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Contemporary construction techniques provide benefits of speed and cost savings on a large scale, and is viable in urban regions with exorbitant housing demand. In rural areas, where scale and access to technology are unavailable, locally prevalent vernacular architecture and methods are more suitable. Although vernacular construction techniques have historically proven more sustainable and climate-sensitive, the lack of skilled labour and lack of versatility in material selection limits its application on large-scale projects. This study explores the choice of building design and technology, from the context of embodied energy, carbon and other life cycle impacts for housing construction.

Life cycle assessment (LCA) that evaluates impacts due to the products/processes is used to analyse different construction techniques. Further a detailed estimation of embodied carbon and embodied energy is done for both “vernacular” and “contemporary” choices of construction methodology for a case study project.

The building constructed using vernacular techniques has lower embodied carbon and energy by over 30% compared to the other clusters designed using contemporary confined masonry techniques. However, with a few external interventions the contemporary methods can be implemented with improved sustainability.

The limitation of the study is that it presents a case study-based exploration into comparing construction techniques to provide a practical understanding of making sustainable design choices and, hence, is limited to two construction methods. However, the same method could be extended to compare other construction techniques. Furthermore, it does not present a whole building LCA since the operating phase impacts are assumed to be fairly constant for such housing type, irrespective of the chosen method. Similarly, the demolition phase or the potential of reuse of the waste generated, water consumption and cultural and social heritage are not investigated in comparing the alternatives. Nevertheless, future studies could perform extensive exploratory and modelling studies on the operation phase and demolition phase to understand these impacts further.

In mass housing projects that belong to the so-called “affordable housing” or low-income housing category, sustainability concerns are not yet at the forefront of the decision-making process. Therefore, this study emphasizes the importance of incorporating sustainability into building design and construction and making sustainability accessible to even low-income communities. Adequate planning, social awareness initiatives and imparting skills and knowledge of sustainability to these communities are of utmost importance. The choice of design and materials should be encouraged by keeping in mind lower upfront costs as well as low maintenance and operational costs.

The primary implications of the study are that the vernacular technologies are much superior in terms of sustainability in comparison to conventional construction of RCC framed structures as well as contemporary construction methods such as confined masonry. However, the implementation of such techniques presents significant challenges such as a lack of skilled forces, increased maintenance and lack of flexibility to minor modifications. Hence, although being a sustainable choice its acceptance and execution present practical difficulties. Therefore, this study primarily aims to reinforce the belief in vernacular architecture and techniques to build sustainable and resilient communities while highlighting the challenges of the modern world in implementing them.

Most studies advocate using construction methods based on their ease of implementation, maintenance or cost. However, this study highlights the importance of considering the aspect of sustainability in the context of the choice of methods for housing construction in urban and semi-urban areas. This study also addresses the need not to overlook vernacular construction technologies while selecting technology for housing for low-income communities.

The purpose of this paper is to develop a reliable model that would predict the compressive strength of slurry infiltrated fiber concrete (SIFCON) modified with various supplementary cementitious materials (SCMs) using artificial intelligence approach.

This study engaged the artificial intelligence to predict the compressive strength of SIFCON through deep neural networks (DNN), artificial neural networks, linear regression, regression trees, support vector machine, ensemble trees, Gaussian process regression and neural networks (NN). A thorough data set of 387 samples was gathered from relevant studies. Eleven variables (cement, silica fume, fly ash, metakaolin, steel slag, fine aggregates, steel fiber fraction, steel fiber aspect ratio, superplasticizer, water to binder ratio and curing ages) were taken as input to predict the output (compressive strength). The accuracy and reliability of the developed models were assessed using a variety of performance metrics.

The results showed that the DNN (11-20-20-20-1) predicted the compressive strength of SIFCON better than the other algorithms with R² and mean square error yielding 95.89% and 8.07. The sensitivity analysis revealed that steel fiber, cement, silica fume, steel fiber aspect ratio and superplasticizer are the most vital variables in estimating the compressive strength of SIFCON. Steel fiber contributed the highest value to the SIFCON’s compressive strength with 16.90% impact.

This is a novel technique in predicting the compressive strength of SIFCON optimized with different SCMs using supervised learning algorithms, improving its quality and performance.

