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Analysis of airport runway pavement reliability considering temperature variation: The case of Sao Paulo-Congonhas international airport

This study demonstrates that incorporating seasonal temperature variations into the reliability analysis of Sao Paulo-Congonhas International Airport's pavement reveals that traditional FAA design methods significantly overestimate fatigue damage, suggesting that local calibration could allow for substantially increased traffic capacity and reduced construction costs.

Original authors: Felipe H. Cava, Dimas B. Ribeiro, Claudia A. Pereira, Mauro Caetano, Evandro Jose da Silva

Published 2026-02-04

📖 4 min read☕ Coffee break read

CC BY 4.0

Original authors: Felipe H. Cava, Dimas B. Ribeiro, Claudia A. Pereira, Mauro Caetano, Evandro Jose da Silva

Original paper licensed under CC BY 4.0 (http://creativecommons.org/licenses/by/4.0/). ✨ This is an AI-generated explanation of the paper below. It is not written or endorsed by the authors. For technical accuracy, refer to the original paper. Read full disclaimer

Imagine a runway as a giant, heavy-duty mattress designed to hold up thousands of airplanes taking off and landing. For decades, engineers have used a standard "recipe" (provided by the FAA) to design these mattresses. This recipe assumes the weather is always the same: a mild, constant 32°C (90°F) year-round. It's like designing a winter coat assuming it will only ever be worn on a pleasant spring day.

However, this study looked at São Paulo-Congonhas International Airport in Brazil and realized that the weather there is actually quite dramatic. It gets hot in the summer and cooler in the winter. The researchers asked a simple question: What happens to our "airplane mattress" when the temperature actually changes?

Here is the breakdown of their findings using everyday analogies:

1. The "Rubber Band" Effect

Asphalt (the black stuff on the runway) is like a rubber band.

In the heat (Summer): The asphalt gets soft and stretchy, like a rubber band left in the sun. It becomes less stiff.
In the cold (Winter): The asphalt gets hard and stiff, like a rubber band in the freezer.

The standard FAA recipe assumes the asphalt is always at a "medium" stiffness. But in reality, the top of the runway is hot and soft, while the bottom layers are cooler and much stiffer.

2. The "Onion" Discovery

The researchers realized that the runway isn't just one uniform block of asphalt; it's more like an onion with layers.

The top layer is hot and soft.
As you go deeper, the temperature drops, and the asphalt gets harder and stiffer.

The old design method treated the whole runway as if it were one single, medium-stiffness layer. The new study showed that because the bottom of the asphalt (where the cracks usually start) is actually cooler and stiffer than the recipe assumed, it can handle the stress of airplanes much better than expected.

3. The "Over-Engineered" Result

The team ran a massive computer simulation (like running a video game 5,000 times with different weather scenarios) to see how much damage the planes actually caused.

The Old Way: The FAA method predicted the runway would get damaged quickly, as if the asphalt were always soft and weak.
The New Way: When they accounted for the real Brazilian temperatures, they found the runway was actually much stronger than the recipe said.

The Big Numbers:

The old method overestimated the damage by 79%.
The runway designed by the old method could actually handle 2.5 times more traffic before failing (at a 95% safety guarantee).
At a 50% safety guarantee, it could handle 5 times more traffic.

4. The "Expensive Mistake"

Because the old recipe thought the runway was weaker than it really is, engineers have been building runways thicker and stronger than necessary.

Analogy: It's like buying a steel-reinforced concrete bunker to protect a bicycle, when a simple wooden shed would have done the job.
The Cost: This "over-design" means airports in Brazil are spending extra money on materials and construction that they don't actually need.

5. What They Didn't Do (The Limitations)

The authors are careful to say their study has some boundaries:

They only looked at temperature. They didn't include rain, water, or changes in how thick the pavement was laid down.
They used a mathematical formula to guess the temperature deep in the ground, rather than measuring it with a thermometer in every single spot.
They didn't test the asphalt in a lab with different temperatures; they used standard lab values.

The Bottom Line

The study concludes that the current "recipe" for building airport runways in Brazil is too conservative. It assumes the weather is always mild, but because the asphalt gets stiffer in the cooler parts of the runway, the runways are actually much tougher than we thought.

The authors suggest that we need to recalibrate the recipe for Brazil's specific climate. If we do this, we can design runways that are just strong enough to be safe, without wasting money on unnecessary extra thickness. They plan to do more testing in the future to fine-tune this new recipe, including building a real-life "digital twin" of a runway to monitor it over time.

