COMPUTATIONAL MODELING OF CLIMATE ATTRIBUTES AND CONDITION DETERIORATION OF CONCRETE HIGHWAY PAVEMENTS
An efficient and safe road network secures the nation’s economy and prosperity by providing public mobility and freight transport. Maintenance and rehabilitation of the road network cost billions of dollars annually. Road and highway infrastructures performance in any country is impacted by load rep...
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eGrove
2021
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| Online Access: | https://egrove.olemiss.edu/etd/2138 https://egrove.olemiss.edu/cgi/viewcontent.cgi?article=3137&context=etd |
| Summary: | An efficient and safe road network secures the nation’s economy and prosperity by providing public mobility and freight transport. Maintenance and rehabilitation of the road network cost billions of dollars annually. Road and highway infrastructures performance in any country is impacted by load repetitions and it is further compromised by climate attributes and extreme weather events. Damages to roads and bridges are among the infrastructure failures that have occurred during these extreme events. If maintenance and rehabilitation are not done promptly, the damages to the road caused by heavy traffic and extreme climate may lead to life-threatening conditions for road users. A disruption in any one system affects the performance of others. For example, damages in road and bridge infrastructure will delay the recovery operation after a disaster. In 2018, a total of 331 natural disaster occurrences were reported worldwide, which resulted in 14,385 deaths. From 1900 to 2000, in 119 years, 14,854 natural disaster occurrences were reported which caused 32,651,605 deaths. Natural disaster occurrences like hurricanes, floods, droughts, landslides, etc. may be influenced by specific climate mechanisms like El Niño and Southern Oscillation (ENSO). Several climate attributes models were developed in this research employing Auto-Regressive Integrated Moving Average (ARIMA) methodology. The sea surface temperature data were analyzed and a prediction model was developed to predict future ENSO years. The model successfully predicted the 2018-2019 El Niño year. The model prediction showed that the next El Niño years will be 2021-22 and 2025-26. The model prediction also shows that the next La Niña year will be 2028-29. Global mean sea level (GMSL) data were analyzed and a prediction model was developed. The predicted annual rate of change in GMSL is 0.6 mm/year from 2013 to 2050. But a higher annual rate of change (1.4 mm/year) is predicted from 2031 to 2050. Northern hemisphere (Arctic) sea ice extent and southern hemisphere ... |
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