Large scale earthmoving operations require the use of heavy and costly construction equipment. Optimum utilization of equipment is a crucial task for the project management team. It can result in substantial savings in both time and cost of earthmoving operations. This paper presents optimization model for earthmoving operations in heavy civil engineering projects. The developed model is designed to assist general contractor in optimizing planning of earthmoving operations. The model utilizes genetic algorithm, linear programming, and geographic information systems to support its management functions. The model assists in planning earthmoving operations; taking into consideration: (1) availability of resources to contractors; (2) project budget and/or time constraints, if any; (3) scope of work; (4) construction site conditions; (5) soil type; (6) project indirect cost; and (7) equipment characteristics. The model also determines the quantities of earth to be moved from different borrow pits and those to ...

Energy consumption of constructed educational facilities significantly impacts economic, social and environment sustainable development. It contributes to approximately 37% of the carbon dioxide emissions associated with energy use and procedures. This paper aims to introduce a study that investigates several artificial intelligence-based models to predict the energy consumption of the most important educational buildings; schools. These models include decision trees, K-nearest neighbors, gradient boosting, and long-term memory networks. The research also investigates the relationship between the input parameters and the yearly energy usage of educational buildings. It has been discovered that the school sizes and AC capacities are the most impact variable associated with higher energy consumption. While 'Type of School' is less direct or weaker correlation with 'Annual Consumption'. The four developed models were evaluated and compared in training and testing stages. The Decision Tree model demonstrates strong performance on the training data with an average prediction error of about 3.58%. The K-Nearest Neighbors model has significantly higher errors, with RMSE on training data as high as 38,429.4, which may be indicative of overfitting. In contrast, Gradient Boosting can almost perfectly predict the variations within the training dataset. The performance metrics suggest that some models manage this variability better than others, with Gradient Boosting and LSTM standing out in terms of their ability to handle diverse data ranges, from the minimum consumption of approximately 99,274.95 to the maximum of 683,191.8. This research underscores the importance of sustainable educational buildings not only as physical learning spaces but also as dynamic environments that contribute to informal educational processes. Sustainable buildings serve as real-world examples of environmental stewardship, teaching students about energy efficiency and sustainability through their design and operation. By incorporating advanced AI-driven tools to optimize energy consumption, educational facilities can become interactive learning hubs that encourage students to engage with concepts of sustainability in their everyday surroundings.

The application of renewable energy has been an integral part of the sustainability drive in the building sector and solar photovoltaic (PV) is one of the most effective technologies in this respect. The present study aims to investigate the prospects of solar PV in residential buildings in the hot–humid climatic conditions. The study discusses the utilization of building roofs for the application of PV in terms of potential hurdles and utilization factor (UF). Technical performance of PV systems has also been investigated in terms of power output as well as the energy saved as a result of the shading impact of panels for two types of residential units, apartments and villas. Investigation of 70 sample residential buildings reveals the average UF of 0.21 and 0.28 for apartments and villas, respectively. For the case study of apartment and villa residential units, roof UF has been found to be 13% and 15% with a respective PV output of 6079 kWh/year and 6162 kWh/year. Potential PV output at the city level has also been estimated. A sensitivity analysis has been conducted to evaluate the impact of various cost and design parameters on the viability of PV systems.

There have been many studies done on the causes of delay on construction projects and building projects all around the world. They use different methods, on different geographical areas, and they come up with different results. There are, however, few studies on industrial and building projects. This paper focuses on industrial/manufacturing projects in Saudi Arabia. The aim of this paper is to identify the major causes of delay in the manufacturing projects in Saudi Arabia. The top causes of delay have been identified through a literature survey and interviews with experts from the industrial field in Saudi Arabia. Twenty-two factors have been identified and listed in a survey that was distributed to professionals in the same field. Two categorizations were made: the first is based on the impact of the cause, and the second is based on the frequency of occurrence of the identified cause. It has been found that the top five impacted causes of delay in the delivery of industrial projects in Saudi Arabia are: difficulties in financing project by contractor/manufacturer, late procurement of materials, late delivery of materials, delay in progress payments, and delay in approving design documents, respectively. In terms of frequency, the top five identified causes are: delay in progress payments, difficulties in financing project by contractor/manufacturer, slowness in decision making, late procurement of materials, and delay in approving design documents, respectively. The diversity of the participants is an important point; therefore, the respondents were from different job positions (management, engineering, etc.), and different categories (contractor, owner, manufacturer, consultant, etc.). It is worth noting that this paper serves not only as an authentic study of the causes of delay in the delivery of industrial projects in Saudi Arabia which is a field that is not widely covered, but also as a fresh paper that gives an indication of the changes that happen to the business over time as compared to previous work.

This paper presents an automated method for estimating productivity of earthmoving operations in near-real-time. The developed method utilizes Global Positioning System (GPS) and Google Earth to extract the data needed to perform the estimation process. A GPS device is mounted on a hauling unit to capture the spatial data along designated hauling roads for the project. The variations in the captured cycle times were used to model the uncertainty associated with the operation involved. This was carried out by automated classification, data fitting, and computer simulation. The automated classification is applied through a spreadsheet application that classifies GPS data and identifies, accordingly, durations of different activities in each cycle using spatial coordinates and directions captured by GPS and recorded on its receiver. The data fitting was carried out using commercially available software to generate the probability distribution functions used in the simulation software “Extend V.6”. The simulation was utilized to balance the production of an excavator with that of the hauling units. A spreadsheet application was developed to perform the calculations. An example of an actual project was analyzed to demonstrate the use of the developed method and illustrates its essential features. The analyzed case study demonstrates how the proposed method can assist project managers in taking corrective actions based on the near-real-time actual data captured and processed to estimate productivity of the operations involved.