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11 |
Distribution laws of freeze-thaw cycles and unsaturated concrete experiments in cold-region tunnels
قوانین توزیع چرخه انجماد و ذوب و آزمایش های بتن اشباع نشده در تونل های منطقه سرد-2020 Studying tunnel temperature fields could prevent frost damage. However, few studies have revealed the distribution
laws of freeze-thaw cycles in cold-region tunnels. In this study, the distribution laws of freeze-thaw
cycles in a tunnel were carried out, and the concrete deterioration induced by freeze-thaw cycling was explored.
First, in situ monitoring equipment was used to collect the temperature in the longitudinal direction of the
tunnels. A one-dimensional heat transfer model was used to analyze the temperature distribution in the circumferential
direction of the tunnels, where a function was proposed to describe both the annual and diurnal
temperature fluctuations. After that, the distribution laws of freeze-thaw cycles inside a tunnel were investigated.
Specifically, in the tunnel longitudinal direction, the number of freeze-thaw cycles decreased from
the entrance to the middle and then increased while approaching the exit, thereby exhibiting a V-shaped distribution.
In the circumferential direction, the intrados lining nearly always exhibited freeze-thaw cycles. The
number of freeze-thaw cycles and the temperature amplitude decreased rapidly with increasing depth.
Furthermore, a series of unsaturated concrete experiments were performed to explore the concrete deterioration
under different numbers of freeze-thaw cycles. The results showed that after being subjected to 100 freeze-thaw
cycles, the concrete specimens displayed visible damage, whereas the concrete compressive strength was not
affected. These findings can enrich the research of freeze-thaw cycles for cold-region tunnels and are significant
for guiding tunnel maintenance. Keywords: Cold-region tunnels | Temperature field | Freeze-thaw cycles | Unsaturated concrete | Numerical simulation |
مقاله انگلیسی |
12 |
Integrating domain knowledge with deep learning models: An interpretable AI system for automatic work progress identification of NATM tunnels
ادغام دانش دامنه با مدل های یادگیری عمیق: یک سیستم هوش مصنوعی قابل تفسیر برای شناسایی پیشرفت کار خودکار تونل های NATM-2020 Finding a reliable and cost-effective approach to monitor the activities of the New Austrian Tunneling Method
(NATM) tunnel construction automatically is a challenging yet important task. This study presents an interpretable
artificial intelligence (AI) framework that automatically identifies NATM construction works using lowcost
site surveillance images. The framework adopts the Bayesian statistics to combine the prior NATM construction
knowledge with the visual evidence extracted by deep learning (DL) based computer vision models.
The analysis results of Site CCTV surveillance videos of four NATM tunneling projects are presented to demonstrate
its ability (i) to label NATM work cycles from the work timeline, (ii) to identify NATM work categories
inside each work cycle, and (iii) to estimate the degree of plan-work deviation at the construction cycle level.
The proposed framework yields promising results on a real NATM tunneling project. Keywords: Artificial intelligence | NATM project monitoring | Deep learning | Computer vision |
مقاله انگلیسی |
13 |
Data-driven safety enhancing strategies for risk networks in construction engineering
راهبردهای افزایش ایمنی داده محور برای شبکه های ریسک در مهندسی ساخت -2020 Risk management is crucial and indispensable to the success of projects, while identifying critical risks is the
fundamental step in devising the corresponding safety measures. To fully exploit the value of richly accumulated
accidental cases, this paper presents a data-driven research framework for proposing effective safety enhancing
strategies based on risk networks in construction engineering, spanning the whole process from extracting accident
chains from accidents to construct a risk network to devising safety measures. Aiming at the weighted
heterogeneity of the risk network, both the performance metrics at network level and critical-risk identification
metrics at node level are deliberately designed. These metrics then enable the proposing of a series of safetyenhancing
strategies. In the case study, based on the accident-related data in China’s bridge-and-tunnel hybrid
projects, different safety-enhancing strategies are compared through simulation experiments and analyzed to
verify their effectiveness on optimizing costs and improving safety. Finally, based on results from simulations,
relevant managerial suggestions are proposed. Keywords: Safety enhancing strategies | Risk network | Data-driven | Construction engineering |
مقاله انگلیسی |
14 |
Reinforcement learning based optimizer for improvement of predicting tunneling-induced ground responses
بهینه ساز مبتنی بر یادگیری تقویتی برای بهبود پیش بینی پاسخ های ناشی از tunneling-2020 Prediction of ground responses is important for improving performance of tunneling. This study proposes a novel
reinforcement learning (RL) based optimizer with the integration of deep-Q network (DQN) and particle swarm
optimization (PSO). Such optimizer is used to improve the extreme learning machine (ELM) based tunnelinginduced
settlement prediction model. Herein, DQN-PSO optimizer is used to optimize the weights and biases of
ELM. Based on the prescribed states, actions, rewards, rules and objective functions, DQN-PSO optimizer
evaluates the rewards of actions at each step, thereby guides particles which action should be conducted and
when should take this action. Such hybrid model is applied in a practical tunnel project. Regarding the search of
global best weights and biases of ELM, the results indicate the DQN-PSO optimizer obviously outperforms
conventional metaheuristic optimization algorithms with higher accuracy and lower computational cost.
