Tunnel flexibility effect on the ground surface acceleration response

Flexibility of underground structures relative to the surrounding medium, referred to as the flexibility ratio, is an important factor that influences their dynamic interaction. This study investigates the flexibility effect of a box-shaped subway tunnel, resting directly on bedrock, on the ground surface acceleration response using a numerical model verified against dynamic centrifuge test results. A comparison of the ground surface acceleration response for tunnel models with different flexibility ratios revealed that the tunnels with different flexibility ratios influence the acceleration response at the ground surface in different ways. Tunnels with lower flexibility ratios have higher acceleration responses at short periods, whereas tunnels with higher flexibility ratios have higher acceleration responses at longer periods. The effect of the flexibility ratio on ground surface acceleration is more prominent in the high range of frequencies. Furthermore, as the flexibility ratio of the tunnel system increases, the acceleration response moves away from the free field response and shifts towards the longer periods. Therefore, the flexibility ratio of the underground tunnels influences the peak ground acceleration (PGA) at the ground surface, and may need to be considered in the seismic zonation of urban areas.     

Integrated rainfall–runoff and flood inundation modeling for flash flood risk assessment under data scarcity in arid regions: Wadi Fatimah basin case study,Saudi Arabia

This paper presents a proposed integrated approach for flood hazardous evaluation in arid and semi-arid areas. Wadi Fatimah in Saudi Arabia is utilized for implementation of such an approach. The approach consists of four stages. In the first stage, a statistical analysis of rainfall data is performed to determine the design storms at specified return periods. In the second stage, geological and geomorphologic analyses are followed to estimate the geomorphic parameters. The third stage concerned with land use and land cover analyses linked with hydrological analysis to estimate the hydrographs. The fourth stage is related to the delineation of the inundation area under two scenarios: the presence and absence of the dam. The statistical analysis proved that some rainfall stations do not follow a Gumbel distribution. The presence of the dam reduces the inundation depth by about 10 %. The reduction in the inundation area due the presence of the dam is about 25 %.     

Rainfall and rainy days trend in Iran

In this study, long-term annual and monthly trends in rainfall amount, number of rainy days and maximum precipitation in 24?h are investigated based on the data collected at 33 synoptic stations in Iran. The statistical significance of trend and climate variability is assessed by the Mann-Kendall test. The Linear trend analysis and the Mann-Kendall test indicate that there are no significant linear trends in monthly rainfall at most of the synoptic stations in Iran. However, the maximum number of stations with negative trends have been observed in April (29 station), and then in May (21 stations) and February (21 stations) and with positive trends in December (26 stations) and July (24 stations). The significant linear trends, with a significant level of 0.05, in annual rainfall have been noticed only at five stations. The monthly number of rainy days does not show any significant linear trend for most areas in Iran. The maximum number of stations with monthly negative trends in rainy days has also been observed in April with the minimum in December. In April, out of 24 stations with negative trends, 12 stations have a significant negative trend. Contrary to that, in October there is no significant linear trend. Most stations have positive trends in annual number of rainy days. Also, the monthly maximum precipitation in 24?h does not show any significant linear trend for most areas in Iran. The maximum number of stations with monthly negative trends in maximum precipitation has also been observed in February with the minimum in December. In spite of that, there are almost no significant precipitation variations in Iran during the last 50-odd years, the tendency of decreasing rainfall amount in April and increasing rainfall amount in December and July could indicate an eventual climate change in this area in the future.     

Roadheader performance prediction using genetic programming (GP) and gene expression programming (GEP) techniques

Roadheading machines play a vital role in excavation operation in tunneling and mining industries notably when selective mining is required. Roadheaders are more effective in soft to medium rock formations due to a higher cutting rate in such strata. A precise prediction of machine’s performance is a crucial issue, as it has considerable effects on excavation planning, project’s cost estimation, machine specification selection as well as safety of the project. In this research, a database of machine performance and some geomechanical parameters of rock formations from Tabas coal mine project, the largest and fully mechanized coal mine in Iran, has been established, including instantaneous cutting rate (ICR), uniaxial compressive strength, Brazilian tensile strength, rock quality designation, influence of discontinuity orientation (Alpha angle) and specific energy. Afterward, the parameters were analyzed through genetic programming (GP) and gene expression programming (GEP) approaches to yield more accurate models to predict the performance of roadheaders. As statistical indices, coefficient of determination, root mean square error and variance account were used to evaluate the efficiency of the developed models. According to the obtained results, it was observed that developed models can effectively be implemented for prediction of roadheader performance. Moreover, it was concluded that performance of the GEP model is better than the GP model. A high conformity was observed between predicted and measured roadheader ICR for GEP model.     

