Search results

Under the repeated action of the construction load, opening deformation and disturbed deformation occurred at the precast box culvert joints of the shield tunnel. The objective of this paper is to investigate the effect of construction vehicle loading on the mechanical deformation characteristics of the internal structure of a large-diameter shield tunnel during the entire construction period.

The structural response of the prefabricated internal structure under heavy construction vehicle loads at four different construction stages (prefabricated box culvert installation, curved lining cast-in-place, lane slab installation and pavement structure casting) was analyzed through field tests and ABAQUS (finite element analysis software) numerical simulation.

Heavy construction vehicles can cause significant mechanical impacts on the internal structure, as the construction phase progresses, the integrity of the internal structure with the tunnel section increases. The vertical and horizontal deformation of the internal structure is significantly reduced, and the overall stress level of the internal structure is reduced. The bolts connecting the precast box culvert have the maximum stress at the initial stage of construction, as the construction proceeds the stress distribution among the bolts gradually becomes uniform.

This study can provide a reference for the design model, theoretical analysis and construction technology of the internal structure during the construction of large-diameter tunnel projects.

The aim of this study is to understand the environmental benefits and economic savings associated with adoption of circular economy in the construction sector. The research findings will support different stakeholders and decision makers to develop business models based on responsible consumption of resources and build sustainable business models.

The research uses mixed methodology wherein inventory for life cycle assessment and life cycle costing for environmental and economic impacts is based on primary data using on-site visits for qualitative and quantitative data.

Different types of land transportation infrastructures are compared for their environmental impacts. It is found that bridges have the highest environmental impacts as compared to tunnels, roads and railways. Further, the results affirm the environmental and economic benefits of adopting circular economy practices.

This is one of a kind research that compares the environmental and economic tradeoffs of adopting circular economy in different types of land transportation infrastructures.

This paper aims to investigate the responses of single piles and pile groups due to tunneling-induced ground movements in a two-layered soil system. The analyses mainly focus on the additional single pile responses in terms of bending moment, lateral deflection, axial force, shaft resistance and pile settlement. Subsequently, a series of parametric studies were carried out to better understand the responses of single piles induced by tunneling. To give further understanding regarding the pile groups, a 2 × 2 pile group with two different pile head conditions, namely, free and capped, was considered.

Using the PLAXIS three-dimensional (3D) software, a full 3D numerical modeling is performed to investigate the effects of ground movements caused by tunneling on adjacent pile foundations. The numerical model was validated using centrifuge test data found in the literature. The relevance of the 3D model is also judged by comparison with the 2D plane strain model using the PLAXIS 2D code.

The numerical test results reveal that tunneling induces significant displacements and internal forces in nearby piles. The magnitude and distribution of internal forces depend mainly on the position of the pile toe relative to the tunnel depth and the distance between the pile and the vertical axis of the tunnel. As the volume loss increases from 1% to 3%, the apparent loss of pile capacity increases from 11% to 20%. By increasing the pile length from 0.5 to 1.5 times, the tunnel depth, the maximum pile settlement and lateral deflection decrease by about 63% and 18%, respectively. On the other hand, the maximum bending moment and axial load increase by about 7 and 13 times, respectively. When the pile is located at a distance of 2.5 times the tunnel diameter (Dt), the additional pile responses become insignificant. It was found that an increase in tunnel depth from 1.5Dt to 2.5Dt (with a pile length of 3Dt) increases the maximum lateral deflection by about 420%. Regarding the interaction between tunneling and group of piles, a positive group effect was observed with a significant reduction of the internal forces in rear piles. The maximum bending moment of the front piles was found to be higher than that of the rear piles by about 47%.

Soil is a complex material that shows differently in primary loading, unloading and reloading with stress-dependent stiffness. This general behavior was not possibly being accounted for in simple elastic perfectly plastic Mohr–Coulomb model which is often used to predict the behavior of soils. Thus, in the present study, the more advanced hardening soil model with small-strain stiffness (HSsmall) is used to model the non-linear stress–strain soil behavior. Moreover, unlike previous studies THAT are usually based on the assumption that the soil is homogeneous and using numerical methods by decoupled loadings under plane strain conditions; in this study, the pile responses have been exhaustively investigated in a two-layered soil system using a fully coupled 3D numerical analysis that takes into account the real interactions between tunneling and pile foundations. The paper presents a distinctive set of findings and insights that provide valuable guidance for the design and construction of shield tunnels passing through pile foundations.

Prefabricated technology is gradually being applied to the construction of subway stations due to its characteristic of mechanization. However, the prefabricated subway station in China is in the initial stage of development, which is prone to construction safety issues. This study aims to evaluate the construction safety risks of prefabricated subway stations in China and formulate corresponding countermeasures to ensure construction safety.

