This study aimed to expand the knowledge on the application of the most common industrial byproduct, i.e., fly ash, as a supplementary cementitious material. The characteristics of cement-based composites containing fly ash as supplementary cementitious material were discussed. This research evaluated the mechanical, durability, and microstructural properties of FA-based concrete. Additionally, the various factors affecting the aforementioned properties are discussed, as well as the limitations associated with the use of FA in concrete. The addition of fly ash as supplementary cementitious material has a favorable impact on the material characteristics along with the environmental benefits; however, there is an optimum level of its inclusion (up to 20%) beyond which FA has a deleterious influence on the composite’s performance. The evaluation of the literature identified potential solutions to the constraints and directed future research toward the application of FA in higher amounts. The delayed early strength development is one of the key downsides of FA use in cementitious composites. This can be overcome by chemical activation (alkali/sulphate) and the addition of nanomaterials, allowing for high-volume use of FA. By utilizing FA as an SCM, sustainable development may promote by lowering CO₂ emissions, conserving natural resources, managing waste effectively, reducing environmental pollution, and low hydration heat.

The emergence of digital twin technology presents tremendous opportunities for several industry sectors. A digital twin is defined as the virtual representation of a physical asset that collects and sends real-time information. A digital twin collects data from the physical asset in real-time and uses this data to create a virtual model of the physical object. Its functionality depends on the bi-directional coordination of data between the physical and virtual models. This is likened to cyber-physical systems, which seek to provide bi-directional coordination between the physical and virtual worlds. While digital twins have found applications in the various industrial sectors such as aerospace, manufacturing, and industrial engineering, their applications in the construction industry are relatively limited. Although some level of progress has been made in the construction industry with the application of a digital twin, it still lags in other sectors. Virtual models of constructed facilities are developed and used to plan and construct the actual facility, with changes in the physical facility being automatically reflected in the virtual model based on real-time data and vice-versa. The digital twin shows promising possibilities in the design, construction, operation, and maintenance of a facility. This paper reviews the development and implementation of digital twin technology in the construction industry and compares its use with other industries while assessing the benefits of DT to the construction industry. A systematic literature review including a thematic analysis was employed to address the purpose of this study. Limitations associated with the existing and emerging applications are also identified. It concludes by highlighting the importance of DT applications in the construction sector.

Compressive and flexural strength are the crucial properties of a material. The strength of recycled aggregate concrete (RAC) is comparatively lower than that of natural aggregate concrete. Several factors, including the recycled aggregate replacement ratio, parent concrete strength, water–cement ratio, water absorption, density of the recycled aggregate, etc., affect the RAC’s strength. Several studies have been performed to study the impact of these factors individually. However, it is challenging to examine their combined impact on the strength of RAC through experimental investigations. Experimental studies involve casting, curing, and testing samples, for which substantial effort, price, and time are needed. For rapid and cost-effective research, it is critical to apply new methods to the stated purpose. In this research, the compressive and flexural strengths of RAC were predicted using ensemble machine learning methods, including gradient boosting and random forest. Twelve input factors were used in the dataset, and their influence on the strength of RAC was analyzed. The models were validated and compared using correlation coefficients (R²), variance between predicted and experimental results, statistical tests, and k-fold analysis. The random forest approach outperformed gradient boosting in anticipating the strength of RAC, with an R² of 0.91 and 0.86 for compressive and flexural strength, respectively. The models’ decreased error values, such as mean absolute error (MAE) and root-mean-square error (RMSE), confirmed the higher precision of the random forest models. The MAE values for the random forest models were 4.19 MPa and 0.56 MPa, whereas the MAE values for the gradient boosting models were 4.78 MPa and 0.64 MPa, for compressive and flexural strengths, respectively. Machine learning technologies will benefit the construction sector by facilitating the evaluation of material properties in a quick and cost-effective manner.

