A Bibliometric Review of the Status and Emerging Research Trends in Construction Safety Management Technologies

Technologies such as virtual reality (VR), online databases, Geographic Information Systems (GIS), Building Information Modelling (BIM), Unmanned Aerial Vehicle (UAV), 4D Computer-Aided Design (4D CAD), wearable robotics have been adopted to improve construction site health and safety. However, little attempt has been made to map global research on construction health and safety technologies. Therefore, this paper conducts a review of technologies for construction health and safety management to reveal emerging research trends. A bibliometric review adopting a two-step literature selection method was conducted to compile relevant publications from the Scopus database. In total, 240 related papers were examined. VOSviewer was used to develop a co-occurrence network based on the bibliographic data obtained. The analysis focused on co-authorship per country, country/ region distribution, the number of publications annually, publication source and source and trend of research topics. Findings revealed that emerging trends in construction health and safety technologies research focused on project health and safety design and planning, visualisation and image processing for construction projects, digital technologies for project monitoring, information management and Internet of Things, automation and robotic systems, health and safety and accident prevention and structure evaluation

Sistem Peringatan Dini pada Pekerjaan Temporary Support untuk Pengecoran Beton Berbasis Integrasi Bayesian Belief Network (BBN) dan Building Information Modeling (BIM)

ndustri konstruksi merupakan kegiatan yang kompleks dan berisiko, sehingga angka kecelakaan yang terjadi lebih tinggi dibandingkan industri lain. Disisi lain dengan berkembangnya construction 4.0, teknologi konstruksi juga terus berkembang ke arah digitalisasi khususnya Building Information Modeling (BIM). Namun, hubungan antara safety management dengan model informasi berbasis teknologi belum banyak dikembangkan. Penelitian ini bertujuan untuk membuat sistem yang terintegrasi antara proses kegiatan konstruksi yang berisiko/berpotensi pada pekerjaan Temporaray Support (bekisting dan scaffolding) berbasis Bayesian Belief Network (BBN) dengan Building Information Modeling (BIM). Indentifikasi variabel dilakukan dengan literature review, kemudian dilakukan wawancara kepada expert (pelaksana dan QHSE). Didapatkan lima variabel penyebab terjadinya risiko dan empat variabel risiko yang akan terjadi. Kemudian untuk hubungan antar variabel, didapatkan dengan melakukan wawancara kepada expert (pelaksana dan QHSE). Selanjutnya, membuat model Bayesian Belief Network (BBN) sebagai model pengambilan keputusan. Untuk validasi model, 6 bangunan gedung bertingkat di Indonesia dijadikan sebagai implementasi. Hasil simulasi menunjukkan bahwa model dapat menghasilkan kondisi yang sama dengan kondisi nyata dengan ketepatan 91.7%. Kemudian hasil output dari model digunakan untuk integrasi dengan BIM Software. Integrasi dilakukan dengan menghubungkan Model Bayesian Belief Network (BBN) dengan Building Information Modeling (BIM), informasi yang digabungkan meliputi permodelan gedung, jadwal pekerjaan, dan risiko yang didapatkan dari hasil Bayesian Belief Network (BBN. ================================================================================================================================= Construction activities are very complex and risky. Therefore, the number of accidents in the construction industry is higher compared to other industries. On the other hand, the construction industry 4.0, forced construction technology to develop into the world of digitalization, especially Building Information Modeling (BIM). However, the relationship between safety management and technology-based information models has not been widely developed. This study aims to propose a model that integrates construction risks and BIM. Variable identification is done by literature review, then interviews with experts (implementing and QHSE). Obtained five variables that cause risk and four risk variables that will occur. Then for the relationship between variables, obtained by conducting interviews with experts (implementing and QHSE). Next, make the Bayesian Belief Network (BBN) model as a decision making model. For model validation, 6 storey buildings in Indonesia are used as implementation. Simulation results show that the model can produce the same conditions with real conditions with an accuracy of 91.7%. Then the output from the model is used for integration with BIM Software. Integration is carried out by connecting the Bayesian Belief Network (BBN) Model with Building Information Modeling (BIM), information combined using building modeling, work schedules, and risks obtained from the results of the Bayesian Belief Network (BBN). Keyword: construction industry, building, safety management, Bayesian belief network, building information modelin

Reading the brain’s personality: using machine learning to investigate the relationships between EEG and depressivity

Electroencephalography (EEG) measures electrical signals on the scalp and can give information about processes near the surface of the brain (cortex). The goal of our research was to create models that predict depressivity (mapping to personality in general, not just sickness) and to find potential biomarkers in EEG data. First, to provide our models with cleaner EEG data, we designed a novel single-channel physiology-based eye blink artefact removal method and a mains power noise removal method. Then, we assessed two main machine learning model types (classification- and regression-based) with a total of eighteen sub-types to predict the depressivity of participants. The models were generated by combining four signal processing techniques with a) three classification techniques, and b) three regression techniques. The experimental results showed that both types of models perform well in depressivity prediction and one regression-based model (Reg-FFT-LSBoost) showed a significant depressivity prediction performance, especially for female group. More importantly, we found that a specific EEG frequency band (the gamma band) made major contributions to depressivity prediction. Apart from that, the alpha and beta band may make modest contributions. Specific locations (T7, T8, and C3) made major contributions to depressivity prediction. Frontal locations may also have some influence. We also found that the combination of both eye states’ EEG data showed a better depressivity prediction ability. Compared to the eyes closed data, the EEG data obtained from the state of eyes open were more suitable for assessing depressivity. In brief, the outcomes of this research provided the possibilities for translating the EEG data for depressivity measure. Furthermore, there are possibilities to extend the research to apply to other mental disorders’ prediction, such as anxiety