scholarly journals Research Trends on Machine Learning in Construction Management: A Scientometric Analysis

2021 ◽  
Vol 2 (03) ◽  
pp. 96-104
Author(s):  
Tam Nguyen Van ◽  
Toan Nguyen Quoc
Keyword(s):  
Machine Learning ◽  
Cost Estimation ◽  
Construction Management ◽  
Energy Demand ◽  
Regional Analysis ◽  
The United States ◽  
Research Trends ◽  
Machine Learning Techniques ◽  
Scientometric Analysis ◽  
Management Domain

Machine learning plays a vital role in construction industry which could make improve project’s safety, productivity, and quality. Many studies have attempted to explore the potential opportunities to adopt this technology in different aspects of the construction sector. However, no comprehensive study to review the global research trends on this technological advancement in construction management domain. The goal is to investigate and summarize the state-of-the-art knowledge body in this topic in a systematic manner. To achieve this, this paper considered 161 studies on machine learning in construction management related to bibliographic records retrieved from the Scopus database by adopting scientometric analysis approach. This paper found that since 2014, there has been a considerable increase in the number of publications on this domain. Researchers from the United States, China, and Australia have been the main contributors to this research area through regional analysis. This study also revealed that approximately 34% of all countries in the world are engaged in this domain research. In addition, five main aspects in construction management have been applied machine learning techniques, namely, assess and reduce risk, safety management for construction sites, cost estimation and prediction, Schedule management, and building energy demand prediction. Furthermore, three potential construction management research areas that can apply this technology were proposed for further studies. The findings will help both professionals and researchers more understanding how machine learning knowledge is evolving and its role played in the construction management domain, and this study thus offers a useful reference point to how can develop this area in the future.

Development of a method for in vivo mechanical characterization of articular cartilage

2017 ◽  
Author(s):  
◽  
Joe Rexwinkle
Keyword(s):  
Machine Learning ◽  
Hospital Setting ◽  
The United States ◽  
Machine Learning Techniques ◽  
Mechanical State ◽  
University Of Missouri ◽  
Orthopaedic Research ◽  
Early Results ◽  
Number Of Patients

[ACCESS RESTRICTED TO THE UNIVERSITY OF MISSOURI AT AUTHOR'S REQUEST.] Arthritis is one of the leading causes of disability in the United States and the second most expensive to treat according to the CDC. One of the key difficulties in diagnosing and treating arthritis, in particular osteoarthritis, is that the mechanisms for progression of the disease are poorly characterized. Mechanical engineer Joe Rexwinkle, working with Dr. Ferris Pfeiffer and the Thompson Lab for Regenerative Orthopaedics, aimed to shed some light on the links between cartilage biology and the degradation seen in osteoarthritis. The study began with obtaining cartilage samples from six patients undergoing total knee replacements and collecting information on several biomarkers with known relevance to osteoarthritis. Specifically, the concentrations of several proteins which may be determined in a standard hospital lab were analyzed. The samples were then tested to determine their mechanical properties, since the progression of osteoarthritis is always accompanied by the physical degradation of the tissue. Machine learning techniques, which are gaining increasing popularity in the field of orthopaedic research, were then used to model the relationships between these biomarkers and the mechanical state of the tissue. These models were found to be highly accurate in characterizing the mechanical state of the tissue, even when limited only to the protein concentrations that one could find in a standard hospital lab. This study has not yet produced a tool which may be used in a hospital setting, considering the low number of patients included in this study, but it does reveal promising early results in using machine learning to characterize osteoarthritis, a task which has thus far eluded the orthopaedic research community.

Primary Factors Influencing the Decision to Vaccinate Against COVID-19 in the United States: A Predictive Analytics Approach (Preprint)

2021 ◽  
Author(s):  
Serkan Varol ◽  
Serkan Catma ◽  
Diana Reindl ◽  
Elizabeth Serieux
Keyword(s):  
Machine Learning ◽  
Decision Making ◽  
Predictive Analytics ◽  
The United States ◽  
Machine Learning Techniques ◽  
Vaccine Hesitancy ◽  
Willingness To Accept ◽  
Perceptions And Attitudes ◽  
Learning Techniques ◽  
Vaccine Decision Making

BACKGROUND Vaccine refusal still poses a risk to reaching herd immunity in the United States. The existing literature focuses on identifying the predictors that would impact the willingness to accept (WTA) vaccines using survey data. These variables range from the socio-demographic characteristics of the participants to the perceptions and attitudes towards the vaccines so each variable’s statistical relationship with the WTA a vaccine can be investigated. However, while the results of these studies may have important implications for understanding vaccine hesitancy by offering interpretation of the statistical relationships, the prediction of vaccine decision-making has rarely been investigated OBJECTIVE We aimed to identify the factors that contribute to the prediction of COVID-19 vaccine acceptors and refusers using machine learning METHODS A nationwide survey was administered online in November, 2020 to assess American public perceptions and attitudes towards COVID-19 vaccines. Seven machine learning techniques were utilized to identify the model with the highest predictive power. Moreover, a set of variables that would contribute the most to the predictions of vaccine acceptors and refusers was identified using Gini importance based on Random Forest structure RESULTS The resulting machine learning algorithm has better prediction ability for willingness to accept (82%) versus reject (51%) a COVID-19 vaccine. In terms of predictive success, the Random Forest model outperformed the other machine learning techniques with a 69.52% accuracy rate. Worrying about (re) contracting Covid 19 and opinions regarding mandatory face covering were identified as the most important predictors of vaccine decision-making CONCLUSIONS The complexity of vaccine hesitancy needs to be investigated thoroughly before the threshold needed to reach population immunity can be achieved. Predictive analytics can help the public health officials design and deliver individually tailored vaccination programs that would increase the overall vaccine uptake.

