VGTU Journals (Vilnius Gediminas Technical University - Vilnius Tech)
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Machine learning of electroencephalography signals and eye movements to classify work-in-progress risk at construction sites
No full textThe construction industry has consistently faced high accident rates and delays in recognizing hazards, posing significant risks to onsite personnel. Traditional hazard detection methods are often reactive rather than proactive, emphasizing a pressing need for innovative solutions. Despite advances in safety technology, a considerable gap remains in real-time, accurate hazard recognition at construction sites. Current technologies do not fully leverage physiological data to predict and mitigate risks. This research introduces a groundbreaking approach by employing machine learning to analyze electroencephalography (EEG) signals and eye movement data, enabling real-time differentiation of safe, warning, and hazardous visual cues. A Random Forest model with an impressive classification accuracy of 99.04% has been developed, marking a significant enhancement in identifying potential hazards. The possible impact of integrating EEG and eye movement analyses into wearable devices or onsite sensors is substantial, as it could revolutionize safety protocols in the construction industry, fostering a safer future.
First published online 31 December 202
Experimental and theoretical investigation of tension stiffening and curvature in RC beams with extended concrete cover
No full textAccurate assessment of tension stiffening is important for predicting deflection and crack width in RC structures. Earlier studies by the authors have shown that an extended cover thickness increases tension stiffening in bending RC members. The current study experimentally and theoretically investigates curvature and tension stiffening in RC beams nominally having a 50 mm cover for 32 mm bars of tensile reinforcement. The four-point bending tests were carried out on square section (400×400 mm) RC beams. Mean experimental curvatures were obtained for the pure bending zone by different approaches, namely, from a mid-point deflection and from strains at several horizontal layers measured either by LVDT or DIC technique. The tension stiffening effect in the test beams was quantified by inversely calculating the resultant internal force of tensile concrete, Nct, using the test moment – curvature diagrams. Tension stiffening is characterized by parameter β0 indicating the ratio of β0 = M / Mcr at which the force Nct reaches zero. The condition Nct = 0 represents the bending stiffness of a fully cracked RC section. Earlier studies by the authors have shown that parameter β0 equals to 3 for the beams with a typical cover thickness (25–35 mm). The current study has demonstrated that for the beams having nominal cover thickness of 50 mm and bar diameter of 32 mm, parameter β0 reached rather high values indicating a little degradation of tension stiffening with increasing load
Investigating the influence of urban green spaces on urban heat island mitigation – taking four districts in Shijiazhuang as an example
No full textThe primary objective of this scholarly investigation is to elucidate the intricate interplay between the urban heat island (UHI) effect and municipal green spaces. The geographical focus includes the four areas with the highest urbanization rate in Shijiazhuang, China. To conduct this survey, ECOSTRESS remote sensing imagery was acquired during distinct temporal intervals–morning, midday, and evening. The data were collected using the equal-scale city blocks performed by the OpenStreetMap urban network and ECOSTRESS remote sensing images at different times (morning, noon and evening). Surface temperature inversion of satellite images was performed using ArcGIS 10.7 software to obtain surface temperature. The overarching aim was to discern the nuanced impact of urban parks on the surface temperatures of their proximate environs during the summer season. The findings of this investigation revealed that, in order to effectively ameliorate the discernible heat island effect (SUH), rejuvenation initiatives ought to be directed toward sites maintaining a distance from green spaces within the range of 160 to 370 meters. Furthermore, augmentation of green space configurations is recommended in vicinities characterized by building densities falling within the range of 0.2 to 0.3. Notably, in locales marked by high building density, park layouts should adhere to a more regularized design during the renovation process. Additionally, it is advisable to ensure that the spatial separation between distinct urban parks exceeds 900 meters. These empirical insights are poised to enhance the comprehension of urban planners regarding the intricate dynamics through which urban parks exert influence on municipal surface temperatures. Furthermore, the discerned patterns furnish pragmatic guidance for mitigating the heat island effect, thereby offering invaluable recommendations for urban planning endeavors
Regeneration and characterization of spent bleaching earth: recycling in the corn oil bleaching process
No full textThe spent bleaching earth (SBE) is a solid waste from the edible oil refining industry which generates soil contamination was successfully recycled after deoiling through an extraction process using different organic solvents, followed by heat treatment. In the current study, the effects of factors, such as solvent to (SBE) ratio [1:1–5:1], temperature [20–40 °C], and stirring time [30–60 min] on the efficiency of extracted oil were investigated by maceration method. Characterization analyses (SEM, XRD, XRFA and TGA) were carried out to compare the characteristics of samples. The best oil extraction efficiency was obtained at the highest level of solvent to (SBE) ratio (MR = 5) at 30 °C temperature and at the 45 minutes stirring time this condition led to 72.82% oil extraction yield. The corn oil bleaching efficiency using the SBE treated at optimal condition and heated at 400 °C was improved to 84.75%
The moderating effects of outside directors on the relationship between managerial overconfidence and earnings management: evidence from Korea
No full textThe subjective judgment and discretionary actions of a manager can influence the core strategy, investment, operations, and decision-making of a company. Managerial actions from the top, particularly the board, plays a vital role in addressing this inclination. While scholars have shown interest in examining managers’ overconfidence tendencies in recent years, few have explored the characteristics of the board members. According to the study, a high proportion of outside directors on the board of Korean companies has been observed to alleviate upward earnings management driven by managerial overconfidence. The obtained results emphasize the significance of the board of directors in enterprises and contribute to theories related to managerial characteristics. This study aims to bridge the research gap concerning managers’ tendencies of overconfidence while expanding the existing knowledge in this field. Firstly, this study aims to address the need to identify cognitive characteristics of managers. Secondly, it investigates the impact of the board of directors’ characteristics on managers’ tendencies towards overconfidence
Should we be wary of using artificial intelligence-based big data management in social research?
