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This paper aims to propose a new deep learning technique to detect the type of material to improve automated construction quality monitoring.

A new data augmentation approach that has improved the model robustness against different illumination conditions and overfitting is proposed. This study uses data augmentation at test time and adds outlier samples to training set to prevent over-fitted network training. For data augmentation at test time, five segments are extracted from each sample image and fed to the network. For these images, the network outputting average values is used as the final prediction. Then, the proposed approach is evaluated on multiple deep networks used as material classifiers. The fully connected layers are removed from the end of the networks, and only convolutional layers are retained.

The proposed method is evaluated on recognizing 11 types of building materials which include 1,231 images taken from several construction sites. Each image resolution is 4,000 × 3,000. The images are captured with different illumination and camera positions. Different illumination conditions lead to trained networks that are more robust against various environmental conditions. Using VGG16 model, an accuracy of 97.35% is achieved outperforming existing approaches.

It is believed that the proposed method presents a new and robust tool for detecting and classifying different material types. The automated detection of material will aid to monitor the quality and see whether the right type of material has been used in the project based on contract specifications. In addition, the proposed model can be used as a guideline for performing quality control (QC) in construction projects based on project quality plan. It can also be used as an input for automated progress monitoring because the material type detection will provide a critical input for object detection.

Several studies have been conducted to perform quality management, but there are some issues that need to be addressed. In most previous studies, a very limited number of material types were examined. In addition, although some studies have reported high accuracy to detect material types (Bunrit et al., 2020), their accuracy is dramatically reduced when they are used to detect materials with similar texture and color. In this research, the authors propose a new method to solve the mentioned shortcomings.

This study aims to investigate the relationship between emergency response times and crash severity in New York City, focusing on spatial disparities and their implications. It examines how these disparities impact disadvantaged neighborhoods, particularly regarding traffic safety and emergency service accessibility.

The research uses comprehensive spatial analysis techniques, including hotspot mapping, network analysis for travel time modeling, local bivariate correlation analysis and service area calculations. It maps crash data alongside emergency facility locations, considering peak-hour traffic. The Area Deprivation Index (ADI) is integrated to evaluate socioeconomic factors influencing accessibility. This approach provides a nuanced understanding of how emergency response times correlate with crash severity at the census block level, accounting for socioeconomic disparities.

This study reveals significant disparities in emergency response times across New York City. In high-poverty, predominantly minority areas, response times are 2–3 min longer than average, correlating with a 15% increase in severe injury rates. Over 20% of neighborhoods show correlations between response times and crash severity, with positive linear (5.51%), negative linear (10.72%), concave (2.44%) and convex (2.80%) relationships. Blocks with positive linear relationships have a mean ADI rank of 3.918. During peak hours, 69.7% of Manhattan blocks show negative relationships, the highest among boroughs.

This research highlights spatial justice issues in urban emergency response systems, emphasizing the need for localized, data-driven planning and infrastructure adjustments. By integrating the ADI, the multifaceted approach reveals the complex dynamics of socioeconomic factors and emergency service accessibility that have not yet been investigated in diverse urban communities.