Meanwhile, this model can identify relationships among influential factors and ground responses through selfpracticing.
The ultimate model can be expressed with an explicit formulation and used to predict tunnelinginduced
ground response in real time, facilitating its application in engineering practice. Keywords: Tunnel | Ground response | Reinforcement learning | Extreme learning machine | Optimization |
مقاله انگلیسی |
15 |
Dynamic luminance tuning method for tunnel lighting based on data mining of real-time traffic flow
روش تنظیم پویا درخشندگی برای روشنایی تونل بر اساس داده کاوی جریان ترافیک در زمان واقعی-2020 Tunnel lighting constitutes one of the major expenses incurred in transportation lighting, and hence substantial
research has been conducted to improve the efficiency of lighting and thus to minimize operating costs. This
paper investigates an intelligent method for adjusting tunnel lighting with dynamic control based on data mining
of traffic flow distribution, traffic composition, and vehicle speed distribution. Field monitoring data of traffic
flow in five real expressway tunnels, which are in HeDa expressway, Jilin Province, China, was used in the
analysis. The K-MEANS clustering algorithm was used to group (or cluster) the distribution of daily traffic
volume into six-time periods, in which the traffic volume includes two peak periods (8:01–11:23 and
14:31–19:01). A dynamic luminance regulation method is proposed that distinguishes operational strategies
under different time periods. Furthermore, the impact of tunnel length and traffic flow on the effect of energysaving
and system sustainability of the proposed method was assessed. The results show that when using the
proposed method, the energy-savings in tunnel lighting could be between about 50% and 60% for a daily traffic
volume between 750 and 2500 vehicles. The results also show that the switching frequency of the lighting system
is significantly reduced, which would significantly enhance the sustainability of the lighting system. Keywords: Data mining | Energy management | Intelligent control | Tunnel lighting |
مقاله انگلیسی |
16 |
Prediction of Disc Cutter Life during Shield Tunneling with AI via the Incorporation of a Genetic Algorithm into a GMDH-Type Neural Network
پیش بینی عمر برش دیسک در حین تونل سازی سپر با هوش مصنوعی از طریق ادغام الگوریتم ژنتیک در شبکه عصبی نوع GMDH-2020 Disc cutter consumption is a critical problem that influences work performance during shield tunneling processes and directly affects the cutter
change decision. This study proposes a new model to estimate the disc cutter life (Hf) by integrating a group method of data handling (GMDH)-type
neural network (NN) with a genetic algorithm (GA). The efficiency and effectiveness of the GMDH network structure are optimized by the GA,
which enables each neuron to search for its optimum connections set from the previous layer. With the proposed model, monitoring data including
the shield performance database, disc cutter consumption, geological conditions, and operational parameters can be analyzed. To verify the
performance of the proposed model, a case study in China is presented and a database is adopted to illustrate the excellence of the hybrid model. The
results indicate that the hybrid model predicts disc cutter life with high accuracy. The sensitivity analysis reveals that the penetration rate (PR) has a
significant influence on disc cutter life. The results of this study can be beneficial in both the planning and construction stages of shield tunneling. Keywords: Disc cutter life | Shield tunneling | Operational parameters | GMDH–GA |
مقاله انگلیسی |
17 |
Time-dependent behaviour of the Callovo-Oxfordian claystone-concrete interface
رفتار وابسته به زمان رابط سنگ خشتی و بتونی Callovo - Oxfordian-2020 In the context of the Cigéo project, the French National Radioactive Waste Management Agency (Andra)
is studying the behaviour of a deep geological facility for radioactive waste deposit in the Callovo-
Oxfordian (COx) claystone. The assessment of durability of this project requires the prediction of irreversible
strain over a large time scale. The mechanical interaction of the host rock and the concrete
support of tunnels must be investigated to ensure the long-term sustainability of the structure. The
instantaneous and time-dependent behaviour of the claystone-concrete interface is experimentally
investigated with direct shear tests and long-duration shear tests of a few months. The mechanical and
structural state of the claystone which is affected after interaction with concrete reflects to the response
of the claystone-concrete interface, and thus different types of COx claystone-concrete interfaces are
tested. The delayed deformation of the interface is found to be linked to the level of the normal loading
and the loading history, while a different response of the interface was observed from the short- and
long-duration tests, indicating a possible progressive modification of interface under long-duration
loadings. Keywords: Callovo-Oxfordian (COx) claystone | Interfaces | Time-dependent behaviour |
مقاله انگلیسی |
18 |
Enforceability and the effectiveness of laws and regulations
قابلیت اجرا و اثربخشی قوانین و مقررات-2020 A major threat to the development of financial markets in emerging markets is “tunneling.” In
