Residential building energy consumption is a significant contributor to greenhouse gas emissions. Accurate prediction of total energy use in residential buildings holds vital importance in the context of energy management. In this paper, we propose a data-driven approach using machine learning (ML) models to predict the total energy consumption of a low-energy house based on indoor and outdoor environmental conditions using data from a house located in Belgium. Four ML(ML) models, including Extreme Gradient Boosting (XGBoost), Random Forest (RF), Decision Tree (DT), and Support Vector Machine (SVM), were trained and tested to evaluate their performance in predicting energy consumption. The results of our study demonstrate that the XGBoost model outperforms all other models used , with a coefficient of determination R2 of 61%, a Root Mean Square Error (RMSE) of 65.28, a Mean Absolute Error (MAE) of 29.81, and a Mean Absolute Percentage Error (MAPE) of 28.55 on the testing set. The findings from this study demonstrate the accurate forecasting of energy consumption by accounting for the non-linear dependencies between environmental conditions and total energy consumption, which can aid in making informed decisions towards the reduction of power usage, enhancement of energy efficiency, and achieving cost savings.

The 2015 seismic events in Nepal highlighted critical challenges in constructing earthquake-resilient, self-built stone masonry houses in rural mountain areas. The Department of Urban Development and Building Construction (DUDBC), however, provided designs that failed to preserve the local architectural landscape and craftsmanship while adding unfamiliar knowledge and technology. To address the shortcomings of DUDBC model houses related to architecture, structure, and socio-economic concerns in designing and implementing post-earthquake houses, a need-based building development framework is required for standardized practice. This study proposes a novel framework to assess, design, and implement a self-built house after a disaster, consisting of five stages: (1) post-disaster problem assessment, (2) need identification, (3) material selection, (4) design development, and (5) validation and implementation. Based on this framework, we proposed a novel gabion building construction technology for two-story stone masonry structures that effectively mitigate post-disaster challenges such as logistic, resilience, and socio-economic aspects while improving disaster resiliency specifically in the high-elevation rural areas of Nepal. The proposed two-story gabion building preserves local architectural values, enhances structural integrity, and provides cost-effectiveness when compared with its DUDBC peer models while providing much-needed relief to the vulnerable community. The proposed resilient house, G-2.1, utilizes locally sourced materials and craftsmanship, and innovative gabion technology that ensures affordability and facilitates knowledge transfer. The contribution of this study includes a multi-objective framework for a two-story gabions house that is most suitable for self-built resilient homes that preserves the local architecture and socio-economic conditions while providing structural integrity and safety.

Accurate and timely prediction of peak flow in streams is essential for transportation safety as these estimates can help transportation authorities implement precautionary measures (e.g., road closures, diversion, emergency routes, transportation planning, flood impact assessment, etc.) well ahead of time to mitigate the impacts of flooding on transportation. Often in practice, flow quantiles are estimated from catchment and climate attributes using simple methods such as linear regression, which overlooks the more complex nature of relationships between variables, potentially leading to errors and uncertainties in the estimates that can trickle down to engineering design. Here, we will discuss findings from our ongoing work on accurate estimation of peak flow using machine learning algorithms. The methodology involves a two-step process. First, k-means clustering is implemented to identify regions that have similarities in the mean annual runoff. Second, Random Forest is implemented to map a wide range of climate and catchment features to flow quantiles in each cluster. To assess the effectiveness of this approach in increasing transportation resilience, we will show how the peak flow estimates from this new approach compare with the estimates from the existing approach followed by the Nebraska Department of Transportation and explore the potential of these new estimates to be used for operational purposes for flood-related decision-making in the context to transportation infrastructure.

The seismic events of 2015 in Nepal exposed the challenges associated with constructing earthquake-resistant 2-story self-built stone masonry structures in high-elevation rural regions, especially post-disaster. The Department of Urban Development and Building Construction (DUDBC) provided designs that failed to preserve the local architectural landscape and craftsmanship while adding unfamiliar knowledge and technology. To address the challenges related to architecture, structure, and socio-economic concerns in designing and implementing post-earthquake houses, a need-based building development framework is required for standardized practice. This study presents a novel framework to assess, design, and implement a self-built house after a disaster, consisting of five stages: 1) post-disaster problem assessment, 2) need identification, 3) material selection, 4) design development, and 5) validation and implementation. Based on this framework, we proposed a novel gabions building construction technology for two-story stone masonry structures that effectively mitigate post-disaster challenges such as logistic, resilience, and socioeconomic aspects while improving disaster resiliency specifically in the high-elevation rural areas of Nepal. The proposed two-story gabion building preserves architectural values, enhances structural integrity, and provides cost-effectiveness when compared with its DUDBC peer models while providing much-needed relief to the vulnerable community. The proposed resilient house, G- 2.1, utilizes locally sourced materials and craftsmanship, and innovative gabion technology that ensures affordability and facilitates knowledge transfer. The contribution of this study includes a development framework and a two-story gabions house that is most suitable for self-built post-disaster reconstruction and resilient to local architecture and socio-economic conditions while providing structural integrity and safety.