The Egyptian construction industry suffers from severe delays and cost overruns. Rework is a prime cause of this challenge. Therefore, the objective of this study to identify the root causes of rework for Egyptian construction projects and their impact on the project duration and cost. An extensive literature review has been done to identify the most common factors affecting rework. Hence, eighty-seven factors were identified and categorised into ten groups. Structured interview technique was adopted to get the opinions of 67 Egyptian construct professionals for nineteen different construction projects. Total Importance Index of Rework Impact (T.I.I.R.I%) was used to analyse and rank rework factors. Accordingly, the most critical five rework factors are economic situation for the country, schedule compression, design changes, specifications change, and inadequate feasibility study. Based on the survey results, the project stakeholders should consider the critical rework factors during the planning stage and before the project execution, to eliminate or reduce the occurrence and impact of rework causes and to improve the performance of the projects. This paper provides state of the art for rework causes and their effects on construction projects performance, and it can be beneficial for both practitioners and researchers for future work.

Undoubtedly, most industrial buildings have a huge Life Cycle Cost (LCC) throughout their lifespan, and most of these costs occur in structural operation and maintenance costs, environmental impact costs, etc. Hence, it is necessary to think about a fast way to determine the LCC values. Therefore, this article presents an LCC deep learning prediction model to assess structural and envelope-type alternatives for industrial building, and to make a decision for the most suitable structure. The input and output criteria of the prediction model were collected from previous studies. The deep learning network model was developed using a Deep Belief Network (DBN) with Restricted Boltzmann Machine (RBM) hidden layers. Seven investigation cases were studied to validate the prediction model of a 312-item dataset over a period of 30 years, after the training phase of the network to take the suitable hidden layers of the RBM and hidden neurons in each hidden layer that achieved the minimal errors of the model. Another case was studied in the model to compare design structure alternatives, consisting of three main structure frames—a reinforced concrete frame, a precast/pre-stressed concrete frame, and a steel frame—over their life cycle, and make a decision. Precast/pre-stressed concrete frames were the best decision until the end of the life cycle cost, as it is possible to reuse the removed sections in a new industrial building.

The construction industry is subjected to more risk and uncertainty than many other industries, in addition to poor risk-tracking records. This usually causes the late completion of projects, surpassing their estimated budgets even without achieving the desired quality and operational requirements. This study first identified the most critical factors affecting the accuracy of cost estimates. A questionnaire has been designed and distributed to construction experts; the results indicated stakeholders’ perception to improve their cost estimation using a proper and predefined risk management plan. Following the respondents’ judgment, this study identified 51 risk factors and their potential root causes and presents a response plan for the most critical risks that might affect cost estimates to improve construction-industry performance. Then, a new simple model has been developed to analyze risk factors affecting the accuracy of conceptual cost estimates, from both clients’ and contractors’ perspectives. The developed model will provide the decision-makers with a list of risks accompanied by guidelines/response plans to mitigate their effect on project cost to help the estimators identify the most effective cost contingency against the project’s scope creep.

Construction projects in developing countries face challenges related to inefficient practices and high levels of waste, leading to cost overruns and delays. Lean Construction (LC) principles are an approach to minimize waste and improve efficiency in construction projects. However, the impact of adopting LC on waste reduction in developing country contexts remains a subject that requires investigation. The current case study aimed to examine how implementing LC. principles can reduce waste in building projects in developing nations. The research utilizes a mixed-method approach; Qualitative interviews are conducted with stakeholders, including project managers, contractors, and architects, to gather their perceptions of the most common waste type in the construction process and how the lean principles implementation minimizes waste. Quantitative approach: a case study was conducted using a lean simulation model by ARENA software, to measure the impact of LC. principles on the overall performance of the construction (reinforcement process). Our Results showed that the lean model enhances the process efficiency by eliminating non-value-added activities, standardizing work procedures, and fostering continuous improvement. These practices lead to a reduction in wasted resources, such as the reinforcement process (improvement in overall cutting process by 67.1% and bending process by 21%) and enhance work productivity by 19.6%, and thus refinement in the overall time cycle by 24.5%. In summary, by adopting lean principles, businesses can create a more sustainable and efficient future while contributing to preserving natural resources and focusing on value-added processes.