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The equipment resale price plays an important role in calculating the optimum time for equipment replacement. Some of the existing models that predict the equipment resale price do not take many of the influencing factors on the resale price into account. Other models consider more factors that influence equipment resale price, but they still with low accuracy because of the modeling techniques that were used. An easy tool is required to help in forecasting the resale price and support efficient decisions for equipment replacement. This research presents a machine learning (ML) computer model helping in forecasting accurately the equipment resale price.

A measuring method for the influencing factors that have impacts on the equipment resale price was determined. The values of those factors were measured for 1,700 pieces of equipment and their corresponding resale price. The data were used to develop a ML model that covers three types of equipment (loaders, excavators and bulldozers). The methodology used to develop the model applied three ML algorithms: the random forest regressor, extra trees regressor and decision tree regressor, to find an accurate model for the equipment resale price. The three algorithms were verified and tested with data of 340 pieces of equipment.

Using a large number of data to train the ML model resulted in a high-accuracy predicting model. The accuracy of the extra trees regressor algorithm was the highest among the three used algorithms to develop the ML model. The accuracy of the model is 98%. A computer interface is designed to make the use of the model easier.

The proposed model is accurate and makes it easy to predict the equipment resale price. The predicted resale price can be used to calculate equipment elements that are essential for developing a dependable equipment replacement plan. The proposed model was developed based on the most influencing factors on the equipment resale price and evaluation of those factors was done using reliable methods. The technique used to develop the model is the ML that proved its accuracy in modeling. The accuracy of the model, which is 98%, enhances the value of the model.

Heritage buildings are a witness to previous civilizations and constitute important elements in transmitting cultural identity through generations. In 1938, Alexandria University was established; it was called the University of Farouk at the time. In 1952, the university was named “Alexandria University,” and since then, it has witnessed growth and expansion in several fields. The research aims to preserve the heritage of this academic institution. It seeks to document this wealth of buildings that tell the story of the second-earliest university in Egypt.

A mixed-method approach was employed. A descriptive method was used to narrate the history of the university and the importance of its buildings. Within the quantitative approach, a questionnaire was chosen as the survey instrument for collecting the data within the research case study. The aim was to determine the awareness of students, staff and employees of the heritage importance of their faculty. Within the qualitative approach, several interviews were conducted with employees in the engineering departments of the university administrative building at Chatby and some of the selected faculties. The aim was to determine the methods used for the conservation of these buildings.

Alexandria University has a heritage value not only in its great history but also through its heritage buildings. Raising the awarness of the university's affiliates of this heritage will lead to enhance the feelings of loyalty and belongings to the university. Therefore, preserving this heritage and properly managing it is crucial.

Universities have to recognize that their built heritage constitutes a unique expression that can create a distinctive sense of place. University heritage is crucial in defining and interpreting the university cultural identity. The institution must identify resources that will help build a new public image and contribute to develop a successful brand. Campus appearance is an important factor that has a significant impact on student feelings of loyalty and belonging.

The primary objective of this study is to examine the effects of integrating human-centric technology on the overall effectiveness of sustainable building practises. This study intends to address the knowledge gap regarding the influence of human-centered factors on the adoption of technology in the construction industry. Additionally, it wants to establish a strong correlation between the integration of technology and the promotion of sustainability.

This research utilizes Partial Least Square Equation Modeling (SEM) as a comprehensive approach to analyze the intricate interrelationships within the building sector. This study examines a range of aspects, including “Change Management,” “Evaluation and Optimization,” “Integration and Coordination,” “Long-Term Planning,” “Safety and Compliance,” and “Training and User Engagement,” in order to evaluate their impact on the adoption of technology and the achievement of sustainable building outcomes.

The study reveals a robust and affirmative correlation between the use of human-centric technology and the attainment of success in the realm of sustainable building. The text elucidates the primary factors that influence the adoption of technology and underscores the need of adopting a comprehensive strategy that encompasses change management, adherence to safety standards, and optimization of resources.

This study contributes novelty by placing emphasis on the human-centric dimensions of technology adoption within the context of sustainable building. The results of this research provide a valuable contribution to the ongoing development of sustainable building practises and the adoption of new technologies in this sector.