A construction safety risk evaluation index system for the prefabricated subway station was established through literature research and the Delphi method. Furthermore, based on the structure entropy weight method, matter-element theory and evidence theory, a hybrid evaluation model is developed to evaluate the construction safety risks of prefabricated subway stations. The basic probability assignment (BPA) function is obtained using the matter-element theory, the index weight is calculated using the structure entropy weight method to modify the BPA function and the risk evaluation level is determined using the evidence theory. Finally, the reliability and applicability of the evaluation model are verified with a case study of a prefabricated subway station project in China.

The results indicate that the level of construction safety risks in the prefabricated subway station project is relatively low. Man risk, machine risk and method risk are the key factors affecting the overall risk of the project. The evaluation results of the first-level indexes are discussed, and targeted countermeasures are proposed. Therefore, management personnel can deeply understand the construction safety risks of prefabricated subway stations.

This research fills the research gap in the field of construction safety risk assessment of prefabricated subway stations. The methods for construction safety risk assessment are summarized to establish a reliable hybrid evaluation model, laying the foundation for future research. Moreover, the construction safety risk evaluation index system for prefabricated subway stations is proposed, which can be adopted to guide construction safety management.

This research aims to propose a model for the complex decision-making involved in the ecological restoration of mega-infrastructure (e.g. railway engineering). This model is based on multi-source heterogeneous data and will enable stakeholders to solve practical problems in decision-making processes and prevent delayed responses to the demand for ecological restoration.

Based on the principle of complexity degradation, this research collects and brings together multi-source heterogeneous data, including meteorological station data, remote sensing image data, railway engineering ecological risk text data and ecological restoration text data. Further, this research establishes an ecological restoration plan library to form input feature vectors. Random forest is used for classification decisions. The ecological restoration technologies and restoration plant species suitable for different regions are generated.

This research can effectively assist managers of mega-infrastructure projects in making ecological restoration decisions. The accuracy of the model reaches 0.83. Based on the natural environment and construction disturbances in different regions, this model can determine suitable types of trees, shrubs and herbs for planting, as well as the corresponding ecological restoration technologies needed.

Managers should pay attention to the multiple types of data generated in different stages of megaproject and identify the internal relationships between these multi-source heterogeneous data, which provides a decision-making basis for complex management decisions. The coupling between ecological restoration technologies and restoration plant species is also an important factor in improving the efficiency of ecological compensation.

Unlike previous studies, which have selected a typical section of a railway for specialized analysis, the complex decision-making model for ecological restoration proposed in this research has wider geographical applicability and can better meet the diverse ecological restoration needs of railway projects that span large regions.

This study aims to focus on exploring the construction productivity of building projects under the influence of potential factors. The three primary purposes are (1) determining critical factors affecting construction productivity; (2) identifying causal relationship and occurrence probability of these factors to develop a Bayesian network (BN) model; and (3) validating the accuracy of predictions from the proposed BN model via a case study.

A conceptual framework that includes three performance stages was used. Twenty-two possible factors were screened from a comprehensive literature review and evaluated through expert opinions. Data were collected using a structured questionnaire-based survey and case-study-based survey. The sampling methods were based on non-probability sampling.

Worker characteristic-related factors significantly affect labour productivity for a construction task. Construction productivity is dominated by the working frequency of workers (overtime), complexity of the task, level of technology application and accidents. Labour productivity is defined as nearly 50% of the baseline productivity using the BN model created by the caut 2sal relationship and probability of factors. The prediction error of the BN model was 6.6%, 10.0% and 9.3% for formwork (m²/h), reinforcing steel (ton/h) and concrete (m³/h), respectively.

The evaluation or prediction of productivity performance has become a necessary topic for research and practice.

Managers and practitioners in the construction sector can utilise the outcome of this study to create good productivity management policies for their prospective projects.

Worker-related characteristics are dominant among critical factors affecting labour productivity for a construction task; the proposed BN-based predictive model is built based on these critical factors. The BN approach is highly accurate for construction productivity prediction. The findings of this study can fill gaps in the construction management body of knowledge when modelling construction productivity under the effects of multiple factors and using a simple probabilistic graphic tool.

The purpose is to identify and evaluate the safety risk factors in the coal mine construction process.

The text mining technique was applied in the stage of safety risk factor identification. The association rules method was used to obtain associations with safety risk factors. Decision-Making Trial and Evaluation Laboratory (DEMATEL) and Interpretative Structural Modeling (ISM) were utilized to evaluate safety risk factors.

The results show that 18 safety risk factors are divided into 6 levels. There are 12 risk transmission paths in total. Meanwhile, unsafe behavior and equipment malfunction failure are the direct causes of accidents, and inadequate management system is the basic factor that determines the safety risk status.

Due to the limitation of the computational matrix workload, this article only categorizes numerous lexical items into 18 factors. Then, the workshop relied on a limited number of experts; thus, the findings may be potentially biased. Next, the accident report lacks a universal standard for compilation, and the use of text mining technique may be further optimized. Finally, since the data are all from China, subsequent cross-country studies should be considered.

