Concrete is the most widely used construction material throughout the world. Extensive experiments are conducted every year to study the physical, mechanical, and chemical properties of concrete involving a hefty amount of money and time. This work focuses on the utilization of Machine Learning (ML) algorithms to predict various concrete properties for avoiding unnecessary experimentation. In this work, six mechanical properties of concrete namely modulus of rupture, compressive strength, modulus of elasticity, Poisson’s ratio, splitting tensile strength, and coefficient of thermal expansion are estimated by applying five different ML algorithms viz. Linear Regression, Support Vector Machine, Decision Tree, Random Forest, and Gradient Boosting models on the Wisconsin concrete mixes database. Further, these ML models were evaluated to identify the most suitable model that can reliably predict the mechanical properties of concrete. The approach followed in this research was verified using the 10-fold Cross-Validation technique to eliminate training and testing split bias. The Grid Search Cross Validation method was used to find the best hyperparameters for each algorithm. Root mean squared error (RMSE) and coefficient of determination (R²) results showed that the Support Vector Machine outperformed the other models applied on the datasets. Support Vector Machine predicted the modulus of rupture of concrete after a curing time of 28 days with an R² score of 0.43, which is better than the R² scores of Random Forest and Gradient Boosting advanced algorithms by 34% and 26%, respectively.

A region's transportation sector is vital to its economic and social health. Transportation systems are also influenced by climate change directly and indirectly and on a variety of spatial and temporal scales. Under a changing climate, many regions around the globe and especially in urban areas, have experienced increases in flood intensity and frequency in recent decades. Flooding can strain transportation networks in both the short- and long-term through transportation delays, infrastructure damage, and recovery, and potentially affect economies. The present article is a review of how flooding impacts transportation networks in both short- and long-term timescales and their subsequent impact on resiliency of the network. Firstly, flood effects are classified based on the connections between the type of flooding and the type of impact (either direct or indirect) on the transportation system. An analysis of the assessment methods and the transport models used to formulate flood effects on the transportation system is provided, as well as the drawbacks from the context of timescales, and recommendations for future research. The analysis indicates that the majority of the articles assess the direct and tangible impacts with focus on the resilience of the transportation network in short- and medium-term temporal scales and at smaller spatial scales. There is less emphasis on indirect, intangible flood impacts, and long-term temporal scales.

Bioretention cells are an emerging low impact development technology that address urban stormwater runoff concerns. Field and column experiments were conducted to assess the efficacy of bioretention cells in cold conditions. Field experiments in a prairie environment demonstrated a significant decrease (91.5%) in effluent volumes compared to influent volumes. The majority (∼60%) of the runoff percolated to the surrounding soils or evapotranspirated. Cold condition performance significantly impacted high volume events and was characterized by significantly higher effluent volumes, significantly lower runoff storage, higher effluent peak flow rates, and longer peak delays. A partially frozen surface layer caused the changes in performance. Long-term simulation experiments on the columns indicated a significant decrease in saturated hydraulic conductivity over the first 4 equivalent years of operation, before levelling to a constant value.