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Despite the documented benefits of voice behavior for projects, little is known about antecedents of voice behavior in the project context, especially construction projects. Against this background, adopting a multi-team system perspective, this study attempts to investigate antecedents of team voice behavior from a contextual view.

This study identifies and examines six factors that influence team voice behavior. Specifically, project urgency, project temporality, and project complexity are identified from the project nature perspective. Satisfaction, trust, and commitment are generated from the relationship quality approach. Then, data from completed construction projects in China was collected to verify the effectiveness of these factors. Besides, the partial least squares structural equation modeling (PLS-SEM) technique was used in this study.

All six factors are found to be significant predictors of promotive team voice behavior. For prohibitive team voice behavior, only project complexity and project commitment make significant effects. Further, the differential effects of these factors on two types of voice behavior are revealed.

This study contributes to the literature on voice behavior in the project context, especially construction projects consisting of multiple teams. Also, this research enriches our knowledge on antecedents of team voice behavior in construction projects and thus affords practical implications to foster voice behavior.

Most developing countries, such as Sri Lanka (SL), are now looking for the support of foreign construction companies for large-scale infrastructure projects in return for expertise and resources. Thus, foreign companies may enter into agreements with local contractors through joint ventures (JVs). However, the priorities of construction project stakeholders may differ, which may ultimately end up in conflicts. Therefore, this research aims to investigate the most suitable conflict management strategies for international construction JVs (ICJVs) considering the SL context.

The mixed method was used for the research choice by selecting a questionnaire survey and expert interviews. Completed questionnaires (n = 78) were analysed using statistical techniques. The expert interviews with six industry practitioners were piloted to increase the validity and credibility of survey findings through a triangulation process where the collected data was analysed through content analysis.

The findings confirm that JV parties should first seek collaborative solutions in a conflict and seek legal redress only when those efforts are unsuccessful. Collaborating and compromising were recommended as the most appropriate tactics if an informal approach to conflict management was chosen. Alternative dispute resolution and litigation were identified as formal conflict management strategies.

This study, to the best of the authors’ knowledge, will be the first of its kind in SL, which will lead to a better understanding of conflict management in IJCVs and will encourage other researchers to extend this study through further work.

The purpose of this study is to automatically generate a construction schedule by extracting data from the BIM (Building Information Modeling) model and combining an ontology constraint rule and a genetic algorithm (GA).

This study developed a feasible multi-phase framework to generate the construction schedule automatically through extracting information from the BIM, utilizing the ontology constraint rule to demonstrate the relationships between all the components and finally using the GA to generate the construction schedule.

To present the functionality of the framework, a prototype case is adopted to show the whole procedure, and the results show that the scheme designed in this study can quickly generate the schedule and ensure that it can satisfy the requirements of logical constraints and time parameter constraints.

A proper utilization of conceptual framework can contribute to the automatic generation of construction schedules and significantly reduce manual errors in the Architectural, Engineering, and Construction (AEC) industry. Moreover, a scheme of BIM-based ontology and GA for construction schedule generation may reduce additional manual work and improve schedule management performance.

The hybrid approach combines the ontology constraint rule and GA proposed in this study, and it is an effective attempt to generate the construction schedule, which provides a direct indicator for the schedule control of the project.

In this study, the data application process of the BIM model is divided into four modules: extraction, processing, optimization, and output. The key technologies including secondary development, ontology theory, and GA are introduced to develop a multi-phase framework for the automatic generation of the construction schedule and to realize the schedule prediction under logical constraints and duration interference.

The purpose of this study is to prepare thermal transfer ribbons with good alcohol resistance.

A variety of alcohol-resistant thermal transfer inks were prepared using different polyester resins. The printing temperature, printing effect, adhesion and alcohol resistance of the inks on the label were studied to determine the feasibility of using the ink for manufacturing thermal transfer ribbons. The ink formulations were prepared by a simple and stable grinding technology, and then use mature coating technology to make the ink into a thermal transfer ribbon.

The results show that the thermal transfer ink has good scratch resistance, good alcohol resistance and low printing temperature when the three resins coexist. Notably, the performance of the ribbon produced by 500 mesh anilox roller was better than that of other meshes. Specifically, the ink on the matte silver polyethylene terephthalate (PET) label surface was wiped with a cotton cloth soaked in isopropyl alcohol under 500 g of pressure. After 50 wiping cycles, the ink remained intact.

The proposed method not only ensures good alcohol resistance but also has lower printing temperature and wider label applicability. Therefore, it can effectively reduce the loss of printhead and reduce production costs, because of the low printing temperature.

Music sentiment analysis helps to promote the diversification of music information retrieval methods. Traditional music emotion classification tasks suffer from high manual workload and low classification accuracy caused by difficulty in feature extraction and inaccurate manual determination of hyperparameter. In this paper, the authors propose an optimized convolution neural network-random forest (CNN-RF) model for music sentiment classification which is capable of optimizing the manually selected hyperparameters to improve the accuracy of music sentiment classification and reduce labor costs and human classification errors.

A CNN-RF music sentiment classification model is designed based on quantum particle swarm optimization (QPSO). First, the audio data are transformed into a Mel spectrogram, and feature extraction is conducted by a CNN. Second, the music features extracted are processed by RF algorithm to complete a preliminary emotion classification. Finally, to select the suitable hyperparameters for a CNN, the QPSO algorithm is adopted to extract the best hyperparameters and obtain the final classification results.

The model has gone through experimental validations and achieved a classification accuracy of 97 per cent for different sentiment categories with shortened training time. The proposed method with QPSO achieved 1.2 and 1.6 per cent higher accuracy than that with particle swarm optimization and genetic algorithm, respectively. The proposed model had great potential for music sentiment classification.

The dual contribution of this work comprises the proposed model which integrated two deep learning models and the introduction of a QPSO into model optimization. With these two innovations, the efficiency and accuracy of music emotion recognition and classification have been significantly improved.