Machine Learning Techniques to Identify and Characterize Sleep Disorders Using Biosignals

2021 ◽  
pp. 136-160
Author(s):  
Mercedes Barrachina ◽  
Laura Valenzuela López
Keyword(s):  
Machine Learning ◽  
Sleep Disorders ◽  
Adult Population ◽  
The United States ◽  
Machine Learning Techniques ◽  
Future Research ◽  
Research Directions ◽  
Learning Techniques ◽  
Control And Prevention ◽  
Future Research Directions

Sleep disorders are related to many different diseases, and they could have a significant impact in patients' health, causing an economic impact to the society and to the national health systems. In the United States, according to information from the Center for Disease Control and Prevention, those disorders are affecting 50-70 million in the adult population. Sleep disorders are causing annually around 40,000 deaths due to cardiovascular problems, and they cost the health system more than 16 billion. In other countries, such as in Spain, those disorders affect up to 48% of the adult population. The main objective of this chapter is to review and evaluate the different machine learning techniques utilized by researchers and medical professionals to identify, assess, and characterize sleep disorders. Moreover, some future research directions are proposed considering the evaluated area.

scholarly journals Tree‐Based Machine Learning to Identify and Understand Major Determinants for Stroke at the Neighborhood Level

2020 ◽  
Vol 9 (22) ◽  
Author(s):  
Liangyuan Hu ◽  
Bian Liu ◽  
Jiayi Ji ◽  
Yan Li
Keyword(s):  
Physical Activity ◽  
Machine Learning ◽  
Cardiovascular Disease ◽  
Cardiovascular Health ◽  
Black People ◽  
The United States ◽  
Machine Learning Techniques ◽  
Data Set ◽  
Wide Range ◽  
Neighborhood Level

Background Stroke is a major cardiovascular disease that causes significant health and economic burden in the United States. Neighborhood community‐based interventions have been shown to be both effective and cost‐effective in preventing cardiovascular disease. There is a dearth of robust studies identifying the key determinants of cardiovascular disease and the underlying effect mechanisms at the neighborhood level. We aim to contribute to the evidence base for neighborhood cardiovascular health research. Methods and Results We created a new neighborhood health data set at the census tract level by integrating 4 types of potential predictors, including unhealthy behaviors, prevention measures, sociodemographic factors, and environmental measures from multiple data sources. We used 4 tree‐based machine learning techniques to identify the most critical neighborhood‐level factors in predicting the neighborhood‐level prevalence of stroke, and compared their predictive performance for variable selection. We further quantified the effects of the identified determinants on stroke prevalence using a Bayesian linear regression model. Of the 5 most important predictors identified by our method, higher prevalence of low physical activity, larger share of older adults, higher percentage of non‐Hispanic Black people, and higher ozone levels were associated with higher prevalence of stroke at the neighborhood level. Higher median household income was linked to lower prevalence. The most important interaction term showed an exacerbated adverse effect of aging and low physical activity on the neighborhood‐level prevalence of stroke. Conclusions Tree‐based machine learning provides insights into underlying drivers of neighborhood cardiovascular health by discovering the most important determinants from a wide range of factors in an agnostic, data‐driven, and reproducible way. The identified major determinants and the interactive mechanism can be used to prioritize and allocate resources to optimize community‐level interventions for stroke prevention.

scholarly journals Machine learning analysis of lifeguard flag decisions and recorded rescues

2019 ◽  
Vol 19 (11) ◽  
pp. 2541-2549
Author(s):  
Chris Houser ◽  
Jacob Lehner ◽  
Nathan Cherry ◽  
Phil Wernette
Keyword(s):  
Machine Learning ◽  
The United States ◽  
Machine Learning Techniques ◽  
Public Health Issue ◽  
Rip Currents ◽  
Effective Strategies ◽  
Wave Forcing ◽  
Learning Techniques ◽  
Yellow Flag ◽  
The Impact