China, this took on the form of controlling shareholders diverting assets from listed firms or
coercing firms to serve as guarantors on questionable loans. A new set of rules enacted in 2005
prohibited asset diversion for “non-operational” purposes. Firms complying with these rules have
experienced a reduction in related party transactions, an increase in investment, and better
performance. In contrast, another set of contemporary rules, which aimed to standardize the
practice of firms providing loan guarantees, has had very little impact. We attribute the contrasting
design, implementation, and effectiveness of these two sets of rules to the difference in
enforcement costs of the two types of tunneling activities. Relative to loan guarantees, it is much
easier for a third party to determine (ex ante) whether a particular form of diversion destroys firm
value, and to verify (ex post) that the losses to the firm resulted from the diversion. Our results
highlight the importance of enforceability—laws and regulations that can be enforced at lower
costs are more likely to succeed, especially in countries with underdeveloped formal institutions. Keywords: Enforceability | Controlling shareholder | Tunneling | Loan guarantee | Asset diversion |
مقاله انگلیسی |
19 |
TBM penetration rate prediction based on the long short-term memory neural network
پیش بینی سرعت نفوذ TBM بر اساس شبکه عصبی حافظه کوتاه مدت-2020 Tunnel boring machines (TBMs) are widely used in tunnel engineering because of their safety and efficiency. The TBM penetration
rate (PR) is crucial, as its real-time prediction can reflect the adaptation of a TBM under current geological conditions and assist the
adjustment of operating parameters. In this study, deep learning technology is applied to TBM performance prediction, and a PR prediction
model based on a long short-term memory (LSTM) neuron network is proposed. To verify the performance of the proposed
model, the machine parameters, rock mass parameters, and geological survey data from the water conveyance tunnel of the Hangzhou
Second Water Source project were collected to form a dataset. Furthermore, 2313 excavation cycles were randomly composed of training
datasets to train the LSTM-based model, and 257 excavation cycles were used as a testing dataset to test the performance. The root mean
square error and the mean absolute error of the proposed model are 4.733 and 3.204, respectively. Compared with Recurrent neuron
network (RNN) based model and traditional time-series prediction model autoregressive integrated moving average with explanation
variables (ARIMAX), the overall performance on proposed model is better. Moreover, in the rapidly increasing period of the PR,
the error of the LSTM-based model prediction curve is significantly smaller than those of the other two models. The prediction results
indicate that the LSTM-based model proposed herein is relatively accurate, thereby providing guidance for the excavation process of
TBMs and offering practical application value. Keywords: TBM performance prediction | Penetration rate | Long short-term memory | Water conveyance tunnel |
مقاله انگلیسی |
20 |
Estimation of natural asbestos content in rocks by fracture network modeling and petrographic characterization
برآورد محتوای آزبست طبیعی در سنگها با استفاده از مدل سازی شبکه شکستگی و خصوصیات پتروگرافی-2020 Asbestos may constitute a severe health risk when meta-ophiolites are excavated for large infrastructural projects.
For public acceptance, a reliable estimation of the content of Naturally Occurring Asbestos (NOA) is necessary
for the design of construction sites, workers safety and spoil management. In the framework of a research
project supporting the final design of a highway tunnel system in NW Italy, SEM-EDS (Scanning Electron
Microscopy – Energy-Dispersive Spectrometry) quantitative analyses were performed to provide a direct NOA
content estimation by counting and weighing the asbestos fibers in the rocks, after a chemical and geometrical
characterization. The direct NOA content estimation was compared with an indirect estimation obtained through
a fracture network modeling based on a structural survey on a selected outcrop and statistical analysis of a
relative digital image. The fracture intensity, inferred from the fracture network model, was multiplied by
coefficients deriving from the semi-quantitative estimation of the geological relations between asbestos mineral
occurrence and fracture size, thickness and distribution. A good agreement between the indirect NOA estimation
and the average result of the SEM-EDS analysis was obtained. Thus, the statistical analysis of the fracture network
may represent a valuable support to the SEM-EDS quantitative analysis based on mineral fibers counting.
However, the quality of the indirect NOA estimation depends on the postulates for inferring the coefficients
describing the distribution and occurrence of the asbestos minerals within the fractures. This Note discusses the
above-mentioned issues, as well as those concerning the procedure for a representative sampling of NOA-bearing
rocks and fractures. Keywords: Naturally occurring asbestos | Fracture network modeling | Meta-ophiolites | Geological hazard |
مقاله انگلیسی |