Abbas Rashidi

Field Evaluation of N95 Filtering Facepiece Respirators on Construction Jobsites for Protection against Airborne Ultrafine Particles

Exposure to high concentrations of airborne ultrafine particles in construction jobsites may play an important role in the adverse health effects among construction workers, therefore adequate respiratory protection is required. The performance of particulate respirators has never been evaluated in field conditions against ultrafine particles on construction jobsites. In this study, respiratory protection levels against ultrafine particles of different size ranges were assessed during three common construction related jobs using a manikin-based set-up at 85 L/min air flow rate. Two NanoScan SMPS nanoparticle counters were utilized for measuring ultrafine particles in two sampling lines of the test filtering facepiece respirator—one from inside the respirator and one from outside the respirator. Particle size distributions were characterized using the NanoScan data collected from outside of the respirator. Two models of N95 respirators were tested—foldable and pleated. Collected data indicate that penetration of all categories of ultrafine particles can exceed 5% and smaller ultrafine particles of <36.5 nm size generally penetrated least. Foldable N95 filtering facepiece respirators were found to be less efficient than pleated N95 respirators in filtering nanoparticles mostly at the soil moving site and the wooden building frameworks construction site. Upon charge neutralization by isopropanol treatment, the ultrafine particles of larger sizes penetrated more compared to particles of smaller sizes. Our findings, therefore, indicate that N95 filtering facepiece respirators may not provide desirable 95% protection for most categories of ultrafine particles and generally, 95% protection is achievable for smaller particles of 11.5 to 20.5 nm sizes. We also conclude that foldable N95 respirators are less efficient than pleated N95 respirators in filtering ultrafine particles, mostly in the soil moving site and the wooden building framework construction site.

A Productivity Forecasting System for Construction Cyclic Operations Using Audio Signals and a Bayesian Approach

A large portion of the expenses in a construction project are allocated towards the capital and operating costs of heavy equipment. Most of construction heavy equipment and tools carry out activities in the form of repetitive cycles (e.g., a cycle of digging, swinging, loading). Precisely estimating cycle times for those operations is a crucial step toward productivity analysis, cost estimation, and scheduling of a construction project. The traditional approaches for estimating cycle times of construction cyclic activities are twofold: 1) based on direct observations and recordings; 2) using available graphs and approximate formulas for estimations. The first approach is time consuming and labor intensive and the second one might not be sufficiently accurate and realistic. To tackle the above-mentioned issues, this paper proposes an automated, Bayesian system for estimating cycle times of construction heavy equipment. Considering that construction equipment usually produces distinct acoustic patterns while performing various tasks, the main input for the system is recorded audio data. The presented system includes a de-noising algorithm for enhancing the quality of audio data as well as a Short-Time Fourier Transform (STFT) and Support Vector Machines (SVM) for classifying various activities in a primary stage. A Markov chain model for activity transitions is calculated from ground truth data and used to code an adaptive filter that converts SVM-labeled time-frequency bins into higher-level labels of the full period for each activity. Preliminary results show that, through this system, the accuracy of predicting cycle times could be as high as 90%.

Using Unmanned Aerial Systems for Automated Fall Hazard Monitoring

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Achievements and challenges in audio-based modeling of construction job sites

Construction job-sites are noisy workplaces and construction equipment and machines create discrete sound patterns while performing their daily operations. Construction engineers usually considered job site noise as a negative phenomenon, but if processed properly, the generated sound patterns could be used as a rich source of information for analyzing ongoing operations at job-sites. This paper presents the current research efforts of the authors regarding initiating and developing an audio-based model for analysis and modeling of construction operations. The audio-based model is based on placing single or multiple microphones at the jobsite, recording the developed sounds patterns, and using various techniques for processing the recorded audio files and detecting and recognizing different operations taking place at the jobsite. The implemented techniques include noise removal and signal enhancement, source separation, signal processing and machine learning algorithms. The paper also discusses about the ...