Abstract. Rip currents and other surf hazards are an emerging public health issue globally. Lifeguards, warning flags, and signs are important, and to varying degrees they are effective strategies to minimize risk to beach users. In the United States and other jurisdictions around the world, lifeguards use coloured flags (green, yellow, and red) to indicate whether the danger posed by the surf and rip hazard is low, moderate, or high respectively. The choice of flag depends on the lifeguard(s) monitoring the changing surf conditions along the beach and over the course of the day using both regional surf forecasts and careful observation. There is a potential that the chosen flag is not consistent with the beach user perception of the risk, which may increase the potential for rescues or drownings. In this study, machine learning is used to determine the potential for error in the flags used at Pensacola Beach and the impact of that error on the number of rescues. Results of a decision tree analysis indicate that the colour flag chosen by the lifeguards was different from what the model predicted for 35 % of days between 2004 and 2008 (n=396/1125). Days when there is a difference between the predicted and posted flag colour represent only 17 % of all rescue days, but those days are associated with ∼60 % of all rescues between 2004 and 2008. Further analysis reveals that the largest number of rescue days and total number of rescues are associated with days where the flag deployed over-estimated the surf and hazard risk, such as a red or yellow flag flying when the model predicted a green flag would be more appropriate based on the wind and wave forcing alone. While it is possible that the lifeguards were overly cautious, it is argued that they most likely identified a rip forced by a transverse-bar and rip morphology common at the study site. Regardless, the results suggest that beach users may be discounting lifeguard warnings if the flag colour is not consistent with how they perceive the surf hazard or the regional forecast. Results suggest that machine learning techniques have the potential to support lifeguards and thereby reduce the number of rescues and drownings.

scholarly journals Machine Learning for Benchmarking Models of Heating Energy Demand of Houses in Northern Canada

Energies ◽  
2020 ◽  
Vol 13 (5) ◽  
pp. 1158
Author(s):  
Behrad Bezyan ◽  
Radu Zmeureanu
Keyword(s):  
Machine Learning ◽  
Energy Demand ◽  
Energy Use ◽  
Machine Learning Techniques ◽  
Northern Canada ◽  
Automation And Control ◽  
And Performance ◽  
Energy Signature ◽  
Heating Energy Demand ◽  
And Control

In most cases, the benchmarking models of energy use in houses are developed based on current and past data, and they continue to be used without any update. This paper proposes the method of retraining of benchmarking models by applying machine learning techniques when new measurements are made available. The method uses as a case study the measurements of heating energy demand from two semi-detached houses of Northern Canada. The results of the prediction of heating energy demand using static or augmented window techniques are compared with measurements. The daily energy signature is used as a benchmarking model due to its simplicity and performance. However, the proposed retraining method can be applied to any form of benchmarking model. The method should be applied in all possible situations, and be an integral part of intelligent building automation and control systems (BACS) for the ongoing commissioning for building energy-related applications.

scholarly journals National Scientific Funding for Interdisciplinary Research: A Comparison Study of Infectious Diseases in the US and EU

2019 ◽  
Vol 11 (15) ◽  
pp. 4120 ◽  
Author(s):  
Heo ◽  
Kang ◽  
Kim
Keyword(s):  
Infectious Diseases ◽  
Interdisciplinary Approach ◽  
The United States ◽  
Research Trends ◽  
Scientometric Analysis ◽  
The European Union ◽  
Emerging Pathogens ◽  
Network Analyses ◽  
The Us ◽  
Project Data

Infectious diseases have been continuously and increasingly threatening human health and welfare due to a variety of factors such as globalisation, environmental, demographic changes, and emerging pathogens. In order to establish an interdisciplinary approach for coordinating R&D via funding, it is imperative to discover research trends in the field. In this paper, we apply machine learning methodologies and network analyses to understand how the European Union (EU) and the United States (US) have invested their funding in infectious diseases research utilising an interdisciplinary approach. The purpose of this paper is to use public R&D project data as data and to grasp the research trends of epidemic diseases in the US and EU through scientometric analysis.

scholarly journals Towards an Understanding of Project Finance in the Mining Sector in the Sustainability Context: A Scientometric Analysis

2021 ◽  
Vol 13 (18) ◽  
pp. 10317
Author(s):  
Juan David González-Ruiz ◽  
Juan Camilo Mejia-Escobar ◽  
Giovanni Franco-Sepúlveda
Keyword(s):  
Mining Industry ◽  
The United States ◽  
Status Quo ◽  
Research Field ◽  
Research Trends ◽  
Project Finance ◽  
Scientometric Analysis ◽  
Comprehensive Understanding ◽  
Main Research ◽  
The Status

The purpose of this study is to analyze the extant literature on Project Finance (PF) with a comprehensive understanding of the status quo and research trends in the mining industry. Thus, this study utilizes a scientometric review of global trends and structure of PF and mining research from 1977 to 2020 using techniques such as co-author, co-word, co-citation, and cluster analyses. A total of 80 bibliographic records from the Scopus database were analyzed to generate the study’s research through scientometric networks. The findings indicate a steady growth of the research field, which includes Environmental, Social, and Governance criteria. The most significant contributions have originated mainly from the United States, Australia, the United Kingdom, and South Africa. The main research trends identified several issues related to risk, management, and financing concerns. This study provides researchers and practitioners with a comprehensive understanding of the status quo and research trends of ontology research within PF in the mining context and promotes further studies in this domain.

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