The results can help China coal mine project managers to have a clear understanding of safety risks, efficiently carry out risk hazard identification work and take timely measures to cut off the path of transmission with risks identified in this study. This helps reduce the economic losses of coal mining enterprises, thus improving the safety standards of the entire coal mining industry and the national standards for coal mine safety policy formulation.

Coal mine construction projects are characterized by complexity and difficulties in construction. Current research on the identification and assessment of safety risk factors in coal mine construction is insufficient. This study combines objective and systematic research approaches. The findings contribute to the safety risk management of China coal mine construction projects by providing a basis for the development of safety measures.

Accurate prediction of the structural condition of urban critical infrastructure is crucial for predictive maintenance. However, the existing prediction methods lack precision due to limitations in utilizing heterogeneous sensing data and domain knowledge as well as insufficient generalizability resulting from limited data samples. This paper integrates implicit and qualitative expert knowledge into quantifiable values in tunnel condition assessment and proposes a tunnel structure prediction algorithm that augments a state-of-the-art attention-based long short-term memory (LSTM) model with expert rating knowledge to achieve robust prediction results to reasonably allocate maintenance resources.

Through formalizing domain experts' knowledge into quantitative tunnel condition index (TCI) with analytic hierarchy process (AHP), a fusion approach using sequence smoothing and sliding time window techniques is applied to the TCI and time-series sensing data. By incorporating both sensing data and expert ratings, an attention-based LSTM model is developed to improve prediction accuracy and reduce the uncertainty of structural influencing factors.

The empirical experiment in Dalian Road Tunnel in Shanghai, China showcases the effectiveness of the proposed method, which can comprehensively evaluate the tunnel structure condition and significantly improve prediction performance.

This study proposes a novel structure condition prediction algorithm that augments a state-of-the-art attention-based LSTM model with expert rating knowledge for robust prediction of structure condition of complex projects.

Major incidents in tunnel environment will pose several challenges for the emergency service organisations in terms of heat, visibility and lack of experiences from working in confined environments. These aspects, in turn, could pose challenges to establish collaboration. This study aims to contribute to the field of collaborative tunnel responses by exploring how “common knowledge” (Edwards, 2011) is built by the emergency services organisations, that is, what the organisations consider important while working on a potentially common problem, and their motives for the interpretations and actions if a major tunnel incident occurs.

Participants from the road traffic control centre, emergency dispatch centre, emergency medical service, rescue service and police were included in the study. Data from four focus group sessions was analysed using thematic analysis.

The study revealed that the tunnel environment presents specific aspects of how common knowledge was produced related to lifesaving and safety. The themes structuring mechanisms to reduce uncertainty, managing information for initial priorities, aligning responsibilities without hampering each other's work and adjusting actions to manage distance, illustrated how common knowledge was produced as crucial aspects to a collaborative response. Organising management sites, grasping and communicating risks, accessing the injury victims, was challenged by the confined environment, physical distances and imbalance in access to information and preparedness activities in tunnel environments.

This study offers new insights of common knowledge, by illustrating a motive perspective on collaborative responses in tunnel incidents. Creating interoperability calls not just for readiness for action and tunnel safety, but also training activities acknowledging different interpretations and motives to further develop tunnel responses.

The construction industry is complex, human-intensive and driven by monetary values. Hence, disputes are widespread. Initial conflicts among parties may develop into a disastrous dispute that costs the project success and good relationships and affects stakeholders' expectations. There has been a focus on causes of construction-related disputes, and studies over the past three decades have attempted to identify a more comprehensive list of reasons for disputes. Some of these studies' limitations were geographical, project delivery methods and project types. The purpose of this study is to identify the most recent and conclusive list of causes of disputes based on current literature by undertaking a systematic literature review (SLR).

Considering the large number of studies that focused on causes of disputes, this study aims to develop a comprehensive list of causes, using a SLR, as it ensures that all previous articles in multiple databases are reviewed to produce a comprehensive outcome. A six-stage SLR was followed from background study to analysis and reporting.

Not surprisingly, the number of publications has increased over time, most from the Middle East region. The interconnected nature of the causes was widely emphasised. The SLR has produced eight common core causes of disputes. They are: poor contractual arrangements, employer-initiated scope changes, unforeseen site changes, poor contract understanding and administration, contractor’s quality of works, the inability of the contractor to achieve time targets, non- or delayed payments and poor quality of design. The majority of previous authors realised that disputes could be avoided by parties’ involvement during the early stages, avoiding being opportunistic and acting collaboratively.

Even though numerous studies have been carried out to identify the causes of disputes in the construction industry, none did a SLR. This study aggregates all the previous studies that focused on construction-related disputes systematically. Categorising causes based on the party primarily responsible help various stakeholders by providing a distinct list of factors to avoid that contribute to disputes.