Search

Geopolymer concrete is a sustainable and eco-friendly substitute for traditional OPC (Ordinary Portland Cement) based concrete, as it reduces greenhouse gas emissions. With various supplementary cementitious materials, the compressive strength of geopolymer concrete should be accurately predicted. Recent studies have applied deep learning techniques to predict the compressive strength of geopolymer concrete yet its hidden decision-making criteria diminish the end-users’ trust in predictions. To bridge this gap, the authors first developed three deep learning models: an artificial neural network (ANN), a deep neural network (DNN), and a 1D convolution neural network (CNN) to predict the compressive strength of slag ash-based geopolymer concrete. The performance indices for accuracy revealed that the DNN model outperforms the other two models. Subsequently, Shapley additive explanations (SHAP) were used to explain the best-performed deep learning model, DNN, and its compressive strength predictions. SHAP exhibited how the importance of each feature and its relationship contributes to the compressive strength prediction of the DNN model. Finally, the authors developed a novel DNN-based open-source software interface to predict the mix design proportions for a given target compressive strength (using inverse modeling technique) for slag ash-based geopolymer concrete. Additionally, the software calculates the Global Warming Potential (kg CO2 equivalent) for each mix design to select the mix designs with low greenhouse emissions.

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 prediction by integrating advanced machine learning models with XAI techniques. Traditional methods, such as the water quality index, often require extensive data collection and laboratory analysis, making them resource-intensive. The weighted arithmetic water quality index is employed alongside machine learning models, specifically RF, LightGBM, and XGBoost, to predict water quality. The models' performance was evaluated using metrics such as MAE, RMSE, R2, and R. The results demonstrated high predictive accuracy, with XGBoost showing the best performance (R2 = 0.992, R = 0.996, MAE = 0.825, and RMSE = 1.381). Additionally, SHAP were used to interpret the model's predictions, revealing that COD and BOD are the most influential factors in determining water quality, while electrical conductivity, chloride, and nitrate had minimal impact. High dissolved oxygen levels were associated with lower water quality index, indicative of excellent water quality, while pH consistently influenced predictions. The findings suggest that the proposed approach offers a reliable and interpretable method for water quality prediction, which can significantly benefit water specialists and decision-makers.

Climate change is a serious global issue causing more extreme weather patterns, resulting in more frequent and severe events like urban flooding. This review explores the connection between climate change and urban flooding, offering statistical, scientific, and advanced perspectives. Analyses of precipitation patterns show clear changes, establishing a strong link between climate change and the heightened intensity of global rainfall events. Hydrological modeling and case studies provide compelling scientific evidence attributing urban flooding to climate-induced changes. Urban infrastructure, including transportation networks and critical facilities, is increasingly vulnerable, worsening the impact on people's lives and businesses. Examining adaptation strategies, the review highlights the need for resilient urban planning and the integration of green infrastructure. Additionally, it delves into the role of advanced technologies, such as artificial intelligence, remote sensing, and predictive modeling, in improving flood prediction, monitoring, and management. The socio-economic implications of urban flooding are discussed, emphasizing unequal vulnerability and the importance of inclusive policies. In conclusion, the review stresses the urgency of addressing climate-induced urban flooding through a holistic analysis of statistical trends, scientific evidence, infrastructure vulnerabilities, and adaptive measures. The integration of advanced technologies and a comprehensive understanding of socio-economic implications are essential for developing effective, inclusive strategies. This review serves as a valuable resource, offering insights for policymakers, researchers, and practitioners striving for climate-resilient urban futures amid escalating climate change impacts.

Streamflow forecasting is crucial for effective water resource planning and early warning systems, especially in regions with complex hydrological behaviors and uncertainties. While machine learning (ML) has gained popularity for streamflow prediction, many studies have overlooked the predictability of future events considering anthropogenic, static physiographic, and dynamic climate variables. This study, for the first time, used a modified generative adversarial network (GAN) based model to predict streamflow. The adversarial training concept modifies and enhances the existing data to embed featureful information enough to capture extreme events rather than generating synthetic data instances. The model was trained using (sparse data) a combination of anthropogenic, static physiographic, and dynamic climate variables obtained from an ungauged basin to predict monthly streamflow. The GAN-based model was interpreted for the first time using local interpretable model-agnostic explanations (LIME), explaining the decision-making process of the GAN-based model. The GAN-based model achieved R2 from 0.933 to 0.942 in training and 0.93–0.94 in testing. Also, the extreme events in the testing period have been reasonably well captured. The LIME explanations generally adhere to the physical explanations provided by related work. This approach looks promising as it worked well with sparse data from an ungauged basin. The authors suggest this approach for future research work that focuses on machine learning-based streamflow predictions.

In this study, we used four different machine learning models - artificial neural network (ANN), support vector regression (SVR), k-nearest neighbor (KNN), and random forest (RF) - to predict the natural period of reinforced concrete frame structures with masonry infill walls. To interpret the models and their predictions, we employed Shapley additive explanations (SHAP), Local interpretable model-agnostic explanations (LIME), and partial dependency plots (PDP). All models showed good accuracy in predicting the fundamental period (T). The post-hoc explanations provided insights into (a) the importance of each feature, (b) their interaction, and (c) the underlying reasoning behind the predictions. For the first time, we have created a graphical interface that can predict the value of T along with its SHAP explanation. This interface can be useful in manually optimizing the design of reinforced concrete frame structures with masonry infill walls. However, the local explanations from SHAP and LIME exhibited significant discrepancies, and LIME underestimated the feature importance of dominant features compared to SHAP. These discrepancies observed in the explanations highlight the need for further research in the field of explainable artificial intelligence (XAI) in structural engineering.