Water quality assessment and prediction play crucial roles in ensuring the sustainability and
safety of freshwater resources. This study aims to enhance water quality assessment and …

Graphene oxide (GO) has shown promise in improving concrete strength. Despite its
frequent use in cement composites, its effect on concrete properties is less explored. The …

Accurate runoff forecasting is crucial for effective water resource management, yet existing
models often face challenges due to the complexity of hydrological systems. This study …

Conventional machine learning techniques in diagnosing cardiovascular disease have a
limitation owing to the lack of interpretability of models. This study utilised an explainable …

Geopolymer concrete (GC) emerges as a sustainable alternative yet faces challenges in
achieving optimal resource utilization for strength development. Balancing these aspects is …

Freshwater salinisation and alkalinisation strongly depend on human and natural factors.
We used an explainable machine learning approach to investigate the impact of natural and …

The fatigue performance of corroded steel wires significantly impacts bridge safety. Existing
methods have limited accuracy and generalizability due to varied experimental conditions …

Geopolymer concrete (GPC) offers an environmentally friendly alternative to ordinary
Portland cement concrete (OPCC). Particularly, fly ash-ground granulated blast furnace slag …

The evaluation of urban noise suitability is crucial for urban environmental management.
Efficient and cost-effective methods for obtaining noise distribution data are of great interest …

Machine learning has been used in predicting the natural period of vibration of reinforced
concrete structures. However, their lack of interpretability diminishes the end user's trust in …