This paper aims to develop a set of safety leading indicators that can be used by construction companies to assess safety levels and measure safety performance at the project level. The development process follows a four-step method: conceptualization, operationalization, indicator generation, and validation and revision. A pressure-state-practice (PSP) model is developed as an overall framework for developing safety leading indicators. The safety level of a construction project is conceptualized as a high-level abstract construct that can be assessed by state indicators, pressure indicators, and practice indicators. Criterion validity (i.e., concurrent validity and predictive validity), practicability, and cost-effectiveness of the leading indicators were qualitatively tested and supported by the empirical evidence collected from three construction projects. This paper adds to the literature on the development of safety leading indicators for the construction industry. The safety leading indicators developed in this paper provide the construction industry a promising tool to proactively measure safety performance and facilitate safety assessments.

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 (a_z + g_x + 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 (a_z + g_x + 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.

This study reports a case study that investigates the gap between BIM tertiary education and the building industry’s needs in New Zealand (NZ). In specific, it aims to (1) identify the industry’s requirements on BIM competencies, (2) examine the status quo of BIM tertiary education in NZ, and (3) identify and analyse the gaps between current BIM education and the industry’s needs. Data were collected through BIM job advertisement to identify the industry’s requirements of BIM competencies and top BIM uses. Data of thirty-three courses were also collected from nine tertiary institutions. Results indicated that there were discrepancies between the type and level (i.e. cognitive levels) of BIM competencies. It was indicated that missing links existed in almost all BIM uses that were taught, except 4D modelling. A large proportion of learning outcomes were focused on ‘understanding’ for several BIM uses, while NZ BIM Handbook requires higher levels of application and evaluation. To bridge the gaps, core competence sets should be determined by disciplines. BIM competencies can also be classified based on BIM uses, BIM processes and phases, and project roles. This paper proposed a conceptual framework, which suggests the way ahead towards future competency-based BIM education.