The purpose of the paper is to predict mapping of areas vulnerable to flooding in the Ourika watershed in the High Atlas of Morocco with the aim of providing a useful tool capable of helping in the mitigation and management of floods in the associated region, as well as Morocco as a whole.

Four machine learning (ML) algorithms including k-nearest neighbors (KNN), artificial neural network, random forest (RF) and x-gradient boost (XGB) are adopted for modeling. Additionally, 16 predictors divided into categorical and numerical variables are used as inputs for modeling.

The results showed that RF and XGB were the best performing algorithms, with AUC scores of 99.1 and 99.2%, respectively. Conversely, KNN had the lowest predictive power, scoring 94.4%. Overall, the algorithms predicted that over 60% of the watershed was in the very low flood risk class, while the high flood risk class accounted for less than 15% of the area.

There are limited, if not non-existent studies on modeling using AI tools including ML in the region in predictive modeling of flooding, making this study intriguing.

In recent years, studies have shown that machine learning significantly improves student performance and retention and reduces the risk of student dropout and withdrawal. However, there is a lack of empirical research reviews focusing on the application of machine learning to predict student performance in terms of learning engagement and self-efficacy and exploring their relationships. Hence, this paper conducts a systematic research review on the application of machine learning in higher education from an empirical research perspective.

This systematic review examines the application of machine learning (ML) in higher education, focusing on predicting student performance, engagement and self-efficacy. The review covers empirical studies from 2016 to 2024, utilizing a PRISMA framework to select 67 relevant articles from major databases.

The findings show that ML applications are widely researched and published in high-impact journals. The primary functions of ML in these studies include performance prediction, engagement analysis and self-efficacy assessment, employing various ML algorithms such as decision trees, random forests, support vector machines and neural networks. Ensemble learning algorithms generally outperform single algorithms regarding accuracy and other evaluation metrics. Common model evaluation metrics include accuracy, F1 score, recall and precision, with newer methods also being explored.

First, empirical research literature was selected from only four renowned electronic journal databases, and the literature was limited to journal articles, with the latest review literature and conference papers published in the form of conference papers also excluded, which led to empirical research not obtaining the latest views of researchers in interdisciplinary fields. Second, this review focused mainly on the analysis of student grade prediction, learning engagement and self-efficacy and did not study students’ risk, dropout rates, retention rates or learning behaviors, which limited the scope of the literature review and the application field of machine learning algorithms. Finally, this article only conducted a systematic review of the application of machine learning algorithms in higher education and did not establish a metadata list or carry out metadata analysis.

The review highlights ML’s potential to enhance personalized education, early intervention and identifying at-risk students. Future research should improve prediction accuracy, explore new algorithms and address current study limitations, particularly the narrow focus on specific outcomes and lack of interdisciplinary perspectives.

Cost overrun in infrastructure projects is a constant concern, with a need for a proper solution. The current estimation practice needs improvement to reduce cost overruns. This study aimed to find possible statistical modelling techniques that could be used to develop cost models to produce more reliable cost estimates.

A bibliographic literature review was conducted using a two-stage selection method to compile the relevant publications from Scopus. Then, Visualisation of Similarities (VOS)-Viewer was used to develop the visualisation maps for co-occurrence keyword analysis and yearly trends in research topics.

The study found seven primary techniques used as cost models in construction projects: regression analysis (RA), artificial neural network (ANN), case-based reasoning (CBR), fuzzy logic, Monte-Carlo simulation (MCS), support vector machine (SVM) and reference class forecasting (RCF). RA, ANN and CBR were the most researched techniques. Furthermore, it was observed that the model's performance could be improved by combining two or more techniques into one model.

The research was limited to the findings from the bibliometric literature review.

The findings provided an assessment of statistical techniques that the industry can adopt to improve the traditional estimation practice of infrastructure projects.

This study mapped the research carried out on cost-modelling techniques and analysed the trends. It also reviewed the performance of the models developed for infrastructure projects. The findings could be used to further research to develop more reliable cost models using statistical modelling techniques with better performance.

