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The purpose of this work is to perform the finite element analysis (FEA) for the numerical discretization of sections with different arrangements of Web openings to investigate the torsion behavior. Typical hexagonal and circular Web opening sections are extensively used in steel construction due to economic development in building design. However, the use of sections with different arrangements of Web opening had improved the performance of the section with Web opening in terms of structural behavior which leads to economic design compared to typical I-beam.

The accuracy of FE results allows extensive numerical analysis of stress concentration magnitude for sections with Web openings, concentrating on the sizes and positions of the Web opening. Five shapes and three sizes of Web opening are used in this work. The shapes involved are c-hexagon, hexagon, octagon, circular and square, whereas the sizes of the Web opening involved are 0.67 D, 0.75 D and 0.80 D where D is the height of the Web. Two types of models for 200 × 100 × 8×6 mm steel section involved which is Model 1, where the section with 50 mm edge and 150 mm center-to-center distance and Model 2, where the section with 100 mm edge and 200 mm center-to-center distance.

It was found that these configurations affect the section with various shapes of Web openings sizes (0.67 D, 0.75 D, and 0.80 D). This also includes the spacing distances, with 50 mm edge and 150 mm center-to-center distance and also a section with 100 mm edge and 200 mm center-to-center distance. Through the FEA results of Model 1 and Model 2, it is found that 50% reduction in horizontal member length in hexagon Web opening, from 50 mm to 20 mm, caused increment about 30%–53% stress concentration in Web for c-hexagon. However, for a stress analysis of c-hexagon, geometry resulted in a lower stress concentration in the Web than other Web opening.

Additionally, the work emphasized the efficiency of Web opening shapes by using an appropriate Web opening radius in section with c-hexagon, hexagon, octagon, square and circular shapes. The final results show the contribution of appropriate Web opening radius to increase the section torsional capacity. It is observed that the torsional capacity at certain loading condition and its angle of twist is analysed.

Manual monitoring is a conventional method for monitoring and managing construction safety risks. However, construction sites involve risk coupling - a phenomenon in which multiple safety risk factors occur at the same time and amplify the probability of construction accidents. It is challenging to manually monitor safety risks that occur simultaneously at different times and locations, especially considering the limitations of risk manager’s expertise and human capacity.

To address this challenge, an automatic approach that integrates point cloud, computer vision technologies, and Bayesian networks for simultaneous monitoring and evaluation of multiple on-site construction risks is proposed. This approach supports the identification of risk couplings and decision-making process through a system that combines real-time monitoring of multiple safety risks with expert knowledge. The proposed approach was applied to a foundation project, from laboratory experiments to a real-world case application.

In the laboratory experiment, the proposed approach effectively monitored and assessed the interdependent risks coupling in foundation pit construction. In the real-world case, the proposed approach shows good adaptability to the actual construction application.

The core contribution of this study lies in the combination of an automatic monitoring method with an expert knowledge system to quantitatively assess the impact of risk coupling. This approach offers a valuable tool for risk managers in foundation pit construction, promoting a proactive and informed risk coupling management strategy.

An accurate prediction of the deformation of retaining structures is critical for ensuring the stability and safety of braced deep excavations, while the high nonlinear and complex nature of the deformation makes the prediction challenging. This paper proposes an explainable boosted combining global and local feature multivariate regression (EB-GLFMR) model with high accuracy, robustness and interpretability to predict the deformation of retaining structures during braced deep excavations.

During the model development, the time series of deformation data is decomposed using a locally weighted scatterplot smoothing technique into trend and residual terms. The trend terms are analyzed through multiple adaptive spline regressions. The residual terms are reconstructed in phase space to extract both global and local features, which are then fed into a gradient-boosting model for prediction.

The proposed model outperforms other established approaches in terms of accuracy and robustness, as demonstrated through analyzing two cases of braced deep excavations.

The model is designed for the prediction of the deformation of deep excavations with stepped, chaotic and fluctuating features. Further research needs to be conducted to expand the model applicability to other time series deformation data.

The model provides an efficient, robust and transparent approach to predict deformation during braced deep excavations. It serves as an effective decision support tool for engineers to ensure the stability and safety of deep excavations.

The model captures the global and local features of time series deformation of retaining structures and provides explicit expressions and feature importance for deformation trends and residuals, making it an efficient and transparent approach for deformation prediction.

This paper aims to conduct a detailed analysis of the industrial practices currently being used in the geothermal energy industry and to determine whether they are contributing to any limitations. A HAZOP-based upgradation model for improvement in existing industrial practices is proposed to ensure the removal of inefficient conventional practices. The HAZOP-based upgradation model examines the setbacks, identifies its causes and consequences and suggests improvement methods comprising of modern-day technology.

This paper proposed a HAZOP-based upgradation model for improvement in existing industrial practices. The proposed HAZOP model identifies the drawbacks brought on by conventional practices and suggests improvements.

The study reviewed the challenges geothermal power plants currently face due to conventional practices and suggested a total of 22 upgradation recommendations. From those, a total of 11 upgradation modules comprising modern digital technology and Industry 4.0 elements were proposed to improve the existing practices in the geothermal energy industry. Autonomous robots, augmented reality, machine learning and Internet of Things were identified as useful methods for the upgradation of the existing geothermal energy system.

If proposed recommendations are incorporated, the efficiency of geothermal energy generation will increase as cumulating setbacks will no longer degrade the work output.

The proposed recommendation by the study will make way for Industry 4.0 integration with the geothermal energy sector.

The paper uses a proposed HAZOP-based upgradation model to review issues in existing industrial practices of the geothermal energy sector and recommends solutions to overcome operability issues using Industry 4.0 technologies.