Construction Automation & Robotics
Construction Safety and Health
Building Energy Efficiency
1. Effects of exoskeleton use on movement kinematics during performance of common work tasks
An exoskeleton may assist performance of basic work-related tasks. Its application should not alter user kinematics, which compromise user safety. EXO augmentation does not need to alter movement kinematics during performances of kneel, lift, and climb tasks. EXO kinematic alterations did not appear to compromise user safety in terms of lateral trunk bending. It may encourage good technique, such as greater foot clearance to avoid tripping, for some tasks.
1. Innovative Visualization-Enhanced Training Method for Occupational Hazardous Noise Awareness Enhancement
For years, occupational safety and health has been a popular research topic in the construction engineering and management field. However, most of the research have mainly focused on the safety hazards, especially the “Fatal Four” - falls, followed by struck by object, electrocution, and caught-in/between. Although health hazards usually do not cause immediate fatalities, without sufficient awareness and timely care they will gradually develop into injuries or disabilities. Workplace noise hazard is one among many that are exposed to construction workers in their daily jobs. In the United States, 22 million workers are exposed to hazardous noise each year and about 4.5 million construction workers were exposed to dangerous workplace noise exposure. Occupational noise exposure from workplace not only affects workers’ quality of life, but also brings a great economic loss. An estimated 242 million dollars is spent annually on workers’ compensation for hearing loss disability. Current noise hazard training is usually briefed during the employee orientation and daily safety meeting in the format of verbal or written instructions, which is not an effective method for raising workers’ safety awareness concerning the hazardous occupational noise and proper use of hearing protection devices. The main objective of this research is to design, develop, and validate a visualization-enhanced training method to predict and visualize the potential noise hazards in a building information model (BIM) integrated environment for effective daily safety training to make sure the workers have sufficient knowledge and awareness of their working environment before starting.
2. Wearable Construction Training Assistance System for Self-Performance Evaluation and Correction
Skilled craft worker shortage has been a serious concern for construction industry. Prevention through training and education to improve workers’ craft skills could overcome the labor market shortage; however, preparing and delivering training courses, generally underpinned by in-person training, is time-consuming and requires relevant experience. The objective of this study is to develop a wearable construction training assistance system for self-performance evaluation and correction. This research used earplugs fitting training as a case study. Currently, most construction workers failed to recognize and follow rigorous procedures of wearing hearing protection devices with hands in a noisy workplace, which would further challenge the workforce health conditions. The development of wearable and non-invasive electromyography (EMG) based armband system introduces an opportunity to measure forearm muscle movements to recognize human activity patterns. The proposed system first standardized training procedures presented through step-by-step video instruction of inserting earplugs, and then enabled muscle movement recognition using EMG-based body sensors network and driven by machine learning analysis for self-performance evaluation and correction. The obtained knowledge will enhance skilled workforce training to meet the need of labor market.
2. Application of Exoskeleton in Construction Activities: Study of Muscle Load Transference Effects
Work-related musculoskeletal disorders (WMSDs) are most common risks for construction workers. They are the primary source of non-fatal injuries caused by sudden or continuous stress on worker’s musculoskeletal system (muscles, tendons, nerves, bones, and ligaments). WMSDs can weaken the ability of the worker to perform the task or cause permanent disability which results in cost burdens to the employer. The main factors causing WMSDs are overexertion, awkward body posture, repetitive motion, vibration and contact force. To overcome the problem of WMSDs, researchers have studied the application of wearable exoskeleton to reduce the mechanical stress on workers. Although bearing advantages of productivity improvement and WMSDs risk reduction, exoskeleton still possibly introduce the risk of transference of load between different musculoskeletal regions. The objective of this paper is to better understand and quantify the transference of load between musculoskeletal regions. A case study was conducted on the manual material handling activity in which the worker is wearing IMUs and elbow exoskeleton. The stresses on various body parts are calculated using the joint angle information obtained from IMUs. The preliminary results show that the load transference exists due to change in body posture which resulted due to the additional strength obtained from exoskeleton. This proposed study can be extended to other body parts to help design exoskeleton for specific construction tasks.
1.Non-Invasive Automated Thermal Building Model Creation
Building information models (BIMs) are increasingly being applied throughout a building’s lifecycle for various applications, such as progressive construction monitoring and defect detection, building renovation, energy simulation, and building system analysis in the Architectural, Engineering, Construction, and Facility Management (AEC/FM) domains. In conventional approaches, as-is BIM is primarily manually created from point clouds, which is labor-intensive, costly, and time consuming. This paper proposes a method for automatically extracting building geometries from unorganized point clouds. The collected raw data undergo data downsizing, boundary detection, and building components categorization, resulting in the building components being recognized as individual objects and their visualization as polygons. The results of tests conducted on three collected as-is building data to validate the technical feasibility and evaluate the performance of the proposed method indicate that it can simplify and accelerate the as-is building model from the point clouds creation process.
2. Energy and Cost Saving Assessments for Small and Medium-sized US Manufacturers
The USDOE Industrial Assessment Centers (IAC) are teams of university-based faculty and student engineers that provide no-cost energy, productivity, and waste assessments to small and medium sized US manufacturers nationwide. After the site visit, a comprehensive report is developed the provides specific details on all cost-saving opportunities identified during the assessment, including applicable rebates and incentives. LSU-IAC is one of the 28 IAC in the US, and was awarded in September 2016. Dr. Chao Wang manages the center as an Assistant Director, during the assessment he mainly charges in boiler assessment, building envelope tightness assessment, lighting assessment, building occupancy modeling, and building energy simulation.
3. Evaluation of Heavy Equipment Operator Training by Monitoring Mental Workload with EEG
Performance of heavy equipment operator training plays an important role in operator’s safety, understanding of machine controls, and professional skills development. However, few studies were performed previously on the quantitative evaluation of the impact of trainee’s mental workload on training performance, although empirical research suggests that there is a causal and logical relationship connecting mental workload during training with performance. Thus, a study is necessary to quantitatively evaluate current heavy equipment operator training programs by monitoring mental workload. The main objective of this research study is to collect data which may help to validate the feasibility of measuring mental workload to evaluate the heavy equipment operator training with electroencephalography (EEG). EEG device allows researchers to quantitively measure the electrical activities of test subject’s brain activities. EEG signals can accurately reflect precise changes of test subject’s mental workload. In this experiment study, a heavy equipment operator training program containing both virtual and real training sections provided by Louisiana CAT will be investigated; approximately 20 students are expected to be enrolled in this experiment study; the enrolled students’ performance will be monitored; and a non-invasive, dry, and wireless EEG headset named as MindWave developed by Nueurosky will be used to monitor mental workload. The expected outcome is to find evidence of quantitative correlation between mental workload and the training performance. The findings of this experiment will feed into our self-report collected data afterwards.