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Abstract Investigating soil swelling potential is indeed a critical research area in geotechnical engineering, given its significant influence on the stability and longevity of civil structures. This study aims to predict and assess the one-dimensional vertical free swelling potential of soils using ensemble machine learning models. Within the study context, a large dataset encompassing a wide array of soil parameters from 210 soil samples, including moisture content, unit weight, plasticity, and clay content, will be used. These parameters are critical in understanding the swelling behavior of soils under varying environmental and load conditions. The novel approach of this research lies in the application of ensemble machine learning techniques, which offer a robust framework to analyze complex, nonlinear relationships within soil properties. Another key aspect of this research is the parametric assessment, where the influence of individual soil properties on swelling potential is investigated using feature importance and partial dependence analyses. These analyses provide valuable insights into the relative importance of different soil parameters on soil behavior. The outcomes of this study contribute to soil mechanics and machine learning applications in geotechnical engineering and offer practical implications for engineers and practitioners. Besides, the predictive models developed in this study aid in more informed decision-making in the design and construction of civil structures, particularly in swelling-prone areas.

Abstract Infrastructure resilience is a critical issue in arid regions due to the unpredictability of extreme rainfall events influenced by climate change. Accordingly, the integration of artificial intelligence (AI) in engineering solutions to address such climatic challenges is important. This study aims to review AI-based engineering solutions for infrastructure resilience in arid areas against extreme rainfall and most importantly critically analyzes their sustainability benefits. By evaluating existing AI-based engineering solutions, this research highlights how these technologies can contribute to sustainable development goals (SDGs) and eventually aims to help set new standards in infrastructure resilience. The significance of this study lies in its potential to advance sustainable development goals by enhancing the resilience of critical infrastructure and equipping arid regions with innovative solutions to withstand adverse weather conditions. Expected outcomes of this study are anticipated to provide pivotal insights for policymakers, engineers, and urban planners, promoting the adoption of intelligent, sustainable infrastructure practices that are in line with global sustainability targets. Finally, this research promotes the adoption of AI solutions in infrastructure resilience and lays the groundwork for future explorations in this field.

Armed conflict is an evolution in reverse that significantly damages infrastructure, displaces students and faculty, and limits access to resources, necessitating innovative solutions. This research uses Syria as a case study to investigate the potential of a digital transformation strategy to revitalize the higher education system in war-ravaged regions. It proposes a digital transformation framework utilizing a mixed-method approach, including literature reviews, expert interviews, and case analysis, to identify critical challenges faced by Syrian higher education institutions. The framework focuses on developing digital infrastructure, capacity building for educators, e-learning platforms, digital content, and public-private partnerships that increase education access, foster collaboration, enable remote learning and enhance global competitiveness in war-torn areas. The e-learning platforms component explores the potential of Massive Open Online Courses (MOOCs), Learning Management Systems (LMS), and blended learning approaches to enhance the quality and accessibility of higher education. The digital content component highlights the importance of open educational resources, digital libraries, and virtual labs to support learning. Finally, the capacity building component addresses the need for training and upskilling educators and administrators to implement digital solutions effectively. The proposed framework is a roadmap for policymakers, educators, and international organizations to develop and implement digital solutions supporting long-term recovery and growth in conflict-affected areas.

Over the last nine years, the ongoing armed conflict in Syria has had a devastating impact on properties and infrastructure, making it necessary to rebuild everything from scratch. As spatial data are a pillar to enhance sustainable socio-economic development, it should be georeferenced and reformed to keep up with the tremendous growth in information and communication technology. On the other hand, the low-distortion conformal mapping contributes significantly to changing the position of any feature on the earth's surface onto a plane to create accurate large-scale spatial data. The current research conducts an analytical study using spatial statistics tools to investigate an optimal conformal projection for representing the Syrian area in a single zone that preserves angles locally, reduces linear distortions, and fulfills modern geospatial technologies' requirements. The findings have shown the applicability of the proposed model as an alternative solution to minimize the scale error over the interest area. In addition, the isograms pattern is close to the shape of the area, and better fits the quality criteria of the chosen projection.

The development of geospatial technologies has opened a new era in terms of data collection techniques and analysis procedures. Digital elevation models as 3D visualization of the Earth’s surface have many mapping and spatial analysis applications. The primary terrain factors derived from the raster dataset are usually less critical than secondary ones, e.g., ruggedness index, which plays a vital role in engineering, hydrological information derivation, and geomorphological processes. Surface ruggedness is a significant predictor of topographic heterogeneity by calculating the absolute value of elevation differences within a specified neighborhood surrounding a central pixel. The current study investigates the impacts of various topographic metrics obtained from a digital elevation model on characterizing terrain ruggedness utilizing stepwise principal component analysis. This popular multivariate statistical technique is applied to conduct a comprehensive assessment and treat the information redundancy of terrain parameters. Simultaneously, the standard deviation of elevation is also proposed as an alternative approach to quantifying topographic ruggedness. Besides, quantitative and qualitative method is espoused to validate the algorithms and compare their capabilities to the previously introduced models in the literature. The findings have shown that principal component analysis provides superior performance against other models. Furthermore, they indicated that the standard deviation of elevation could be used instead of the available ones.

In fact, Computer Aided Design (CAD) offers powerful design tools to produce digital large scale topographic mapping that is considered the backbone for construction projects, urban planning and landscape architecture. Nowadays local agencies in small communities and developing countries are facing some difficulties in map to map transformation and handling discrepancies between the physical reality and represented spatial data due to the need for implementing high cost systems such as GIS and the experienced staff required. Therefore, the require for providing a low-cost tool based on the most common CAD system is very important to guarantee a quality and positional accuracy of features. The main aim of this study is to describe a mathematical relationship to fulfil the coordinate conversion between two different grid references applying two-dimensional conformal polynomial models built on control points and a least squares fitting algorithm. In addition, the automation of this model was performed in the Microsoft Visual Studio environment to calculate polynomial coefficients and convert the positional property of entities in AutoCAD by developing spatial CAD tool. To evaluate the proposed approach the extracted coordinates of check points from the interpolation surface are compared with the known ones.

In this study, a multi-linear regression model for potential fishing zone (PFZ) mapping along the Saudi Arabian Red Sea coasts of Yanbu’ al Bahr and Jeddah was developed, using Moderate Resolution Imaging Spectroradiometer (MODIS) satellite data derived parameters, such as sea surface salinity (SSS), sea surface temperature (SST), and chlorophyll-a (Chl-a). MODIS data was also used to validate the model. The model expanded on previous models by taking seasonal variances in PFZs into account, examining the impact of the summer, winter, monsoon, and inter-monsoon season on the selected oceanographic parameters in order to gain a deeper understanding of fish aggregation patterns. MODIS images were used to effectively extract SSS, SST, and Chl-a data for PFZ mapping. MODIS data were then used to perform multiple linear regression analysis in order to generate SSS, SST, and Chl-a estimates, with the estimates validated against in-situ data obtained from field visits completed at the time of the satellite passes. The proposed model demonstrates high potential for use in the Red Sea region, with a high level of congruence found between mapped PFZ areas and fish catch data (R2 = 0.91). Based on the results of this research, it is suggested that the proposed PFZ model is used to support fisheries in determining high potential fishing zones, allowing large areas of the Red Sea to be utilized over a short period. The proposed PFZ model can contribute significantly to the understanding of seasonal fishing activity and support the efficient, effective, and responsible use of resources within the fishing industry.