Technical Summary: Analysis of Airport Runway Pavement Reliability Considering Temperature Variation at São Paulo-Congonhas International Airport

Problem Statement
Airport pavement design methodologies, particularly those relying on FAA standards (e.g., FAARFIELD), typically assume constant climatic conditions, specifically a fixed asphalt field temperature of 32°C throughout the design period. This study posits that such assumptions fail to account for regional temperature variations, which significantly influence asphalt material behavior, stress/strain distribution, and overall pavement reliability. In countries with large continental dimensions like Brazil, temperature differences between regions and across seasons alter the viscoelastic properties of asphalt mixtures. Furthermore, the fatigue equations used in current design methods have not been calibrated for specific field conditions, potentially leading to inaccurate damage predictions and inflated construction costs, especially in regions where fatigue is the primary failure criterion.

Methodology
The study employs a four-part methodology focused on São Paulo-Congonhas International Airport (CGH), one of Brazil's busiest airports:

Data Collection: Traffic data (2017, 2018, 2022) and meteorological data (2014–2024) from the nearest station (Mirante de Santana) were analyzed. The B738 aircraft was identified as the primary traffic contributor (approx. 26,000 operations/year). Air temperature data were verified to follow a normal distribution per season using the Kolmogorov-Smirnov test.
Pavement Design: A baseline pavement structure was designed using FAARFIELD software based on projected traffic loads for a 20-year period. The design utilized standard FAA material properties, assuming a constant HMA modulus corresponding to 32°C.
Temperature Modeling and Sensitivity Analysis: Pavement temperatures at various depths were calculated using Huber's (1994) deterministic equations, adjusted for CGH's latitude, albedo, and wind speed. The asphalt layer was discretized into ten parts to capture thermal gradients. A sensitivity analysis was conducted using mePADS software to evaluate how varying HMA moduli (derived from temperature-dependent dynamic modulus models) affected tensile strains at the bottom of the asphalt layer and compressive strains at the subgrade.
Reliability Analysis: A Monte Carlo Simulation (MCS) with 5,000 samples was performed to assess pavement reliability. The simulation incorporated seasonal temperature variations to generate random HMA modulus values. Reliability ( $R$ ) was calculated as the ratio of samples satisfying the design criteria (Cumulative Damage Factor, CDF $\le$ 1) to the total number of samples.

Key Results

Thermal Gradient Impact: The study confirmed significant temperature gradients within the asphalt layer. The HMA modulus varied by over 76% between winter and summer seasons and increased significantly with depth due to lower temperatures at the bottom of the layer.
Strain Variability: Sensitivity analysis revealed that tensile strains at the bottom of the asphalt layer increase by an average of 122% when temperatures rise from 22°C to 41°C. Conversely, the higher modulus at the bottom of the layer (due to cooler temperatures) results in lower tensile strains than predicted by a uniform 32°C assumption.
Damage Overestimation: The total Cumulative Damage Factor (CDF) computed via MCS was 79% lower than the value obtained using the standard FAARFIELD method. This indicates that the FAA method significantly overestimates fatigue damage for the specific climatic conditions of CGH.
Reliability and Traffic Capacity: When accounting for actual temperature variations, the pavement designed by FAARFIELD could withstand traffic loads 2.5 times greater at 95% reliability and 5.0 times greater at 50% reliability compared to the design assumptions.
Design Period Implications: Based on the MCS damage levels, the design period could theoretically extend to approximately 50 years (95% reliability) or 102 years (50% reliability), far exceeding typical airport design periods.

Significance and Conclusions
The paper concludes that the current FAA pavement design method, which relies on a fixed temperature and uncalibrated fatigue equations, overestimates damage for airports in Brazilian climatic conditions where fatigue is the dominant failure mode. This overestimation leads to unnecessarily thick pavement structures and increased construction costs. The findings suggest that the fatigue performance prediction models require calibration for local climatic conditions to improve accuracy.

The authors acknowledge specific limitations: the pavement temperature equation is deterministic (assuming fixed albedo and wind speed), precipitation and thickness variability were not included in the reliability analysis, and fatigue tests under varying asphalt temperatures were not conducted. Consequently, the study calls for future research to calibrate performance equations using laboratory and field data, including the development of a digital twin combining finite element analysis and machine learning to support these calibrations. The results highlight the necessity of adapting international design standards to local environmental realities to ensure resilient and cost-effective infrastructure.