Soft Computing and Machine Learning in Dam Engineering

Abstract

Traditional dam safety methods, based on visual inspections and manual monitoring, have long been the standard for ensuring the stability and safety of dams. However, as the scale and complexity of dam projects have increased, these methods have become increasingly insufficient. Major limitations of traditional dam safety methods are the existence of deficient observation plans and the potential for human error. Inspectors may miss crucial signs of deterioration or failure, and manual monitoring can be prone to inaccuracies. In addition, as the number of (aged and new) dams continues to increase, it becomes increasingly difficult and resource-intensive to manually inspect and monitor each one. Another limitation of traditional dam safety methods is that they are typically reactive rather than proactive. They focus on identifying and addressing problems after they have already occurred, rather than predicting and preventing them. In contrast, modern techniques such as remote sensing, drones, and sensor networks can provide more accurate, real-time data on dam conditions. They can also be used to continuously monitor dams, providing an early warning of potential problems. Artificial Intelligence (AI) can be applied to the data collected from these modern techniques for identifying patterns and anomalies that may indicate a potential problem. AI algorithms can be used in the decision-making process for dam safety by providing accurate and updated risk analysis.