This paper presents the results of an experimental investigation carried out to evaluate the effect of fly ash (FA) on fine sand compaction and its suitability as a material for embankments. The literature review demonstrates the lack of research on stabilization of sandy material using FA. The study is concerned with the role of FA content in stabilized soil physical characteristics. The main aim of this paper is to determine the optimum quantity of FA content for stabilization of this type of soil. This is achieved through particle size distribution and compaction (standard proctor) tests. The sand was stabilized with three proportions of FA (5%, 10% and 15%) and constant cement content of 3% was used as an activator. For better comparison, the sand was also stabilized by 3% cement only so that the effect of FA could be observed more clearly. The results were in line with the literature for other types of soil, i.e. as the % of FA increases, reduction in maximum dry density and higher optimum moisture content were observed.

In order to fulfil the UK government’s ambitious goal of 80% reductions in greenhouse gas emissions by 2050 compared to the levels of 1990s, unprecedented measures for improving the energy efficiency of buildings are needed. This study investigates the impact of a specific type of Low-emissivity (Low-E) window film—Thinsulate Climate Control 75—on the holistic energy consumption of an existing United Kingdom (UK) hotel building. Building modelling and energy simulation software EDSL TAS is used to conduct the study. The result of the simulations demonstrates that by applying Thinsulate films, savings in heating, cooling, and total energy consumptions are achieved by 3%, 20%, and 2.7%, respectively. Also 4.1% and 5.1% savings are achieved in annual CO₂ emissions and total energy costs, respectively, while the initial costs may be an issue. This study found that application of Low-E window films results in slightly better energy performance of the hotel regarding its heating-dominant climate. The study also recommends using average annual actual energy consumption data for a time range, instead of picking a single year’s data for validating purposes.

Studies conducted by major national and international scientific bodies have indisputably concluded that the increase in anthropogenic greenhouse gas emissions (GHG) since the mid-20th century has led to irreversible changes in the climate. Data has shown that the contribution of the building sector accounts for 39% of these emissions. Reducing GHG emissions associated with the construction phase of buildings, or embodied carbon (EC), will prevent GHG emissions from entering the atmosphere earlier, reducing the negative impacts. However, to achieve any meaningful reduction, there is a need for consistency and accuracy in the calculations. The accuracy of these calculations is primarily tied to the accuracy of embodied carbon factors (ECF) used in the calculations, values determining the environmental impact of a product or procedure per unit weight. The emissions of any product can be calculated by performing a Life Cycle Assessment (LCA). While the requirements for carrying out an LCA have been standardised in ISO14044, the lack of a definitive national ECF database in the UK means that EC calculations can vary drastically based on the chosen database. An LCA has been carried out on a standard Lidl supermarket design within the A1–A3 boundary. For the calculation, the ECFs were sourced from two different databases, using the GHG conversion factor data published in 2020 by the UK Department of Energy & Climate Change and data published in 2019 by the Inventory of Carbon and Energy (ICE). The latter is currently accepted as the most consistent database for carbon factors in the UK. This study showed that using a more detailed database compared to using a more general database could result in a 35.2% reduction of embodied carbon, while using more detailed data from a single database can reduce it by a further 5.5%. It is necessary to establish the most accurate baseline for embodied carbon so that any carbon reduction attempts can be as effective as possible.

The integration of artificial intelligence (AI) and machine learning (ML) has revolutionised civil engineering, enhancing predictive accuracy, decision-making, and sustainability across domains such as structural health monitoring, geotechnical analysis, transportation systems, water management, and sustainable construction. This paper presents a detailed review of peer-reviewed publications from the past decade, employing bibliometric mapping and critical evaluation to analyse methodological advances, practical applications, and limitations. A novel taxonomy is introduced, classifying AI/ML approaches by civil engineering domain, learning paradigm, and adoption maturity to guide future development. Key applications include pavement condition assessment, slope stability prediction, traffic flow forecasting, smart water management, and flood forecasting, leveraging techniques such as Convolutional Neural Networks (CNNs), Long Short-Term Memory (LSTM), Support Vector Machines (SVMs), and hybrid physics-informed neural networks (PINNs). The review highlights challenges, including limited high-quality datasets, absence of AI provisions in design codes, integration barriers with IoT-based infrastructure, and computational complexity. While explainable AI tools like SHAP and LIME improve interpretability, their practical feasibility in safety-critical contexts remains constrained. Ethical considerations, including bias in training datasets and regulatory compliance, are also addressed. Promising directions include federated learning for data privacy, transfer learning for data-scarce regions, digital twins, and adherence to FAIR data principles. This study underscores AI as a complementary tool, not a replacement, for traditional methods, fostering a data-driven, resilient, and sustainable built environment through interdisciplinary collaboration and transparent, explainable systems.