 water ponds exist along the route. Each pond has an estimated water volume of 300 m³, and a 30 cm recharging well, with a maximum recharging capacity of 25 m³/h with satisfactory performance, is recommended to be constructed for rainwater harvesting. The recharging wells will clear the ponding volume within 2.5 to 3.5 h after the rainfall has stopped. The design incorporates a 1.2 safety factor against blockage inside the well. In addition, a model was established between the average rainfall depth and the average measured highway speed for the period (2008–2020) during rainy months, indicating an exponential function with a determination factor R² = 0.7076. The present rainfall (2020) and the representative concentration path (RCP) for 4.5 and 8.5 emissions scenarios were used to simulate the rainfall for future years: the 2040s, 2060s, 2080s, and 2100s. The results show that in the winter season for the current scenario (2020), the average rainfall depth was 45 mm, and the highway speed was 78 km/h. For the RCP 4.5 emission scenarios for the 2040s, 2060s, 2080s, and 2100s, the rainfall depths were 67.8, 126.4, 131.2, and 143.9 mm, and the corresponding reductions in the highway speeds were 23, 34, 35.3, and 36.9%, respectively, compared to the baseline scenario (2020). On the other hand, the RCP 8.5 emission scenarios show a reduction in the highway speed of 23, 34.5, 36.9, and 36.9% for the years 2040, 2060, 2080, and 2100, respectively, due to rainfall depths of 68.7, 128.4, 143.9, and 143.9 mm, respectively. This study helps policymakers to make wise decisions regarding sustainable water resource management and highway traffic problems related to rainwater depths in the context of climate change.