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A reasonable assessment of overall effective levels, incorporating dimensions such as risks, durations, and costs, will be salutary for the rational planning and better selection of optional transmission corridor mechanized construction schemes. With this motivation, this paper establishes an ensemble to address the issue of effective level diagnoses, and thus the hidden patterns and regularities between scheme features and effective levels can be explored. Based on the complex characteristics of input data, the Pearson correlation coefficient is deployed to handle the multidimensional data from multiple sources, and K-means clustering is then employed to classify scheme indicators into classes. The Weighted Itemset Mining (W-IM) model is proposed to identify the underlying key factors, to cope with the frequent omission of High Impact Low Probability (HILP) factors during the qualitative analysis stage. Next, the Factor Criticality Analysis (FCA) model is built to quantify the specific impact levels of these distinguished key elements. Finally, the significance proportion of individual features to the overall effectiveness can be determined and optimized according to the Entropy Impact Model (EIM) model. An empirical case study indicates that the proposed ensemble in this paper exhibits higher predictive accuracy along with better flexibility and comprehensiveness.

Currently, the international economic situation is becoming increasingly complex, and there is significant downward pressure on the global economy. In recent years, China’s infrastructure sector has experienced rapid growth, with the structure of its power engineering business gradually shifting from traditional infrastructure construction to more diversified areas such as production and operation, as well as emergency repairs. As a result, the transformation of mechanized construction in power transmission and transformation projects has become increasingly urgent. This article proposes a post-evaluation model based on game theory to improve comprehensive weighting and fuzzy grey relational projection sorting, which can be used to evaluate the optimal mechanized construction scheme for power transmission and transformation projects. The model begins by considering the entire lifecycle of power transmission and transformation projects. It constructs a post-evaluation index system that covers the planning and design stage, on-site construction stage, operation and maintenance stage, and the decommissioning and disposal stage, with corresponding calculation methods for each index. The fuzzy grey correlation projection sorting method is then employed to evaluate and rank the construction schemes. To validate the model’s effectiveness, a case study of a power transmission and transformation project in a specific region of China is used. The comprehensive benefits of three proposed mechanized construction schemes are evaluated and compared. According to the evaluation results, Scheme 1 is ranked the highest, with a membership degree of 0.870945, excelling in sustainability. These results suggest that the proposed model can effectively evaluate and make decisions regarding the optimal mechanized construction plan for power transmission and transformation projects.

Mechanized construction is being fully implemented in the electric power infrastructure domain to ensure construction safety, enhance project quality, and improve efficiency. Traditional methods of designing mechanized construction plans are often inefficient due to their labor-intensive processes and heavy reliance on human expertise. This study introduces and evaluates an ontology-guided system designed to automate mechanized construction planning for power grid projects. The developed ontology effectively models domain-specific knowledge, enabling the semantic integration of data from various sources. By leveraging SPARQL queries, the ontology-guided system incorporates knowledge reasoning capabilities that facilitate the automated selection of construction equipment and the generation of comprehensive construction plans. A prototype system incorporating an ontology-guided mechanism has been developed, showcasing marked enhancements in efficiency and accuracy over traditional manual methods, as evidenced by case studies and expert evaluations. The research results emphasize the potential of ontology-guided systems in innovating architectural planning, providing an extensible and standardized approach. Expert evaluation indicates that the system achieves 71.38% effectiveness in generating mechanized construction plans.

In the mechanized construction of tunnels, due to limitations in mechanical equipment parameters, the distance from the palm face to the inverted arch and secondary lining does not meet the safety distance specified in the regulations. In order to obtain the deformation of surrounding rocks and the stress laws of support structures under the safety distance out of specification of mechanized construction, the paper first analyzed the parameters of mechanized construction equipment and the safety distance of adjacent equipment. The safety distance between the three arm rock drill trolley equipment parking area and the inverted arch was 50 m, and the safety distance to the secondary lining was 150 m. The distance from the palm face to the inverted arch and the secondary lining needs to increase the distance of the blasting flying stone based on the original safety distance. Then, based on the Qilinguan tunnel, numerical simulation was used to study the distance from the palm surface to inverted arch and the secondary lining. The results showed that the deformation difference of surrounding rock and the force difference of supporting structures under different distance from the palm face to the inverted arch and the secondary lining obtained in the paper were small, and the deformation of surrounding rock is within the allowable range of the specification. Finally, the rationality of numerical simulation and the distance from the palm surface to inverted arch and the secondary lining was verified. The research of the paper can provide reference for mechanized tunnel construction.Article HighlightsAccording to the parameters of tunnel mechanized construction equipment and the safety distance between adjacent equipments, the safety distance from the three arm rock drill trolley to the inverted arch is 50 m, and the safety distance to the secondary lining is 150 m.Based on the blasting flying stone distance obtained from the Qilinguan Tunnel, combined with the characteristics of mechanical equipment and the safety distance of adjacent equipments, the safe distances between the palm face and the inverted arch and secondary lining are 80–100 m and 180–200 m, respectively.Based on the actual engineering, the rationality of the safety distance between the palm face and the inverted arch and secondary lining proposed in the paper is verified through numerical simulation and on-site monitoring.

In order to continuously improve the construction capacity of power grid projects, improve the level of construction technology, and promote the transformation and upgrading of construction enterprises, power companies actively promote the mechanized construction of transmission lines. On the basis of summarizing the existing calculation rules of mechanized construction area, through on-site investigation and extensive collection of funds, research and excavate indicators that adapt to the new situation and new regulations. According to the actual area composition of the mechanized construction site, scientific calculation rules are formulated by modeling analysis and other methods. Compare and verify the calculated area of the new rule with the actual compensation area and the calculated area of the original rule, and propose a complete set of calculation rules for mechanized construction area, which has the conditions and value for popularization and application.

