International Journal of BIM and Engineering Science

This study examines the role of Historic Building Information Modelling (HBIM) in preserving the architectural heritage of the Old City of Aleppo, focusing on a case study of the Al-Matbakh al-Ajami building. The study aims to provide an integrated framework for using HBIM for documenting and managing historical buildings. This is done through multiple stages and working according to the levels of detail by developing the 3D model from LOD200 to LOD500, which contributes to improving restoration and maintenance processes.

Efficient change order management is crucial in construction, particularly as project requirements evolve over time. In Syria's traditional construction process, lengthy gaps between planning, design, and execution significantly increase the likelihood of changes. This paper introduces a methodology that leverages Building Information Modeling (BIM) and cloud computing to enhance change management. A detailed case study of the Al-Eddekhar Housing project in Tartous was conducted, where Revit was employed for 3D modeling and Primavera for scheduling and cost estimation. Changes were meticulously analyzed using Revit's Model Compare tool, tracked through Primavera, and managed using Autodesk Construction Cloud for seamless document exchange and version control. The integration of BIM and cloud computing facilitates real-time collaboration between teams, significantly reducing errors, minimizing delays, and boosting overall project efficiency. The platform also preserves a historical record of project versions, enables visual comparisons of 3D models, and streamlines the approval process for change orders.

Smart agriculture leverages Internet of Things (IoT) technology to improve crop yield, resource efficiency, and environmental sustainability. This study presents an IoT-based smart agricultural monitoring system that integrates Wireless Sensor Networks (WSNs) with predictive analytics to monitor key environmental parameters, such as soil moisture, temperature, humidity, and light intensity, in real-time. The system utilizes WSNs to gather data from distributed sensor nodes and employs machine learning models for predictive analytics, enabling proactive decision-making for optimized irrigation, fertilization, and pest control. Experimental results demonstrate that the proposed system enhances resource usage by 40% and increases crop yield by 30% compared to traditional farming methods with Artificial Intelligence (AI). Additionally, the predictive analytics component achieves an accuracy of 92% in forecasting environmental conditions, aiding in timely interventions and minimizing crop stress. This IoT-based solution supports sustainable farming practices and offers scalability for various agricultural applications, including precision farming and greenhouse monitoring.

Wireless Sensor Networks (WSNs) are pivotal for real-time environmental monitoring, providing valuable data on variables like temperature, humidity, and pollution levels. However, ensuring timely and accurate data transmission and analysis remains a challenge due to resource constraints in WSNs. This study introduces a machine learning-enhanced WSN framework that leverages predictive algorithms for efficient data processing and anomaly detection in real time. By integrating machine learning models, the system can predict environmental trends, detect sensor faults, and identify unusual events, improving data reliability and reducing network load. Experimental evaluations in a simulated environment show a 40% improvement in anomaly detection accuracy and a 35% reduction in data redundancy. Furthermore, this framework achieved a 25% increase in energy efficiency, enhancing network longevity. This machine learning-optimized WSN framework provides an effective solution for continuous environmental monitoring in applications such as wildlife tracking, pollution control, and smart agriculture.

The increasing demand for personalized, efficient, and adaptive healthcare solutions has catalyzed the development of dynamic, learning-driven software ecosystems. This paper introduces a novel framework that leverages real-time data and machine learning algorithms to revolutionize healthcare services. The proposed system integrates continuous learning capabilities to enhance decision-making, optimize resource allocation, and enable precise diagnostics and treatment plans. By incorporating real-time data from patient monitoring systems, electronic health records, and IoT-enabled devices, the ecosystem offers adaptable healthcare solutions that evolve based on new data insights. The adaptability and scalability of the proposed framework ensure that healthcare providers can offer timely and personalized interventions while minimizing operational costs. Key features include dynamic learning models, predictive analytics, and seamless integration with existing healthcare infrastructures. Through extensive case studies, the paper demonstrates how these innovations can transform patient care, improve outcomes, and support proactive healthcare management.

Curriculum design is a critical aspect of education, requiring careful consideration of content relevance, student progression, and pedagogical coherence. In recent years, the use of Knowledge Graphs (KG) has gained attention for their ability to represent complex relationships between concepts in a structured format. This paper introduces KGCD (Knowledge Graph-based Curriculum Design), a novel approach to intelligent curriculum design that leverages knowledge graphs to model subject matter interdependencies, skill progression, and student learning paths. By incorporating AI-driven insights, KGCD offers educators a powerful tool for designing adaptive, personalized curricula that align with student needs and educational goals. The system provides real-time suggestions for curriculum adjustments, ensuring the inclusion of relevant content and logical sequencing of topics. Initial pilot studies demonstrate KGCD’s potential to improve curriculum coherence and student learning outcomes by providing data-driven support for curriculum development and revision.