This study aims to investigate the determinants of Moroccan consumers’ intentions to boycott products associated with Israel amidst the prolonged Palestinian–Israeli conflict. As global interest in ethical consumption and consumer activism intensifies, this research explores how sociopolitical sentiments influence boycott behaviors in emerging markets.

This study uses a quantitative methodology based on a novel technique that comprised a two-phase analysis including structural equation modeling (SEM) and machine learning through artificial neural network (ANN). SEM was used to analyze direct and indirect relationships among variables, offering insights into both causality and model validity. ANN complemented SEM by examining nonlinear relationships, using multilayer perceptron analysis and cross-validation to assess predictive accuracy and reveal the relative importance of each predictor. An online survey, based on a seven-point Likert scale, gathered data from 234 Moroccan consumers, surpassing the required sample size for robust analysis.

The results reveal that consumer animosity, positive and negative anticipated emotions, subjective norms and social media influence boycott intentions significantly, whereas negative or positive anticipated emotion do not affect the intention to boycott surrogate Israeli products. This study highlights that consumers’ perceived responsibility and emotional responses to geopolitical issues shape their purchase behaviors, underlining ethical consumption’s complexity in Morocco.

This study primarily examines Arab and Muslim participants, potentially limiting its generalizability. Future research should include non-Muslim and non-Arab individuals who oppose Israel, to strengthen the findings on surrogate product consumption and boycott behavior, enhancing the robustness and broader applicability of the conclusions.

This study offers two key practical implications. First, it provides nongovernmental organizations and advocacy groups with insights on leveraging consumer boycotts as effective tools for promoting ethical and social causes. Second, it highlights how MSMEs can gain a competitive advantage by aligning their branding with cultural and ethical values, fostering consumer loyalty in politically engaged markets.

Positioned at the crossroads of Africa and the Middle East, Morocco is not immune to the conflict’s impact on marketing and consumer behavior. This research offers a novel approach to understanding Moroccan consumers’ intention to boycott Israeli surrogate products. This study contributes to global consumer behavior understanding and highlights sociopolitical implications of the Israeli–Palestinian conflict.

Accurate predictions of the steady-state corrosion phase and service life to achieve specific safety limits are crucial for assessing the service of reinforced concrete (RC) structures. Forecasting the service life (SL) of structures is imperative for devising maintenance and repair strategy plans. The optimization of maintenance strategies serves to prolong asset life, mitigate asset failures, minimize repair costs and enhance health and safety standards for society.

The well-known empirical conventional (traditional) approaches and machine learning (ML)-based SL prediction models were presented and compared. A comprehensive parametric study was conducted on existing models, considering real-world conditions as reported in the literature. The analysis of traditional and ML models underscored their respective limitations.

Empirical models have been developed by considering simplified assumptions and relying on factors such as corrosion rate, steel reinforcement diameter and concrete cover depth, utilizing fundamental mathematical formulas. The growth of ML in the structural domain has been identified and highlighted. The ML can capture complex relationships between input and output variables. The performance of ML in corrosion and service life evaluation has been satisfactory. The limitations of ML techniques are discussed, and its open challenges are identified, along with insights into the future direction to develop more accurate and reliable models.

To enhance the traditional modeling of service life, key areas for future research have been highlighted. These include addressing the heterogeneous properties of concrete, the permeability of concrete and incorporating the interaction between temperature and bond-slip effect, which has been overlooked in existing models. Though the performance of the ML model in service life assessment is satisfactory, models overlooked some parameters, such as the material characterization and chemical composition of individual parameters, which play a significant role. As a recommendation, further research should take these factors into account as input parameters and strive to develop models with superior predictive capabilities.

Recent deployment has revealed that ML algorithms can grasp complex relationships among key factors impacting deterioration and offer precise evaluations of remaining SL without relying on traditional models. Incorporation of more comprehensive and diverse data sources toward potential future directions in the RC structural domain can provide valuable insights to decision-makers, guiding their efforts toward the creation of even more resilient, reliable, cost-efficient and eco-friendly RC structures.

This paper develops a neural network surrogate model based on a discrete lattice approach to investigate the influence of complex microstructures on the emergent behavior of biological networks.