In Malaysia, there has been a growing need for mechanization in recent years due to the high construction demand under the Economic Transformation Programme. Onsite mechanization in construction implies the use of equipment and machinery that are powered by fossil fuels with the aim of reducing the dependency on manual labour and to increase productivity, quality and efficiency. However, this heavy equipment and machinery are also responsible for issues pertaining to environment, health and safety of human beings. All nonroad diesel engine machinery emits hazardous gases, particulate matters, solid waste and contaminate land, water and the environment. Prior research on environmental impacts of construction works was more focused on raw materials, its manufacturing and operational phase of the facility. Therefore, the aim of this research is to focus on environmental concerns that are important during construction and mainly due to the operation of onsite heavy construction equipment. Data for the analysis was collected through a questionnaire survey from Malaysian G7 class 'A' contractors. The results of the survey show that despite the good awareness of Malaysian contractors towards a green environment, their practices for controlling energy consumption and greenhouse gases (GHG) are still in their infancy stages. It was also found that environment management systems are not highly implemented by the contractors. The survey results further indicate that energy saving, GHG and black smoke emission are the top ranking concerns of contractors when operating onsite equipment and

Replacing worn disc cutters in a tunnel boring machine (TBM) operation is a time-consuming and expensive process. This article presents the study of disc cutter wear rate during the excavation of 36 km Naqadeh water conveyance tunnel with a diameter of 6.325 m. The construction of this tunnel in northwest Iran has been recently completed. The geological setting of the tunnel consists of variable lithology, including limestone, shale, granite, and granodiorite units. Field data were collected and analyzed from TBM tunneling of 9.5 km of the tunnel. The analysis was performed by incorporating the types of wear and comparing the accuracy of traditional cutter wear prediction models. The new model was developed using statistical analysis of the observed cutter wear in this project. The validity of the proposed model was evaluated using the excavation data for the following 2 km of the tunnel. The model developed in this study allows estimating wear rate and cutter life using common rock characterization parameters such as the Cerchar abrasivity index (CAI) and the uniaxial compressive strength (UCS).

Tunnel boring machine (TBM) performance prediction in mechanized tunneling is an essential factor for selecting an appropriate excavation machine, tunnel design, and safe construction. To implement safe mechanized excavation, it is important to accurately assess and predict the range of machine driving parameters, especially the machine rate of penetration (ROP); this can reduce the cost of TBM repairs due to the abrasion of disc cutters and cutterhead and also has a positive effect on the post-construction period. This study focuses on predicting the ROP of TBMs passing through metamorphic rocks during deep excavation and under a complex geotechnical situation. For this purpose, three fuzzy-based models of the Mamdani fuzzy inference system (MFIS), adaptive neuro-fuzzy inference system (ANFIS), Takagi Sugeno fuzzy model (TSF), as well as linear and non-linear regression models were developed. Historical tunnels were used to compile 189 data points (151 for training and 37 for testing). In the dataset, three parameters, including uniaxial compressive strength (UCS), cutterhead rotational speed per minute (RPM), and thrust force (TF), were considered effective parameters on the TBM’s ROP. According to the findings, the suggested models provided satisfactory and consistent accuracy. Moreover, the results demonstrated that the forecasted values correlate rather well with the measured ones. The proposed algorithms can be considered for use in similar ground and tunneling conditions (metamorphic rocks with low-average strength). It is worth noting that this study has the potential to drastically cut down on tunneling uncertainties and makes fuzzy inference systems a robust algorithm for planning mechanized tunneling.HighlightsPrediction of TBM performance in complex geological conditions.Forecasting TBM performance in deep tunnels passing through metamorphic rocks.Presenting an empirical model for calculating the TBM performance based on statistical analysis.Detailed analysis of fuzzy-based techniques potential for TBM performance prediction.Examining the models’ accuracy with several loss functions and statistical indices.

An accurate estimate of the groundwater inflow to a tunnel is one of the most challenging but essential tasks in tunnel design and construction. Most of the numerical or analytical solutions that have been developed ignore tunnel seepage conditions, material properties and hydraulic-head changes along the tunnel route during the excavation process, leading to inaccurate prediction of inflow rates. A method is introduced that uses MODFLOW code of GMS software to predict inflow rate as the tunnel boring machine (TBM) gradually advances. In this method, the tunnel boundary condition is conceptualized and defined using Drain package, which is simulated by dividing the drilling process into a series of successive intervals based on the tunnel excavation rates. In addition, the drain elevations are specified as the respective tunnel elevations, and the conductance parameters are assigned to intervals, depending on the TBM type and the tunnel seepage condition. The Qomroud water conveyance tunnel, located in Lorestan province of Iran, is 36 km in length. Since the Qomroud tunnel involved groundwater inrush during excavating, it is considered as a good case study to evaluate the presented method. The groundwater inflow to this tunnel during the TBM advance is simulated using the proposed method and the predicted rates are compared with observed rates. The results show that the presented method can satisfactorily predict the inflow rates as the TBM advances.

Exponential increase in urbanization has led to a growing need for a mass rapid transit system in developing countries. Underground network of tunnels constructed using tunnel boring machines (TBMs) has been an efficient solution. Urban tunnels have an essential requirement of minimizing the ground disturbances as it adversely impacts the existing infrastructure. Current study presents potential correlations for parametric geotechnical assessment for mechanized soft ground tunnelling data considering statistical numerical regression. The established correlations essentially enable us to determine and validate the geotechnical parameters in real time and subsequently enables optimization and even potential automation of TBM operations during soft ground mechanized tunnelling.