Prof. Emad Bakhoum

The slaughterhouse industry generates substantial wastewater rich in proteins, lipids, fibers, and carbohydrates. This study integrates experimental investigations into artificial neural network (ANN) optimization and commerce design studies for treating slaughterhouse and meat processing wastewater (SMW). Batch coagulation/flocculation experiments identified optimal conditions for three

One of the inevitable challenges that face the construction industry is the delay of project completion. The current state of the industry makes the need for delay analysis apparent, however the process of choosing the most reliable delay analysis technique can get very complex in some situations. This research aims to develop a framework to identify and analyze the most important factors to

Construction projects in Egypt are becoming more complex as the need for mega projects increases to meet the Egyptian 2030 vision. Meanwhile, the advancement of Virtual Reality technologies is apparent in the 21st century. This paper aims to assess the impact of implementing virtual reality in the Egyptian construction industry. An application using Fuzor VDC and Oculus Quest 2 has been developed

Construction, demolition, and renovation activities generate a significant amount of waste, posing serious environmental risks. The scarcity of recycling facilities makes it difficult to implement the new legislation, which calls for producing recycled aggregates. Moreover, the lack of studies on the environmental feasibility of recycling construction and demolition waste in regions with plentiful

Poor payment practices are perceived as one of the biggest challenges facing the construction industry. Since payments are issued according to project contract terms, the project’s cash flow is inherently affected by the contract and how parties fulfill their obligations. This research proposes a framework for payment automation in construction projects to achieve smart construction contracts

Integrating artificial intelligence and green concrete in the construction industry is a challenge that can help to move towards sustainable construction. Therefore, this research aims to predict the compressive strength of green concrete that includes a ratio of cement kiln dust (CKD) and fly ash (FA), then recommend the optimum sustainable mixture design. The artificial neural network (ANN) and

Recently, sustainability has become one of the most critical goals to be accomplished in the construction industry to mitigate its environmental impacts, energy consumption, waste, and cost. Therefore, this research aims to assess the sustainability of concrete structures using the Building Information Modeling and Life Cycle Assessment (BIM–LCA) approach. It can aid to rank and select the type of

Concrete industry is challenged by sustainability and technical concerns. Sustainability includes minimization of raw material usage, energy consumption, and emission of greenhouse gases, while technical concerns comprise the enhancement of mechanical properties and durability such as compressive strength, resistance to chloride, acids, and elevated temperatures. Therefore, recycling of industrial

Purpose: Utilization of sustainable materials is a global demand in the construction industry. Hence, this study aims to integrate waste management and artificial intelligence by developing an artificial neural network (ANN) model to predict the compressive strength of green concrete. The proposed model allows the use of recycled coarse aggregate (RCA), recycled fine aggregate (RFA) and fly ash