The depletion of natural resources and greenhouse gas emissions related to the manufacture and use of ordinary Portland cement (OPC) pose serious concerns to the environment and human life. The present research focuses on using alternative binders to replace OPC. Geopolymer might be the best option because it requires waste materials enriched in aluminosilicate for its production. The research on geopolymer concrete (GPC) is growing rapidly. However, substantial effort and expenses are required to cast specimens, cures, and tests. Applying novel techniques for the said purpose is the key requirement for rapid and cost-effective research. In this research, supervised machine learning (SML) techniques, including two individual (decision tree (DT) and gene expression programming (GEP)) and two ensembled (bagging regressor (BR) and random forest (RF)) algorithms were employed to estimate the compressive strength (CS) of GPC. The validity and comparison of all the models were made using the coefficient of determination (R²), k-fold, and statistical assessments. It was noticed that the ensembled SML techniques performed better than the individual SML techniques in forecasting the CS of GPC. However, individual SML model results were also in the reasonable range. The R² value for BR, RF, GEP, and DT models was 0.96, 0.95, 0.93, and 0.88, respectively. The models’ lower error values such as mean absolute error (MAE) and root mean square errors (RMSE) also verified the higher precision of ensemble SML methods. The RF (MAE = 2.585 MPa, RMSE = 3.702 MPa) and BR (MAE = 2.044 MPa, RMSE = 3.180) results are better than the DT (MAE = 4.136 MPa, RMSE = 6.256 MPa) and GEP (MAE = 3.102 MPa, RMSE = 4.049 MPa). The application of SML techniques will benefit the construction sector with fast and cost-effective methods for estimating the properties of materials.

Technological advances have enabled the monitoring and control of construction operations and assets remotely. Digital twins, based on computational modeling, have enabled the creation of a digital map for physical structures. Research on digital twins (DTs) for constructed facilities projects has gained widespread traction in the industry. While these applications have increased over the years, there has been sparse review of them. This paper systematically reviews the applications of digital twins in construction using content analysis. We identified and analyzed 53 academic journal and conference papers, which revealed several DT applications that could be categorized into nine areas: lifecycle analysis, facility management, energy, education, disaster, structural health monitoring, DT for cities, infrastructure management, and miscellaneous. This enables the visualization of the current state of DT, comparison with the desired state, and possible integrations with other technologies. Among the observed benefits of DTs are the ability to increase engagement and collaboration, reduce construction and operating costs, reduce human error, automate energy demand, manage assets throughout their lifecycle, and apply structural health monitoring. It also enables the collection of real-time data on an asset’s status, history, maintenance needs, and provides an interactive platform for managing an asset. Future directions include addressing how to standardize data acquisition as well as the semantic interoperability and heterogeneity of data. Additionally, modeling human cognitive processes as well as spatiotemporal information would be beneficial to a smart city and other infrastructure systems, especially in disaster situations.

The global energy demand has been increasing and posing multiple challenges across the globe, including global warming, environmental pollution, and energy-sustainability issues. Thus, multiple countries have been adopting renewable-energy (RE) sources to provide clean, reliable, affordable, and sustainable energy. Previously, a number of renewable energy projects has been delivered in the form of a public–private partnership (PPP) to take advantage of the private sector’s investment, technology advancements, and expertise. In general, renewable-energy projects are considered large-scale universal projects that involve expertise from different countries and require a clear understanding of the barriers and key success factors (KSFs) across the globe. Thus, this paper focuses on providing a comprehensive understanding of the main barriers and success factors of renewable-energy projects across the globe. For that aim, a comprehensive literature review was first carried out to identify and report on the barriers and KSFs of renewable-energy projects. This was followed by a questionnaire survey wherein the opinions of 60 experts with wide experience in RE PPPs in multiple countries were collected and analyzed. The analysis shows that political and regulatory barriers are the main risks globally. Additionally, well-prepared contract documentations and skilled and efficient parties are the KSFs. However, these factors change from one continent to another. Additionally, this paper sheds light on the difference between the public and private sectors’ perceptions on the severity of the risks and the importance of the KSFs to each sector.