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