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The COVID-19 pandemic caused significant societal changes and altered how much of the construction industry operates. This study investigates the impacts of pandemic-related changes, how these changes may apply to different companies, and which changes should continue post-pandemic.

We aim to identify pandemic-driven changes that have affected the construction workplace and the advantages and challenges associated with them. We then make recommendations for what could and should endure through the pandemic and beyond, and under what circumstances. To achieve this objective, we conducted both qualitative and quantitative analyses of 40 semi-structured interviews with US-based construction professionals.

Identified through these interviews were 21 pandemic-driven changes across six categories: management and planning, technology, workforce, health and safety, supply chain, and contracts. This study noted both positive and negative impacts of the changes on cost, schedule, productivity, collaboration, employee retention, flexibility, quality, and risk mitigation. Participants indicated that some changes should remain after the pandemic and others (e.g. select safety measures, schedule adjustments) should be temporary.

By incorporating these lessons learned into recommendations, the findings of this study will help businesses identify and implement the most appropriate improvements for their organizations. The findings also provide policymakers with valuable insights on how to promote innovation in the construction industry and potentially enact more effective policies during crises to drive long-term improvements.

The purpose of the study is to classify the radiographic images into three categories such as fillings, cavity and implant to identify dental diseases because dental disease is a very common dental health problem for all people. The detection of dental issues and the selection of the most suitable method of treatment are both determined by the results of a radiological examination. Dental x-rays provide important information about the insides of teeth and their surrounding cells, which helps dentists detect dental issues that are not immediately visible. The analysis of dental x-rays, which is typically done by dentists, is a time-consuming process that can become an error-prone technique due to the wide variations in the structure of teeth and the dentist's lack of expertise. The workload of a dental professional and the chance of misinterpretation can be decreased by the availability of such a system, which can interpret the result of an x-ray automatically.

This study uses deep learning (DL) models to identify dental diseases in order to tackle this issue. Four different DL models, such as ResNet-101, Xception, DenseNet-201 and EfficientNet-B0, were evaluated in order to determine which one would be the most useful for the detection of dental diseases (such as fillings, cavity and implant).

Loss and accuracy curves have been used to analyze the model. However, the EfficientNet-B0 model performed better compared to Xception, DenseNet-201 and ResNet-101. The accuracy, recall, F1-score and AUC values for this model were 98.91, 98.91, 98.74 and 99.98%, respectively. The accuracy rates for the Xception, ResNet-101 and DenseNet-201 are 96.74, 93.48 and 95.65%, respectively.

The present study can benefit dentists from using the DL model to more accurately diagnose dental problems.

This study is conducted to evaluate dental diseases using Convolutional neural network (CNN) techniques to assist dentists in selecting the most effective technique for a particular clinical condition.