Technical Analysis of Collapse in Tunnel Excavation and Suggestion of Preventing Appropriate Applicable Methods (Case Study: Sardasht Dam Second Diversion Tunnel)

In order to either optimal use of water resource of KELAS river basin and electrical energy generation, Sardasht Dam and power plant are designed. Dam water diversion system includes two tunnels with inner diameter of 7 m. Several collapses have occurred in portal of second diversion tunnel (A2) which has created large cavity in tunnel crown. In order to prevent collapse, various ways such as steel sets installation and also grouting to increase strength of surrounded rock mass, are prescribed but none of technics could not to ban caving in. considering this fact that in order to continue tunnelling process, collapse zone should be passed, a solution or solutions must be suggested to overcome consecutive and dangerous collapses problem. In order to decrease tunnelling risks, in this research, using both experience and knowledge obtained from previous proposed executive solution to similar cases and technically analysis of occurred collapse in current diversion tunnel, it has been tried to suggest a new appropriate solution which defeat the problem. Finally, in order to stabilize of tunnel crown, as an effective and applicable solution, constructing retaining crown by means of rock bolts, was introduced

Identifying and Ranking of Mechanized Tunneling Project's Risks by Using A Fuzzy Multi-Criteria Decision Making Technique

A tunneling project is one of the most significant infrastructure projects. Its implementation requires access to adequate data and use of unique proceedings; hence it has a special position among civil engineering projects. Unexpected and uncertain conditions in tunneling projects lead to an increase of potential risks during project implementation. Identifying and evaluating risks in tunneling projects are considered one of the significant challenges among civil engineers, which can cause proper risk management during tunnel construction. Therefore, this study aims to evaluate and rank the risks of the second part of the Emamzadeh Hashem tunnel in the north of Iran which was considered as a case study. For this purpose, twelve potential risks were identified by using geological studies and experts. Then, they were evaluated and ranked using effective fuzzy multi-criteria decision-making (FMCDM) techniques, namely fuzzy analytical hierarchical process (FAHP). The three decision variables were considered, including repeat chance, occurrence possibility, and efficacy. The results obtained indicated that the occurrence possibility was the most effective among the decision variables in this case study. In addition, Instability of the wall and lack of contractor’s experiences had the highest and lowest ranks with 0.103 and 0.052, respectively

Assessment of risks of tunneling project in Iran using artificial bee colony algorithm

The soft computing techniques have been widely applied to model and analyze the complex and uncertain problems. This paper aims to develop a novel model for the risk assessment of tunneling projects using artificial bee colony algorithm. To this end, the risk of the second part of the Emamzade Hashem tunnel was assessed and analyzed in seven sections after testing geotechnical characteristics. Five geotechnical and hydrological properties of study zone are considered for the clustering of geological units in front of tunneling project including length of tunnel, uniaxial compressive strength, rock mass rating, tunneling index Q, density and underground water condition. These sections were classified in two low-risk and high-risk groups based on their geotechnical characteristics and using clustering technique. It was resulted that three sections with lithologies Durood Formation, Mobarak Formation, and Ruteh Formation are placed in the high risk group and the other sections with lithologies Baroot Formation, Elika Formation, Dacite tuff of Eocene, and Shear Tuff, and Lava Eocene are placed in the low risk group. In addition, the underground water condition and density with 0.722 and 1 Euclidean distances have the highest and lowest impacts in the high risk group, respectively. Therefore, comparing the obtained results of modelling and actual excavation data demonstrated that this technique can be applied as a powerful tool for modeling risks of tunnel and underground constructions

Assessing the system vibration of circular sawing machine in carbonate rock sawing process using experimental study and machine learning

Predicting the vibration of the circular sawing machine is very important in examining the performance of the sawing process, as it shows the amount of energy consumption of the circular sawing machine. Also, this factor is directly related to maintenance cost, such that with a small increase in the level of vibration, the maintenance cost increases to a large extent. This paper presents new prediction models to assess the vibration of circular sawing machine. An evaluation model based on the imperialist competitive algorithm as one of the most efficient artificial intelligence techniques was used for estimation of sawability of the dimension stone in carbonate rocks. For this purpose, four main physical and mechanical properties of rock including Schimazek's F-abrasivity, uniaxial compressive strength, mean Mohs hardness, and Young's modulus as well as two operational parameters of circular sawing machine including depth of cut and feed rate, were investigated and measured. In the predicted model, the system vibration in stone sawing was considered as a dependent variable. The results showed that the system vibration can be investigated using the newly developed machine learning models. It is very suitable to assess the system vibration based on the mechanical properties of rock and operational properties

STABILITY ANALYSIS OF TUNNEL SUPPORT SYSTEMS USING NUMERICAL AND INTELLIGENT SIMULATIONS (CASE STUDY: KOUHIN TUNNEL OF QAZVIN-RASHT RAILWAY)

Izgradnja podzemnih konstrukcija skup je proces, gdje veliku važnost ima procjena i sprječavanje mogućih rizika. Za tu namjenu razvijene su brojne metode, a u radu je prikazana primjena računskoga modela za ocjenu sustava tunela. Prvo je načinjena numerička analiza na temelju metode konačnih razlika (elemenata) uporabom paketa FLAC2D. Njome je modeliran način iskapanja i postavljanja pratećih instalacija. Predviđena masa iskapanja analizirana je s obzirom na osna opterećenja, moment i silu smicanja. Sve te veličine izračunane su za odabrane potporne točke u krovini, središtu, podini te bočnim zidovima. Kako bi se odredila stabilnost sustava, izdvojena su tri klastera i analizirana meta-heurističkim „Bee Colony” algoritmom (u paketu Matlab). Rezultati klasterizacije uspoređeni su s faktorima sigurnosti potpornoga sustava. Pokazali su kako sigurnosne točke klastera 1 imaju manji sigurnosni faktor negoli one u klasterima 2 i 3. Zaključeno je kako model temeljen na algoritmu „Bee Colony” može biti pouzdano primijenjen za početnu procjenu potpornoga sustava tunela, uzimajući u obzir osna i smična naprezanja te moment sile.According to underground construction development and its high cost process, an accurate assessment and prevention of probable risks are of significant importance. Different methods have been developed to assess underground constructions. In this paper, the aim is to develop a new soft computing model to evaluate tunnel support systems. Firstly, a numerical analysis was performed using the explicit finite difference model by FLAC2D software to excavate a sequence model and support system installation. The design loads including the axial force, moment, and shear force were calculated for some important points of the support system including the crown, the middle of the bottom and the side walls. In order to analyse the stability of the support system, the section points were evaluated into 3 clusters by the artificial bee colony as a meta-heuristic algorithm and a k-means algorithm using Matlab software. The results of clustering were compared by the safety factor of the support system. The results indicated that the section points that are in cluster 1 have a lower safety factor than clusters 3 and 2, respectively. It concluded that the artificial bee colony can be reliably used in the initial assessment of tunnel support systems based on the axial force, moment, and shear force