The adaptability of network-forming organisms, such as, slime molds, relies on fluid-to-solid state transitions and dynamic behaviors at the level of the discrete microstructure, which continuum modeling methods struggle to capture effectively. To address this challenge, we present an optimized approach that combines lattice spring modeling with machine learning to capture dynamic behavior and develop nonlinear constitutive relationships.

This integrated approach allows us to predict the dynamic response of biological materials with heterogeneous microstructures, overcoming the limitations of conventional trial-and-error lattice design. The study investigates the microstructural behavior of biological materials using a neural network-based surrogate model. The results indicate that our surrogate model is effective in capturing the behavior of discrete lattice microstructures in biological materials.

The combination of numerical simulations and machine learning endows simulations of the slime mold Physarum polycephalum with a more accurate description of its emergent behavior and offers a pathway for the development of more effective lattice structures across a wide range of applications.

The novelty of this research lies in integrating lattice spring modeling and machine learning to explore the dynamic behavior of biological materials. This combined approach surpasses conventional methods, providing a more holistic and accurate representation of emergent behaviors in organisms.

Machine learning (ML) technologies are increasingly being applied in building cost estimation as an advanced method to overcome the challenge of insufficient data and subjective effects of experts. To address the gap of lacking a review of ML applications in building cost estimation, this research aimed to conduct a systematic literature review to provide a robust reference and suggest development pathways for creating novel ML-based building cost prediction models, ultimately enhancing construction project management capabilities.

A systematic literature review according to preferred reporting items for systematic reviews and meta-analyses (PRISMA) was adopted using quantitative bibliographic analysis and qualitative narrative synthesis based on the 70 screened publications from Web of Science (WOS) and Scopus databases. The VOSviewer software was used to prepare the thematic focus from the bibliographic data garnered.

Based on the results of a bibliographic analysis, current research hotspots and future trends in the application of ML to building cost estimation have been identified. Additionally, the mechanisms behind existing ML models and other key points were analyzed using narrative synthesis. Importantly, the weaknesses of current applications were highlighted and recommendations for future development were made. These recommendations included defining the availability of building attributes, increasing the application of emerging ML algorithms and models to various aspects of building cost estimation and addressing the lack of public databases.

The findings are instrumental in aiding project management professionals in grasping current trends in ML for cost estimation and in promoting its adoption in real-world industries. The insights and recommendations can be utilized by researchers to refine ML-based cost estimation models, thereby enhancing construction project management. Additionally, policymakers can leverage the findings to advocate for industry standards, which will elevate technical proficiency and ensure consistency.

Compared to previous research, the findings revealed research hotspots and future trends in the application of ML cost estimation models in only building projects. Additionally, the analysis of the establishment mechanisms of existing ML models and other key points, along with the developed recommendations, were more beneficial for developing improved ML-based cost estimation models, thereby enhancing project management capabilities.

This study reviews research applying Space Syntax, in conjunction with other methodologies, to explore walkability and socio-spatial relations in urban environments, with a particular emphasis on moderate- and low-density city centers.

A systematic literature review was conducted following PRISMA guidelines, reviewing English-language studies published from 2013 to 2023, involving introduction to research context, information sources and search strategy, screening process and application of eligibility and inclusion criteria to ensure a comprehensive evaluation of relevant papers.

This research highlights that the predominant focus in the literature has been on high-density city centers in existing Space Syntax studies, revealing a significant gap in understanding socio-spatial dynamics, particularly in moderate- and low-density city centers. Furthermore, this research found that technology-based tools were the most frequently used in past studies, with 454 instances, compared to participatory tools (191 instances) and observational tools (57 instances), when using Space Syntax as an integrated approach to explore socio-spatial relations in urban environments. Moreover, emerging technology-based tools remain the least used technological tools in the literature. Combining these approaches represents a recent trend that could offer valuable insights into pedestrian socio-spatial experiences in both broader urban contexts and smaller-scale city centers.

Unlike previous literature reviews on Space Syntax that neglected its use as an integrated approach, this study uniquely explores the correlation between spatial configurations and human experiences through a review of studies that combine space syntax with other methods, including observational, participatory and technology-based tools to pinpoint the gaps. The research recommends further exploration of pedestrians’ socio-spatial needs by integrating Space Syntax with trends and less-explored technology-based tools.