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University of Canterbury, as an institution, had developed strong educational resilience during and after the 2010-11 earthquakes. Due to the Coronavirus pandemic, New Zealand shut down all university educational facilities in March 2020, with education migrated online. Within this unique context, this research aims to (1) investigate civil engineering students' perceptions about the benefits and challenges of online learning during the lockdown period, (2) identify significant factors that contribute to effective online teaching and learning, and (3) provide the implications of this research for future emergency remote teaching (ERT). A survey was designed and administered to measure students' perceptions of online learning during the lockdown. A total of 192 completed responses were collected from both undergraduate and Master civil engineering students. In addition to typical online learning challenges, students also experienced unique challenges, such as social and emotional isolation, anxiety, depression, and uncertainty. This paper suggests that teaching presence be brought to the forefront of ERT. Future research directions were recommended to better understand ERT by linking it to the Community of Inquiry framework and enhance tertiary education institutions' resilience to handle a future crisis.

As-built progress of the constructed pavement should be monitored effectively to provide prompt project control. However, current pavement construction progress monitoring practices (e.g., data collection, processing, and analysis) are typically manual, time-consuming, tedious, and error-prone. To address this, this study proposes sensors mounted using a UGV-based methodology to develop a pavement layer change classifier measuring pavement construction progress automatically. Initially, data were collected using the UGV equipped with a laser ToF (time-of-flight) distance sensor, accelerometer, gyroscope, and GPS sensor in a controlled environment by constructing various scenarios of pavement layer change. Subsequently, four Long Short-Term Memory network variants (LSTMs) (LSTM, BiLSTM, CNN-LSTM, and ConvLSTM) were implemented on collected sensor data combinations for developing pavement layer change classifiers. The authors conducted the experiment to select the best sensor combinations for feature detection of the layer change classifier model. Subsequently, individual performance measures of each class with learning curves and confusion matrices were generated using sensor combination data to find out the best algorithm among all implemented algorithms. The experimental result demonstrates the (az + gx + D) sensor combination as the best feature detector with high-performance measures (accuracy, precision, recall, and F1 score). The result also confirms the ConvLSTM as the best algorithm with the highest overall accuracy of 97.88% with (az + gx + D) sensor combination data. The high-performance measures with the proposed approach confirm the feasibility of detecting pavement layer changes in real pavement construction projects. This proposed approach can potentially improve the efficiency of road construction progress measurement. This research study is a stepping stone for automated road construction progress monitoring. [ABSTRACT FROM AUTHOR]

This study aims to explore the role of digital education in the development of skills and employability for engineering students through researching the role of big data analytics courses. The empirical study proposes the hypothesis that both soft and hard skills have positive effects on human capital, individual attributes, and the career development dimensions of engineering students. This is achieved through constructing a framework of three dimensions of engineering students' employability and two competency development dimensions of big data analytics courses. A questionnaire survey was conducted with 155 college engineering students and a structural equation model (SEM) was used to test the hypotheses. The results found that courses on big data analytics have a positive impact on engineering students' abilities in both hard skills (p <0.01) and soft skills (p <0.001) dimensions, while soft skills have a more significant impact on engineering students' employability. The study has practical and theoretical implications that further enriches the knowledge base on engineering education and broadens our understanding of the role of digitalization in enhancing the skills and employability of engineering students. [ABSTRACT FROM AUTHOR]

This paper aims to investigate the design for safety (DfS) knowledge, attitude, and practice (KAP) of multiple stakeholders, including architects, civil and structural (CS) engineers, mechanical and electrical (ME) engineers, developers/clients, project managers, and safety professionals, and propose potential interventions to improve the DfS implementation in the construction industry. Data were collected from 257 multiple stakeholders in Singapore using a KAP questionnaire and seven semistructured interviews with DfS professionals, architects, developers, and project managers. The results indicated that the DfS knowledge level of survey participants needs to be improved. The majority of respondents demonstrated a positive attitude towards DfS, but the average level of DfS practice was low. Statistically significant differences in DfS practice were found between developers/clients, CS engineers, and project managers. Three recommendations for improving DfS implementation were proposed: (1) enhance DfS training programs, (2) establish a DfS Community of Practice, and (3) develop DfS courses in tertiary institutions. The findings of this paper can help in the design of strategies to improve the implementation of DfS in the construction industry at the national level and the approach can be used to track the effectiveness of DfS-related interventions across time. [ABSTRACT FROM AUTHOR]