No full textThis study examines the future role of artificial intelligence (AI) in transforming research processes within the social sciences, focusing on how AI may redefine researchers\u27 responsibilities and potentially replace human participants in certain types of studies. Employing the Delphi method, the study collects expert opinions to evaluate both facilitating factors and barriers to the integration of AI into scientific research. Key findings indicate that while technological advancements – such as open-access data and the integration of AI with existing research tools – support the growing role of AI, significant challenges remain. These include the difficulty of verifying AI-generated information and concerns regarding authenticity in AI-driven research. Social factors, particularly the risk of excessive reliance on AI leading to diminished originality, emerged as critical barriers. In contrast, economic considerations, such as declining development costs, were viewed as less influential. The study’s practical implications include the need for robust ethical guidelines and enhanced AI training for researchers. By offering original insights into the evolving intersection of AI and social science research, this study highlights both the transformative potential of AI and the urgent need for its responsible integration to preserve research integrity and reliability
Exploring the use of in-house sodium silicate from agro-industrial by-products in pervious geopolymer concrete
No full textThe extraction of in-house sodium silicate (IHS) as an alternative to commercial silicate in geopolymer pervious concrete (GPC) is the focus of this research. The IHS was developed from rice husk ash (RHA) and treated palm oil fuel ash (TPOFA) using the hydrothermal method. Class F Fly Ash (FA) and Ground Granulated Blast Furnace Slag (GGBS) were used as precursors in a 70:30 ratio. Steel slag aggregate (SSA) was used to wholly replace the conventional aggregates. Palm kernel shell biochar (PKS-BC) at various weight percentages between 1 and 5% was used to replace coarse aggregates (CA). GPC specimens were prepared using 10 M sodium hydroxide (NaOH) and one of the SS: commercial SS, RHA-based IHS, and a ‘Hybrid SS’ (commercial SS: TPOFA-based IHS – 50:50). The findings revealed that due to the toughness, surface roughness, and shape of the SSA, the compressive strength of SSA-based GPC specimens produced higher strength compared to crushed granite aggregate (CGA)-based GPC. ‘Hybrid SS’ and RHA-based IHS yielded slightly higher compressive strengths in GPC specimens compared to commercial SS-based GPC specimens. This finding proved that the appropriate ratio of silica source with NaOH facilitates the development of SS in the development of GPC
BIM-based digital twin for the management of a railway station
No full textBuilding Information Modelling (BIM) has positioned as one of the main project methodologies into the AECO (Architecture, Engineering, Construction and Operations) sector. However, its implementation entails important barriers such as hardware and software requirements or BIM skills and education. The main aim of this research was to overcome those implementation barriers providing full accessibility to BIM benefits, especially in the infrastructure management phase leading to a digital twin (DT) of the built environment. Furthermore, the paper shows the technical aspects of the implementation of BIM information as well as the external functionalities, i.e., real time information or data queries, which are commonly define in BIM-IoT topic rather than BIM-FM interoperability. The research is based on the project for the northern covering and new building of the Bidebieta-Basauri passenger station, located on the Bilbao-Orduña commuter line in the Autonomous Community of the Basque Country, Spain. The final result is a BIM-based online web platform with Full access to the BIM model of the infrastructure, linked with other functionalities based on technologies such as Internet of Things (IoT), Bigdata or Cloud Computing
Sustainable design of recycled concrete using shape optimization and carbon dioxide emission based on LCA
No full textSwitching from waste concrete disposal to recycling is urgently needed to enhance resource efficiency and reduce carbon emissions. This paper proposes a sustainable design framework for recycled concrete, incorporating shape optimization and carbon dioxide (CO2) emission analysis using life cycle assessment (LCA). Using recycled concrete in infrastructure projects, this paper develops a carbon dioxide emissions accounting model based on LCA. Two water-cement ratios (WCR) and four recycled concrete aggregate replacement rates (RCARR) were tested on two natural aggregate concrete (NAC) and six recycled aggregate concrete (RAC) samples. Furthermore, four shapes options for the RAC structural member were designed, optimized, and compared. The G35 Expressway slope projects were used as a case study. The results showed that the regular hexagonal RAC structural member was selected for the project, achieving a carbon reduction rate of about 9%. The study also found that 1) life cycle carbon emission decreases with the increase of WCR and RCARR, respectively; 2) compared to NAC, the key processes of carbon emission reduction of RAC include the raw material acquisition and transportation stage as well as the carbonization absorption stage; 3) there is a transport distance threshold, beyond which the life cycle CO2 emissions of RAC exceed those of NAC
Enhancing construction safety management through multivariable grey model analysis and variable selection optimization
No full textIn this study, a multivariable grey model (GM(1, N)) is employed to explore how different combinations of variables impact the accuracy of construction accident prediction, using a full permutation algorithm. The aim is to optimize variable selection and improve prediction accuracy. By conducting an exhaustive analysis of 511 potential combinations involving nine variables, it was observed that by integrating crucial external variables such as macroeconomic indicators and industry scale, the multivariable model achieved a prediction accuracy error rate of less than 0.5%, thereby significantly enhancing its information capture and forecasting precision. The analysis suggests that optimal predictive performance is achieved when the number of control variables is approximately four. Additionally, further research shows that increasing the dataset size significantly enhances the model’s predictive capability. This study highlights the scientific rigor and precision of decisionmaking in preventing construction accidents and provides empirical evidence for construction safety management. The research in this paper not only enriches the connotation of the grey system prediction model theoretically, but also provides a data-driven decision support tool for urban construction and safety accident prevention in practice