Self-compacting concrete (SCC) is a type of concrete known for its environmental benefits and improved workability. In this study, data-driven approaches were used to anticipate the compressive strength (CS) of self-compacting concrete (SCC) containing recycled plastic aggregates (RPA). A database of 400 experimental data sets was used to assess the capabilities of multi-objective genetic algorithm evolutionary polynomial regression (MOGA-EPR) and gene expression programming (GEP). The analysis results indicated that the proposed equations provided more accurate CS predictions than traditional approaches such as the linear regression model (LRM). The proposed equations achieved lower mean absolute error (MAE) and root mean square error (RMSE) values, a mean close to the optimum value (1.0), and a higher coefficient of determination (R²) than the LRM. As such, the proposed approaches can be utilized to obtain more reliable design calculations and better predictions of CS in SCC incorporating RPA.

One practical option for modifying an asphalt mixture’s performance is to use additives. This will help the mixture perform better against the damaging effects of traffic, loads, and climatic variations. In this regard, glass fiber (GF) has drawn much interest because of its positive effect. Therefore, this paper attempts to study the effect of glass fiber length and content on the performance and strength of asphalt mixtures. It also aims to determine the optimum glass fiber content and the best glass fiber length of modified asphalt mixtures. An experimental program is carried out, which includes the Marshall test, volumetric properties, freeze-thaw splitting test, immersion Marshall test, and wheel tracking test to characterize related properties of glass fiber incorporated in asphalt mixtures. Seven different percentages (0, 0.25, 0.5, 0.75, 1, 1.25, and 1.5) of glass fiber by total weight of aggregates in three various lengths are used to design 19 asphalt mixtures. Based on the results obtained, the performance of the asphalt mixture was enhanced remarkably after adding glass fiber. The use of various lengths of glass fiber led to a better-quality asphalt mixture in terms of volumetric properties, moisture damage resistance, and permanent deformation resistance. Specifically, asphalt mixtures made with (0.5%) glass fiber illustrated the highest quality, and adding (20 mm) length of glass fiber was better than (10 mm and 30 mm) glass fiber lengths. The results also show that adding (10 mm and 30 mm) lengths of glass fiber can improve the resistance of asphalt mixtures to water damage and permanent deformation compared with the control mixture (M0). The findings indicate the applicability of 20 mm glass fiber length in asphalt mixtures to achieve better resistance against moisture and reduce the chance of irreparable permanent deformation under growing traffic loads and hot climate changes. Although the inclusion of glass fiber in asphalt mixtures led to a modest increase (6%) in overall cost, the effective improvement in performance and extension of the service life of the asphalt pavement constitute a convincing argument for this approach, making it an attractive option. Finally, it was concluded that a higher amount of glass fiber (i.e., > 0.5%) and a length greater than (20 mm) could diminish the positive effect of glass fiber to improve the properties of glass fiber asphalt mixtures.

Water Treatment Residuals (WTRs) are a by-product of the addition of chemical coagulants to water during the water treatment process and are a mixture of water and organic and inorganic matter that coagulates during the treatment process. WTRs often contain metals such as iron, aluminium, and manganese that have been oxidised as part of the process or are constituents of the coagulation chemicals used. The metals within WTRs are of interest with regard to applying these sludges to agricultural land. WTRs can also contain beneficial organic matter and nutrients (primarily nitrogen). The nature of the benefits delivered is largely dependent on the quality of the raw water and these beneficial components are generally found in much smaller quantities in WTRs than are found in sewage sludge produced from wastewater. However, WTRs can still be used to enhance the physical properties of soils. As urban populations increase in size, it is anticipated that the tonnage of WTRs will increase significantly in the future. At present, the majority of WTRs are disposed of in landfills; however, landfill charges are increasing significantly, making disposal of an increasing tonnage of WTRs financially unviable. In terms of a circular economy, the procedure of reusing WTRs for alternative applications satisfies the Scottish Government’s goals in terms of waste prevention and reducing the amount of material being sent to landfill as set out in the Proposals for Legislation in 2019. Given the potential benefits in terms of cost savings and compliance with government legislation, and the complexities of understanding where and when WTRs can be used in land applications, we developed a Decision Support Tool (DST) that uses data obtained from an extensive review of approaches in other countries to assist in decision making. We also conducted a pre-application analysis and provided guidance on when and where WTRs can be used in land applications and when they are not suitable, presented in a simplified format that requires few inputs from the user in order to simplify the process and removes the requirement for a specialist operator during pre-application analyses.

The decarbonisation of the energy sector through major renewable energy developments in rural areas is one the requirements for sustainable development and future carbon-neutral societies. However, this has resulted in increased construction on peatlands and wetlands and has led to diverse environmental impacts in the affected areas. The overall aim of this project was to review the effectiveness of standard mitigation measures used during construction to maintain the hydrological conditions within peat soils and wetland habitats. This work involved a literature review of the evidence of the impacts of construction on the habitat and groundwater in groundwater-dependent wetlands and peatlands. In addition, developers and contractors were consulted to gain feedback on what practical approaches have and have not been successful and remedial actions taken when monitoring or observation identifies ongoing issues. This research also developed regulatory-relevant recommendations. The main recommendation focuses on the central importance of collecting relevant and detailed site investigation data at an early stage of the application process to enable a full understanding of the site character and to inform a more accurate design process. This will reduce or avoid impacts on the environment, minimise risk, and produce a more informed construction strategy.

Self-compacting concrete (SCC) is a type of concrete that is known for its environmental benefits and improved workability. In this study, data-driven approaches were used to anticipate the compressive strength (CS) of self-compacting concrete (SCC) containing recycled plastic aggregates (RPA). A database of 400 experimental data sets was used to assess the capabilities of Multi-Objective Genetic Algorithm Evolutionary Polynomial Regression (MOGA-EPR) and Gene Expression Programming (GEP). The results of the analysis indicated that the proposed equations provided more accurate CS predictions than traditional approaches such as the Linear Regression model (LRM). The proposed equations achieved lower mean absolute error (MAE) and root mean square error (RMSE) values, a mean close to the optimum value (1.0), and a higher coefficient of determination (R²) than the LRM. As such, the proposed approaches can be utilized to obtain more reliable design calculations and better predictions of CS in SCC incorporating RPA.