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Successful project delivery for sustainable building construction (SBC) has been linked to certain features. Previous studies have emphasised the need to improve SBC practice in South Africa. The purpose of this study is to explore the SBC features for project delivery in South Africa.

A structured questionnaire elicited the primary data from 281 built environment professionals, mainly in South Africa’s Gauteng province. Descriptive and inferential statistics were used for the data analysis. This study used the principal component analysis technique to ascertain the principal SBC features.

Three components of SBC features, namely, sustainable resource use and compliance, sustainable waste minimisation and recycling and sustainable designs and materials, were developed from the principal component analysis. The factor loadings of the constituent variables ranged from 0.570 to 0.836. The reliability of each component was evaluated, and the results were 0.966, 0.931 and 0.913.

The revelations from this study will aid the decision-making of the relevant stakeholders towards establishing improvement initiatives and mitigating the reluctance to shift from conventional building methods and poor knowledge sharing of SBC benefits.

This is one of the most recent South African studies that sheds light on the components of a successful SBC deployment. The findings of this study added to knowledge by confirming three fundamental features of SBC. This study recommends adequately considering the principal features for successful SBC project delivery in South Africa.

This study aims to ensure reliable analysis of dynamic responses in asphalt pavement structures. It investigates noise reduction and data mining techniques for pavement dynamic response signals.

The paper conducts time-frequency analysis on signals of pavement dynamic response initially. It also uses two common noise reduction methods, namely, low-pass filtering and wavelet decomposition reconstruction, to evaluate their effectiveness in reducing noise in these signals. Furthermore, as these signals are generated in response to vehicle loading, they contain a substantial amount of data and are prone to environmental interference, potentially resulting in outliers. Hence, it becomes crucial to extract dynamic strain response features (e.g. peaks and peak intervals) in real-time and efficiently.

The study introduces an improved density-based spatial clustering of applications with Noise (DBSCAN) algorithm for identifying outliers in denoised data. The results demonstrate that low-pass filtering is highly effective in reducing noise in pavement dynamic response signals within specified frequency ranges. The improved DBSCAN algorithm effectively identifies outliers in these signals through testing. Furthermore, the peak detection process, using the enhanced findpeaks function, consistently achieves excellent performance in identifying peak values, even when complex multi-axle heavy-duty truck strain signals are present.

The authors identified a suitable frequency domain range for low-pass filtering in asphalt road dynamic response signals, revealing minimal amplitude loss and effective strain information reflection between road layers. Furthermore, the authors introduced the DBSCAN-based anomaly data detection method and enhancements to the Matlab findpeaks function, enabling the detection of anomalies in road sensor data and automated peak identification.