Sensor-Based Safety Performance Assessment of Individual Construction Workers

Over the last decade, researchers have explored various technologies and methodologies to enhance worker safety at construction sites. The use of advanced sensing technologies mainly has focused on detecting and warning about safety issues by directly relying on the detection capabilities of these technologies. Until now, very little research has explored methods to quantitatively assess individual workers’ safety performance. For this, this study uses a tracking system to collect and use individuals’ location data in the proposed safety framework. A computational and analytical procedure/model was developed to quantify the safety performance of individual workers beyond detection and warning. The framework defines parameters for zone-based safety risks and establishes a zone-based safety risk model to quantify potential risks to workers. To demonstrate the model of safety analysis, the study conducted field tests at different construction sites, using various interaction scenarios. Probabilistic evaluation showed a slight underestimation and overestimation in certain cases; however, the model represented the overall safety performance of a subject quite well. Test results showed clear evidence of the model’s ability to capture safety conditions of workers in pre-identified hazard zones. The developed approach presents a way to provide visualized and quantified information as a form of safety index, which has not been available in the industry. In addition, such an automated method may present a suitable safety monitoring method that can eliminate human deployment that is expensive, error-prone, and time-consuming

Application of As-built Data in Building Retrofit Decision Making Process

AbstractWith the growing needs of improving building sustainability, an increasing number of existing buildings need renovation to meet the expectation of the stakeholders. In the pre-design phase, it is very critical to have the best decision made to satisfy both the project budget and the performance standard. For a new buildings, a whole building energy simulation analysis is very helpful for this decision making process because it can provide the stakeholders the evaluation results of all alternative solutions. However, for existing buildings, the as-built data required for the building energy modeling process is not always available, and its manual collection process is time-consuming and error prone. This paper first reviews the state-of-the-art methods of automated data collection, and then introduces the automatic as-built BIM model creation process through a case study. This study also successfully demonstrated the interoperability between the created as-built model and a typical energy simulation tool. At last, a discussion is made about the limitations and challenges of the current state of practice to enlighten the future direction

Automatic 3D Thermal Zones Creation for Building Energy Simulation of Existing Residential Buildings

ABSTRACT Existing buildings now represent the greatest opportunity to improve building energy performance. Building energy simulation is becoming increasingly important because the simulation results can assist the decision makers to quickly make the solution for improving building energy efficiency and reducing environmental impacts. However, most of the time there is no available as-is 3D building models for the existing buildings to conduct energy simulation. Nowadays, it is a common practice to obtain point clouds of existing buildings through using 3D laser scanning technologies for as-is building modeling. Current methods using point clouds need manual processes to prepare thermal zones based on point clouds data which is very time consuming and labor intensive. This paper introduces an automated thermal zone creation method to create both a building zone and room zones. A building thermal envelope was extracted from 3D point clouds. Then, 2D building floor plans were used as references to accurately determine the location and size of each thermal zone. A preliminary experiment has been conducted on a residential house to validate the proposed method, and the created building components and thermal zones were successfully imported into a building energy simulation program and ready for various energy analyses. INTRODUCTION Improving Energy efficiency has been a popular subject for the whole world since the energy crisis in the late 1970's (Maldague 2001). Buildings account for 16 percent of world energy consumption (EIA 2009), with a higher share in developed economies (nearly 42 percent of total energy use in the United States) (DOE 2010). Each year, roughly four percent of the building is newly constructed or renovated, thus the existing building stock will have the majority of opportunities to improve energy efficiency over the next several decades. Thus, existing buildings represent the greatest opportunity to improve building energy efficiency and reduce environmental impacts. The public and private building stocks are increasingly mandated to meet green standards by the federal government and jurisdictions across the country. The fact that people gradually consider saving energy and lowering greenhouse gas emissions as civil virtues boosts the private demand for green building

Meta-Learning for Low-Resource Neural Machine Translation

In this paper, we propose to extend the recently introduced model-agnostic meta-learning algorithm (MAML) for low-resource neural machine translation (NMT). We frame low-resource translation as a meta-learning problem, and we learn to adapt to low-resource languages based on multilingual high-resource language tasks. We use the universal lexical representation~\citep{gu2018universal} to overcome the input-output mismatch across different languages. We evaluate the proposed meta-learning strategy using eighteen European languages (Bg, Cs, Da, De, El, Es, Et, Fr, Hu, It, Lt, Nl, Pl, Pt, Sk, Sl, Sv and Ru) as source tasks and five diverse languages (Ro, Lv, Fi, Tr and Ko) as target tasks. We show that the proposed approach significantly outperforms the multilingual, transfer learning based approach~\citep{zoph2016transfer} and enables us to train a competitive NMT system with only a fraction of training examples. For instance, the proposed approach can achieve as high as 22.04 BLEU on Romanian-English WMT'16 by seeing only 16,000 translated words (~600 parallel sentences).Comment: Accepted as a full paper at EMNLP 201

Quantum dot-doped porous silicon metal–semiconductor metal photodetector

In this paper, we report on the enhancement of spectral photoresponsivity of porous silicon metal–semiconductor metal (PS-MSM) photodetector embedded with colloidal quantum dots (QDs) inside the pore layer. The detection efficiency of QDs/PS hybrid-MSM photodetector was enhanced by five times larger than that of the undoped PS-MSM photodetector. The bandgap alignment between PS (approximately 1.77 eV) and QDs (approximately 1.91 eV) facilitates the photoinduced electron transfer from QDs to PS whereby enhancing the photoresponsivity. We also showed that the photoresponsitivity of QD/PS hybrid-MSM photodetector depends on the number of layer coatings of QDs and the pore sizes of PS.Published versio