Impacts of karst phenomena on engineering properties of limestone foundation bed,Ar Riyadh,Saudi Arabia

Evaluation of mechanical and petrophysical properties of the karst limestone became essential to avoid future risks in the construction of new urban cities built on limestones. Therefore, this study aims to evaluate the impact of karsts phenomena on engineering properties of limestone foundation bed at Ar Riyadh in Saudi Arabia. Three hundred core plugs were obtained by rotary drilling at depths ranging from the ground surface to 20 m collected from 24 boreholes in two sites: (1) karst limestone (KL) at Al Aziziyah district and (2) massive hard limestone (HL) at Hittin district in Ar Riyadh city, Saudi Arabia. Petrographic, SEM, EDX, and XRD analyses are used to identify the mineralogical composition and microstructures of limestone samples. The petrophysical properties included the ovendry density, P-wave, and porosity where the mechanical properties covered the uniaxial compressive strength (UCS), point load strength index (PLI), and rock quality designation (RQD) for the karst and hard limestone samples. KL is characterized by 17.11% total porosity, 14.71% water absorption, 32.1 MPa UCS, 1.70 g/cm³ ovendry density (γ dry), 51% weathered RQD, 5.49 MPa medium shear strength, and low modulus of deformation of the plate loading test. HL showed 11.63% total porosity, 9.45% water absorption 43.1 MPa UCS, 2.50 g/cm³ ovendry density (γ dry), 78% hard to fresh rock affinity RQD, and 9.93 MPa high strength and high modulus of deformation of the plate loading test. For the water absorption (%), KL at Al Aziziyah district showed a range of 12.85–17.80% averaged 14.71%. HL at Hittin district varied between 7.04 and 11.29% with an average of 9.45%. KL proved to be dense with ovendry density (γ dry) averaged at 1.70 g/cm³ while HL showed very dense affinity of 2.50 g/cm³. KL clarified a UCS range from 22.5 to 40.1 MPa and an average of 32.1 MPa while HL showed a range from 35.4 t o 48.1 MPa with an average of 43.1 MPa. KL is moderately weathered with RQD average of 51% while HL showed a hard to fresh rock affinity of 78%. Point load test clarified a medium shear strength with 5.49 MPa for KL and high strength of 9.93 MPa for HL. Plate loading tests indicated low and high modulus of deformation for KL and HL, respectively. Results of petrographical analyses and XED of limestone samples showed that the strength parameters of samples mostly composed of micrite (mudstone/wackestone) and dolomite in hard limestone of Hittin district. In Al Aziziyah district (KL), the samples mostly consist of foraminifera and high amount of calcite as in karst limestone (wackestone/packstone). Rock mechanical tests with combination of fabric analyses have shown that strength parameters depend not only on the amount of karst but also on the amount of allochem. Major geomechanical differences between the two types of limestone provide the proper base for prioritizing areas to alleviate future risks and sustainable urban planning for decision makers. The karstic limestone, therefore, is considered as an acceptable foundation bed for light engineering structures. However, for heavy structures and buildings, improving the foundation bed strength by grouting, cement injection, and mat foundations is necessary to avoid future failure risks.     

An improved classification of mineralized zones using particle swarm optimization: A case study from Dagh-Dali ZnPb (±Au) prospect,Northwest Iran

Classification of mineralized areas into different geochemical classes in terms of prospectivity is crucial in the optimal management of exploration risk and costs. Machine learning (ML) algorithms can be served as appropriate alternatives for separating ore-related anomalies due to avoiding the assumptions of statistical distribution and compatibility with the multivariate nature of geochemical features. By hybridizing the ML with a metaheuristic algorithm called particle swarm optimization (PSO), this contribution aims to provide an innovative approach to optimize the classification of geochemical anomalies within the study area. The algorithm, PSO, is inspired by simulating the social behavior of flocks of birds in search of food. The Dagh-Dali ZnPb (±Au) mineral prospect in northwest Iran was subjected as a case study to examine the integrity of the proposed method. Mineralization-related features were extracted by applying principal component analysis (PCA) on metallogenic elements analyzed in soil samples as PC1 and PC2 with elemental assemblages of AgAuPbZn and PbZn, respectively. The silhouette index was employed to estimate the number of underlying geochemical clusters within the adopted feature space. To constitute a comparative analysis, two k-means clustering and PSO-based learning (PSO-L) algorithms were implemented to classify the gridded data of PC1 and PC2 within the study area. The results indicated that the use of PSO has improved the cost function of the clustering problem (up to 4%). Adapting the mineralization classes with the metallogenic evidence demonstrated by boreholes drilled in the study area indicated that PSO-L was superior to the traditional k-means method, improving the accurate estimation of subsurface mineralization classes by 7%. By overcoming the drawbacks of conventional methods for trapping at the local optima, PSO-based learning possesses the potential to highlight weak mineralization signals that are numerically located in boundary conditions. The results show that the proposed approach can serve as an effective medium for optimal modeling of geochemical classes and management of detailed exploration operations.     

