A holistic sustainability overview of hemp as building and highway construction materials

The construction sector, responsible for over one-third of global carbon emissions, is increasingly focusing on hemp-based construction materials to alleviate the environmental impact in the built environment; however, the lack of information and streamlined processes hinder widespread adoption. By conducting a comprehensive review of state-of-the-art research, this study explores the vast potential of hemp-based materials across the built environment, encompassing building and transportation applications. In this study, the material properties and application of hemp lime concrete for buildings, along with hemp fibre in asphalt for highways, are discussed, and crucial research gaps and technical challenges are identified. Employing a holistic sustainability approach, the material evaluation considers economic, social, and environmental factors. Notable hemp construction projects are presented as case studies, emphasising their environmental carbon credentials. Furthermore, technoeconomic challenges are scrutinised, and effective solutions are proposed. Beyond its role as a wall material, hempcrete's significant application as building insulation material is highlighted due to its exceptional hygrothermal properties. The material also shows promise in enhancing asphalt mix for pavement construction. Evidence from life cycle analysis supports the claim that hempcrete can be considered a carbon-negative material. Moreover, the findings indicate that the hempcrete industry has the potential to yield various macroeconomic and socio-economic advantages, including job creation, enhancing energy access, alleviating cost of energy, and improved societal health and well-being

Meta databases of steel frame buildings for surrogate modelling and machine learning-based feature importance analysis

Traditionally, nonlinear time history analysis (NLTHA) is used to assess the performance of structures under future hazards which is necessary to develop effective disaster risk management strategies. However, this method is computationally intensive and not suitable for analyzing a large number of structures on a city-wide scale. Surrogate models offer an efficient and reliable alternative and facilitate evaluating the performance of multiple structures under different hazard scenarios. However, creating a comprehensive database for surrogate modelling at the city level presents challenges. To overcome this, the present study proposes meta databases and a general framework for surrogate modelling of steel structures. The dataset includes 30,000 steel moment-resisting frame buildings, representing low-rise, mid-rise and high-rise buildings, with criteria for connections, beams, and columns. Pushover analysis is performed and structural parameters are extracted, and finally, incorporating two different machine learning algorithms, random forest and Shapley additive explanations, sensitivity and explainability analyses of the structural parameters are performed to identify the most significant factors in designing steel moment resisting frames. The framework and databases can be used as a validated source of surrogate modelling of steel frame structures in order for disaster risk management

Evaluation of self-compacting rubberized concrete properties: Experimental and machine learning approach

Diverse negative impacts of waste tire disposal have created a menace to a cleaner environment worldwide. Global awareness on the use of unconventional materials in concrete necessitated the use of solid waste in concrete. Towards sustainable construction and building materials, in this study, powdered waste rubber tires (PWRT) were incorporated into self-compacting concrete as a partial substitute for fine aggregate. The suitability of the self-compacting rubberized concrete (SCRC) was assessed by conducting workability tests (slump flow, T50, and L-box), mechanical tests (compressive, splitting tensile, and flexural strength tests), microstructural analysis, and durability tests. The results showed that an increasing percentage of PWRT had an adverse effect on the workability and flowability of SCRC. Mechanical strength at 3, 7, 21, 28, 56, and 90 days exhibited a reduction with an increasing PWRT content. Furthermore, the microstructural analysis showed weaker adhesion at the interfacial transition zone in the SCRC. A correlation matrix with empirical relationships was also developed. The effect of acid attack on SCRC was measured by immersion in HCL and Na2SO4, and a poor resistance was noticed. Machine learning regression algorithms were employed to predict the SCRC mechanical properties, including linear, ridge, lasso, decision tree, random forest, extreme gradient boosting, and support vector. In addition, evaluation metrics with statistical checks were also used to assess the model's performance. Ridge regression appeared best suited for predicting the compressive strength, while random forest regression best estimates the tensile and flexural strength