Children are highly represented in statistics relating to road traffic injuries and fatalities. There are multiple risk factors that together create an increased risk for children in traffic, some of the major reasons include children's inability to efficiently and actively scan the environment and look for information relevant to the traffic environment, inconsistent behavior, and less developed hazard perception skills. Traffic safety education is one of the most important means for improving knowledge and skills required for children to behave safely in traffic. This study evaluated a newly developed and gamified e-learning platform meant to promote traffic safety among elementary school pupils in Belgium. Participants in this study were from four grades of elementary school and voluntarily took part in the training. They followed a self-study program at home for approximately 15 minutes per week over a period of five weeks in total. The platform included four modules: traffic knowledge, situation awareness, risk detection, and risk management. For each of these modules, a set of photos and videos were used as stimuli and selected from a database of camera recordings of real-life situations. Half of each module consisted of familiar situations for the pupils (i.e., own municipality), while the other half of each module consisted of unfamiliar situations for the pupils (i.e., other municipalities). A fifth module, 'the final', contained a mix of the first four modules. In total, 44 elementary school pupils (9-13 years old) completed the program. During the first round of measurement (i.e., the first four modules), pupils performed significantly better in the traffic knowledge module when compared to the other three modules. Further, in comparison to unfamiliar situations, pupils scored significantly higher in familiar situations. During the second round of measurement (i.e., the fifth module), pupils achieved higher scores in the risk detection and risk management modules when contrasted to the first measurement. The effect of gamification elements is discussed and the results also indicate the type of traffic safety issues to be emphasized in traffic safety education for children.

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Road safety education has been recognized as an instrument for reducing road accidents. This study aims to evaluate the road safety education program "Traffic Weeks" among higher secondary school students (age 16-19) in Belgium. The program focuses on driving under influence (DUI) and traffic risks. This study investigates whether the program has an effect on socio-cognitive variables using a questionnaire based on the theory of planned behavior. During the pre-test, 445 students filled in the questionnaire, while 253 students filled in the questionnaire during the post-test. Of these, 175 questionnaires could be matched. The results indicate that the students already had quite a supportive view of road safety at pre-test, with female students showing a more supportive view of road safety than male students. The DUI workshop had a positive effect on most socio-cognitive variables (attitude, subjective norm-friends, and intention) of female students in general education, while the traffic risks workshop only affected perceived behavioral control of female students. In terms of appreciation, students had a significantly higher appreciation of the DUI workshop compared to the traffic risks workshop. During the focus groups, students gave recommendations to improve the program.

This paper addresses the issues in making wood–concrete composites more resilient to environmental conditions and to improve their compressive strength. Tests were carried out on cubic specimens of 10 × 10 × 10 cm3 composed of ordinary concrete with a 2% redwood- and hardwood-chip dosage. Superficial treatments of cement and lime were applied to the wood chips. All specimens were kept for 28 days in the open air and for 12 months in: the open air, drinking water, seawater, and an oven. Consequently, the compressive strength of ordinary concrete is approximately 37.1 MPa. After 365 days of exposure to the open air, drinking water, seawater, and the oven, a resistance loss of 35.84, 36.06, 42.85, and 52.30% were observed, respectively. In all environments investigated, the untreated wood composite concrete’s resistance decreased significantly, while the cement/lime treatment of the wood enhanced them. However, only 15.5 MPa and 14.6 MPa were attained after the first 28 days in the cases of the redwood and the hardwood treated with lime. These findings indicate that the resistance of wood–concrete composites depends on the type of wood used. Treating wood chips with cement is a potential method for making these materials resistant in conservation situations determined by the cement’s chemical composition. The current study has implications for researchers and practitioners for further understanding the impact of these eco-friendly concretes in the construction industry.

The liquid limit (LL) is considered the most fundamental parameter in soil mechanics for the design and analysis of geotechnical systems. According to the literature, the LL is governed by different particle sizes such as sand content (S), clay content (C), and silt content (M). However, conventional methods do not incorporate the effect of all the influencing factors because traditional methods utilize material passing through a # 40 sieve for LL determination (LL40), which may contain a substantial number of coarse particles. Therefore, recent advancements suggest that the LL must be determined using material passing from a # 200 sieve. However, determining the liquid limit using # 200 sieve material, referred to as LL200 in the laboratory, is a time-consuming and difficult task. In this regard, artificial-intelligence-based techniques are considered the most reliable and robust solutions to such issues. Previous studies have adopted experimental routes to determine LL200 and no such attempt has been made to propose empirical correlation for LL200 determination based on influencing factors such as S, C, M, and LL40. Therefore, this study presents a novel prediction model for the liquid limit based on soil particle sizes smaller than 0.075 mm (# 200 sieve) using gene expression programming (GEP). Laboratory experimental data were utilized to develop a prediction model. The results indicate that the proposed model satisfies all the acceptance requirements of artificial-intelligence-based prediction models in terms of statistical checks such as the correlation coefficient (R2), root-mean-square error (RMSE), mean absolute error (MAE), and relatively squared error (RSE) with minimal error. Sensitivity and parametric studies were also conducted to assess the importance of the individual parameters involved in developing the model. It was observed that LL40 is the most significant parameter, followed by C, M, and S, with sensitivity values of 0.99, 0.93, 0.88, and 0.78, respectively. The model can be utilized in the field with more robustness and has practical applications due to its simple and deterministic nature.