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The complexities in strait-crossing cable-stayed bridge project are increasing the risks. This study aims to identify and analyze the significant and worth-considered construction risks of the first, biggest and longest spanned strait-crossing bridge project in Indonesia.

As many as 32 risk events were identified and determined as the risks that exist and can be represented in the Suramadu bridge project context. Data was collected through a design-based questionnaire disseminated to experts involved in the project as well as semi-formal interviews. Several quantitative methods were applied to analyze the significant risks, such as relative importance index, Spearman’s rank correlation test and Mann–Whitney U test.

The analyses reveal that “unexpected natural behavior” confirmed by both contractor and consultant parties is the most significant and crucial risk event. Another risk event found to be significant is the “delayed payment.” On the other hand, it is also found that several risks within the legal category are found to be less significant compared to other major risk events.

The results of the present research should be interpreted in the context of several limitations. Given these possible concerns regarding the generalizability of the findings, along with the relatively low rate of participants in the current research, additional studies are needed to provide a more complete picture of stakeholder perceptions who are involved directly in the construction environment as well as to identify more construction risks specifically in the large-scale bridge project.

This study has provided fundamental contributions to the body of knowledge and practical implication to promote and assist decision-makers toward developing a comprehensive risk assessment of a large-scale bridge project.

The analyses of outcomes and discussion, as well as the findings of this research, have shed light on the construction risks understanding, which contributes to delivering a theoretical framework for achieving large-scale bridge project success.

Structural health monitoring (SHM) has gained significant attention due to its capability in providing support for efficient and optimal bridge maintenance activities. However, despite the promising potential, the effectiveness of SHM system might be hindered by unprecedented factors that impact the continuity of data collection. This research presents a framework utilising convolutional neural network (CNN) for estimating structural response using environmental variations.

The CNN framework is validated using monitoring data from the Suramadu bridge monitoring system. Pre-processing is performed to transform the data into data frames, each containing a sequence of data. The data frames are divided into training, validation and testing sets. Both the training and validation sets are employed to train the CNN models while the testing set is utilised for evaluation by calculating error metrics such as mean absolute error (MAE), mean absolute percentage error (MAPE) and root mean square error (RMSE). Comparison with other machine learning approaches is performed to investigate the effectiveness of the CNN framework.

The CNN models are able to learn the trend of cable force sensor measurements with the ranges of MAE between 10.23 kN and 19.82 kN, MAPE between 0.434% and 0.536% and RMSE between 13.38 kN and 25.32 kN. In addition, the investigation discovers that the CNN-based model manages to outperform other machine learning models.

This work investigates, for the first time, how cable stress can be estimated using temperature variations. The study presents the first application of 1-D CNN regressor on data collected from a full-scale bridge. This work also evaluates the comparison between CNN regressor and other techniques, such as artificial neutral network (ANN) and linear regression, in estimating bridge cable stress, which has not been performed previously.

The public's trust in the authorities has a great impact on people's perception and cognition on development of different types of urban transport infrastructure projects (UTIPs). Given the importance of public acceptance for the efficient construction and operation of UTIPs, this study aims at investigating the personal and environmental factors that influence public acceptance behavior from the perspective of stakeholder management.

Based on social cognitive theory (SCT), this study explores the multiple dimensions of social trust on public acceptance in the development of UTIPs by a comparative case study. Two types of UTIPs, a metro railway and a bridge in the Wuhan City, China, were selected as cases, with a questionnaire distributed among the public to collect their sense of trust towards the development of these projects. The data were analyzed through structural equation modeling (SEM).

This study reveals that social trust positively influences public acceptance, directly or indirectly through perceived benefit and -risks and self-efficacy. However, the emphasis on social trust about competence and integrity of the authorities varies with the types of projects. Self-efficacy worked as the “mirror of trust” reflecting people's attitude towards social trust in the authorities on their ability and morality.

The value of the paper lies in discussing social trust from multiple dimensions in the field of urban infrastructures, which provides new insights into specific mechanisms for shaping public acceptance in project management towards the development of UTIPs.

The Government’s investment in infrastructure projects is considerably high, especially in bridge construction projects. Government authorities must establish an initial forecasted budget to have transparency in transactions. Early cost estimating is challenging for Quantity Surveyors due to incomplete project details at the initial stage and the unavailability of standard cost estimating techniques for bridge projects. To mitigate the difficulties in the traditional preliminary cost estimating methods, there is a requirement to develop a new initial cost estimating model which is accurate, user friendly and straightforward. The research was carried out in Sri Lanka, and this paper aims to develop the artificial neural network (ANN) model for an early cost estimate of concrete bridge systems.

The construction cost data of 30 concrete bridge projects which are in Sri Lanka constructed within the past ten years were trained and tested to develop an ANN cost model. Backpropagation technique was used to identify the number of hidden layers, iteration and momentum for optimum neural network architectures.

An ANN cost model was developed, furnishing the best result since it succeeded with around 90% validation accuracy. It created a cost estimation model for the public sector as an accurate, heuristic, flexible and efficient technique.

The research contributes to the current body of knowledge by providing the most accurate early-stage cost estimate for the concrete bridge systems in Sri Lanka. In addition, the research findings would be helpful for stakeholders and policymakers to propose policy recommendations that positively influence the prediction of the most accurate cost estimate for concrete bridge construction projects in Sri Lanka and other developing countries.