Prediction & optimization of alkali-activated concrete based on the random forest machine learning algorithm

Alkali-activated concrete (AAC) is regarded as a promising alternative construction material to reduce the CO₂ emission induced by Portland cement (PC) concrete. Due to the diversity in raw materials and complexity of reaction mechanisms, a commonly applied design code is still absent to date. This study attempts to directly...

Acoustic Emission-Based Detection in Restricted-Access Areas Using Multiple PZT Disc Sensors

The performance of the Acoustic Emission (AE) technique is significantly dependent on the sensors attached to the structural surface. Although conventional commercially AE sensors, like R15a and WSa sensors, have been extensively employed in monitoring many different structures, they are unavailable in restricted-assess areas. In contrast,...

Towards understanding and prediction of atmospheric corrosion of an Fe/Cu corrosion sensor via machine learning

The atmospheric corrosion of carbon steel was monitored by a Fe/Cu type galvanic corrosion sensor for 34 days. Using a random forest (RF)-based machine learning approach, the impacts of relative humidity, temperature and rainfall were identified to be higher than those of airborne particles, sulfur dioxide, nitrogen dioxide, carbon monoxide...