Despite its potential, the use of machine learning in safety studies had been limited. Considering machine learning's advantage in predictive accuracy, this study used a supervised learning approach to evaluate the relative importance of different cognitive factors within the Theory of Reasoned Action (TRA) in influencing safety behavior. Data were collected from 80 workers in a tunnel construction project using a TRA-based questionnaire. At the same time, behavior-based safety (BBS) observation data, % unsafe behavior, was collected. Subsequently, with the TRA cognitive factors as the input attributes, six widely-used machine learning algorithms and logistic regression were used to develop models to predict % unsafe behavior. The receiver operating characteristic (ROC) curves show that decision tree provides the best prediction. It was found that intention and social norms have the biggest influence on whether a worker was observed to work safely or not. Thus, managers aiming to improve safety behaviors need to pay specific attention to social norms in the worksite. The study also showed that a TRA survey can be used to extend a BBS to facilitate more effective interventions. Lastly, the study showed that machine learning algorithms provide an alternative approach for analyzing the relationship between the cognitive factors and behavioral data., (Copyright © 2018 Elsevier Ltd. All rights reserved.)

Introduction: Previous safety climate studies primarily focused on either large construction companies or the construction industry as a whole, while little is known about whether company size has significant effects on workers' understanding of safety climate measures and relationships between safety climate factors and safety behavior. Thus, this study aims to: (a) test the measurement equivalence (ME) of a safety climate measure across workers from small and large companies; (b) investigate if company size alters the causal structure of the integrative model developed by Guo, Yiu, and González (2016)., Method: Data were collected from 253 construction workers in New Zealand using a safety climate measure. This study used multi-group confirmatory factor analyses (MCFA) to test the measurement equivalence of the safety climate measure and structure invariance of the integrative model., Results: Results indicate that workers from small and large companies understood the safety climate measure in a similar manner. In addition, it was suggested that company size does not change the causal structure and mediational processes of the integrative model., Conclusions: Both measurement equivalence of the safety climate measure and structural invariance of the integrative model were supported by this study. Practical applications: Findings of this study provided strong support for a meaningful use of the safety climate measure across construction companies in different sizes. Safety behavior promotion strategies designed based on the integrative model may be well suited for both large and small companies., (Copyright © 2017 National Safety Council and Elsevier Ltd. All rights reserved.)

Construction safety management involves complex issues (e.g., different trades, multi-organizational project structure, constantly changing work environment, and transient workforce). Systems thinking is widely considered as an effective approach to understanding and managing the complexity. This paper aims to better understand dynamic complexity of construction safety management by exploring archetypes of construction safety. To achieve this, this paper adopted the ground theory method (GTM) and 22 interviews were conducted with participants in various positions (government safety inspector, client, health and safety manager, safety consultant, safety auditor, and safety researcher). Eight archetypes were emerged from the collected data: (1) safety regulations, (2) incentive programs, (3) procurement and safety, (4) safety management in small businesses (5) production and safety, (6) workers' conflicting goals, (7) blame on workers, and (8) reactive and proactive learning. These archetypes capture the interactions between a wide range of factors within various hierarchical levels and subsystems. As a free-standing tool, they advance the understanding of dynamic complexity of construction safety management and provide systemic insights into dealing with the complexity. They also can facilitate system dynamics modelling of construction safety process., (Copyright © 2015 Elsevier Ltd. All rights reserved.)