Risk assessment of water erosion for the Qareh Aghaj subbasin, southern Iran

South of the Zagros belt, the entire land of Southern Iran faces problems arising out of various types of land degradation of which water erosion forms a major type. A new model has been developed for assessing the risk of water erosion. Taking into consideration nine indicators of water erosion the model identifies areas with ‘Potential Risk’ (risky zones) and areas of ‘Actual Risk’ as well as projects the probability of the worse degradation in future. The Qareh Aghaj subbasin (1,265,000 ha), which covers the upper reaches of Mond River, has been chosen for a test risk assessment of this kind. The preparation of risk maps based on the GIS analysis of these indicators will be helpful for prioritizing the areas to initiate remedial measures. The different kinds of data for indicators of water erosion were gathered from the records and published reports of the governmental offices of Iran. By fixing the thresholds of severity classes of the nine indicators a hazard map for each indicator was first prepared in GIS. The risk classes were defined on the basis of risk scores arrived at by assigning the appropriate attributes to the indicators and the risk map was prepared by overlaying nine hazard maps in the GIS. Areas under potential risk have been found to be widespread (63%) in the basin and when classified into subclasses with different probability levels the model projects a statistical picture of the risk of land degradation.     

EXPERIMENTAL STUDY ON BED SCOUR IN A 90°CHANNEL BEND

The special feature of bend flow leads to scouring of the bed and bank. Various parameters like flow depth, flow velocity or discharge, geometry of bend and characteristics of bed material may affect the scour process. Experiments were carried out to study the effect of some important parameters on bend scour under clear water condition. Experiments were conducted in a 0.6m wide and 0.7m high flume with 90 degree bend. The lateral variations of bed slope were studied. The maximum depth of scour was correlated to densimetric Froude number, relative bend radius and relative depth of flow.     

Landslide susceptibility assessment in the Anfu County,China: comparing different statistical and probabilistic models considering the new topo-hydrological factor (HAND)

The present study is aimed at producing landslide susceptibility map of a landslide-prone area (Anfu County, China) by using evidential belief function (EBF), frequency ratio (FR) and Mahalanobis distance (MD) models. To this aim, 302 landslides were mapped based on earlier reports and aerial photographs, as well as, carrying out several field surveys. The landslide inventory was randomly split into a training dataset (70%; 212landslides) for training the models and the remaining (30%; 90 landslides) was cast off for validation purpose. A total of sixteen geo-environmental conditioning factors were considered as inputs to the models: slope degree, slope aspect, plan curvature, profile curvature, the new topo-hydrological factor termed height above the nearest drainage (HAND), average annual rainfall, altitude, distance from rivers, distance from roads, distance from faults, lithology, normalized difference vegetation index (NDVI), sediment transport index (STI), stream power index (SPI), soil texture, and land use/cover. The validation of susceptibility maps was evaluated using the area under the receiver operating characteristic curve (AUROC). As a results, the FR outperformed other models with an AUROC of 84.98%, followed by EBF (78.63%) and MD (78.50%) models. The percentage of susceptibility classes for each model revealed that MD model managed to build a compendious map focused at highly susceptible areas (high and very high classes) with an overall area of approximately 17%, followed by FR (22.76%) and EBF (31%). The premier model (FR) attested that the five factors mostly influenced the landslide occurrence in the area: NDVI, soil texture, slope degree, altitude, and HAND. Interestingly, HAND could manifest clearer pattern with regard to landslide occurrence compared to other topo-hydrological factors such as SPI, STI, and distance to rivers. Lastly, it can be conceived that the susceptibility of the area to landsliding is more subjected to a complex environmental set of factors rather than anthropological ones (residential areas and distance to roads). This upshot can make a platform for further pragmatic measures regarding hazard-planning actions.     

Impact of flash flood recharge on groundwater quality and its suitability in the Wadi Baysh Basin,Western Saudi Arabia: an integrated approach

An integrated approach was used to evaluate the impact of flash flood recharge on groundwater quality and its suitability for drinking, irrigation, livestock and poultry uses in the Wadi Baysh Basin, Western Saudi Arabia. Analyses of 182 groundwater samples, collected from the study area before and after a flash flood (FF) event, show that the average concentrations of TDS, Mg, Na, Cl, NO₃ and EC decreased significantly after the event. The major water types (mixed CaMgCl, NaCl and CaCl) indicate that the infiltration of surface water from FF recharge has a great influence on groundwater chemistry. Drinking water suitability maps, created using WHO standards, indicate that wells located in the upstream region are suitable for drinking despite their high TDS and total hardness (TH) values. Groundwater in the coastal region is unsuitable due to its high salinity, high TH and high concentrations of major ions. The suitability of groundwater for irrigational use was assessed using salinity, sodium adsorption ratio, bicarbonate hazard, residual sodium carbonate, Kelly’s ratio, magnesium hazard, sodium percentage and permeability index values, which indicated that groundwater in the study region is suitable for most soils and crops. After FF, groundwater quality is improved by dilution, especially in the downstream region. USSL classification shows that the majority of the water samples are in the C3S1, C4S2, and C3S2 classes and are therefore suitable for the irrigation of salt-tolerant crops. Irrigational suitability maps suggest that wells in the upstream region are suitable for irrigation, whereas wells located near to the coast are unfit for irrigation. This study implies that construction of check dams in the dry valleys (wadies) may improve the groundwater quality in the area.