To decrease the spreading of the virus during the pandemic, many managers and decision-makers decided to have laborers working remotely via different technology and some sites. On the other hand, governments issued new rules about the lockdown and working times to control the spread of the virus and reduce losses. On the other hand, Personal Protective Equipment (PPE) may be a profitable investment because all employees can work at the same time, lowering long-term operating costs and increasing the output rate ^[2] (Jain et al., 2020). On another side, other jobs during this period related to the new rules of the government’s designers worked remotely from home at different times to achieve their production rates before the virus. Different precautions have been taken for the construction workers, like social distancing and wearing the required PPE ^[3] (Bsisu, 2020). Others add a prioritizing of important works with the previous precautions and work was done to boost productivity. Construction companies also developed daily/weekly checklists to ensure adherence to the guidelines ^[4] (McGovern, 2020). Construction output in the UK dropped by 5.9% in March 2020, which was the worst drop since the financial crisis. However, infrastructure projects outside urban areas had a less significant drop in output and a faster return to work ^[5] (MACE, 2020). The housing projects were affected more than other sectors as most contractors and clients stopped work for at least a month, driven by a drop in demand for house sales from buyers who could no longer visit properties. Construction companies could survive the immediate impact of COVID-19 by relying on goverment assistance. In April 2020, the direct impact of the lockdown in the UK reduced output by up to 90% across construction and infrastructure projects, but the true challenge will be the long-term impact of reduced sustained production ^[5] (MACE, 2020). Another study by Alkhalouf (2020) ^[6] focused on the impact of COVID-19 on the materials supply chain and labor productivity in construction site building projects. The study utilised a real-life construction project at the peak of the pandemic in April 2020. The data collected includes interviews with project managers and progress meeting minutes. Results reveal that COVID-19 affected the progress of construction in two ways. The first way is a delay resulting from the shutdown of the manufacturing facilities and suppliers that state officials deemed nonessential. The second way is labor disruptions resulting from restrictions on gatherings and absenteeism of workers who were either sick with COVID-19 or who were avoiding construction sites and preferred to stay at home in response to state officials’ recommendations. The study suggested that contractors should request compensable time extensions as they establish the basis for a legitimate claim that will modify the contract time and total cost. In the UK, Working remotely is becoming more widespread in companies with higher educated and better-paid people. It is predicted to boost employee efficiency, increase output, decrease expenses, and shorten project completion times even after the pandemic ^[7] (Bartik et al., 2020). Productivity assessment of industries suitable for remote work shows fewer productivity losses ^[8] (Dingel and Neiman 2020). Jallow et al. (2020) ^[9] stated that improvements for the infrastructure sector in the UK had been taken to keep productivity while adhering to the COVID-19 lockout restrictions. According to the findings, the lockdown is a hard method to manage projects. However, technology techniques such as video chatting and online meetings are found to be effective in team communications. In major buildings and infrastructure projects, the cost of low productivity is very large. Management, technological, labor, and external factors were evaluated based on their impact on construction productivity. Planning and scheduling, material availability, and material storage are the top three factors affecting the construction production rate ^[10] (Dixit & Sharma, 2020). Bloom et al. (2020) ^[11] measured both labor productivity and total factor productivity (TFP) quarterly between the second quarter of 2020 to the second quarter of 2021 and provided medium-term projections for 2022. According to the findings, COVID-19 reduces TFP by up to 5% in the fourth quarter of 2020, dropping to 1% in the medium run. Because hours worked fall faster, the effects of COVID-19 on the hourly labor production rate are expected to be less negative than on TFP. The negative impacts of the COVID-19 crisis have increased significantly on the construction industry, and delivering building and construction services during these exceptional times has become a difficult challenge. It had an impact on the production rate of construction projects. As such, the main aim of the research ^[12] is to study the impact of COVID-19 on the production rate by developing an artificial neural networks (ANNs) model which predicts the most important and influential factors of the construction industry in Egypt, to evaluate the performance of this branch at the peak of the virus. Also, the developed ANN model can be used in similar crises as a lesson learned for the same crisis in the future, and no previous studies mentioned these points.

Artificial Neural Network (ANN) is a mathematical model that tries to simulate the structure and functionalities of biological neural networks. ANNs can solve complex real-life problems by processing information in their basic building blocks, artificial neurons, non-linear, distributed, parallel, and local. ANNs learn through experience rather than programming and by identifying patterns and relationships in data. Once the network is trained and tested, it can be given new input information to predict the output ^[13] (Agatonovic-Kustrin & Beresford, 2000). The basic ANN comprises input, output, and hidden layers. Each layer can have several nodes, and nodes from the input layer are connected to the nodes from the hidden layer. Also, nodes from the hidden layer are connected to the nodes from the output layer. Those connections represent weights between nodes ^[14], (Mirza Cilimkovic, 2015) ^[15]. The default steps to create a suitable ANN for the production rate in the construction sector are: first: Create the initial ANN structure (determine the activation function, number of hidden layers, and its neurons for each layer with the method of learning). Secondly, training this initial ANN by multi-iterations until the convergence between the Mean Absolute Percentage Error (MAPE) and the determination coefficient (R²) becomes stable. Finally, an optimum number of hidden layers and a number of neurons for each layer will be determined by using the trial-and-error method and the transfer function tool.