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As climate change affects precipitation patterns, urban infrastructure may become more vulnerable to flooding. Flooding mitigation strategies must be developed such that the failure of infrastructure does not compromise people, activities, or other infrastructure. “Safe-to-fail” is an emerging paradigm that broadly describes adaptation scenarios that allow infrastructure to fail but control or minimize the consequences of the failure. Traditionally, infrastructure is designed as “fail-safe” where they provide robust protection when the risks are accurately predicted within a designed safety factor. However, the risks and uncertainties faced by urban infrastructure are becoming so great due to climate change that the “fail-safe” paradigm should be questioned. We propose a framework to assess potential flooding solutions based on multiple infrastructure resilience characteristics using a multi-criteria decision analysis (MCDA) analytic hierarchy process algorithm to prioritize “safe-to-fail” and “fail-safe” strategies depending on stakeholder preferences. Using urban flooding in Phoenix, Arizona, as a case study, we first estimate flooding intensity and evaluate roadway vulnerability using the Storm Water Management Model for a series of downpours that occurred on September 8, 2014. Results show the roadway types and locations that are vulnerable. Next, we identify a suite of adaptation strategies and characteristics of these strategies and attempt to more explicitly categorize flooding solutions as “safe-to-fail” and “fail-safe” with these characteristics. Lastly, we use MCDA to show how adaptation strategy rankings change when stakeholders have different preferences for particular adaptation characteristics.

The purpose of this study is to experimentally estimate the criteria for shallow landslide occurrence using hydrological indicators such as matric suction and volumetric water content for representative soils with the different geological conditions in which landslides frequently occur in South Korea. To investigate the detection criteria for shallow landslides, a series of landslide model tests are conducted using weathered soils obtained from regions of granite, gneiss, and mudstone where landslides occur. A landslide model test device, which includes a rainfall simulator, a slope model flume, and a measurement system with sensors, is developed to simulate shallow landslides that generally occur on natural slopes during rainfall. Based on the results of the model test, an infinite slope stability analysis considering the suction stress of unsaturated soil is applied to analyze changes in the safety factor of the slopes according to rainfall. Using the domestic standard of slope design used in South Korea, landslide detection criteria based on the safety factor of slopes are recommended as 1.3 for attention-level alerts and 1.0 for warning-level alerts. The matric suction corresponding to the attention and warning levels is defined as the critical matric suction, and the volumetric water content corresponding to the critical matric suction on the soil‒water characteristic curve (SWCC) is defined as the critical volumetric water content. The proposed critical matric suctions and critical volumetric water contents can potentially be used as basic data to detect the time of shallow landslide occurrence and issue a landslide early warning.

The stability of underground parallelepiped coal pillars formed during trunk road development in inclined coal seams is very important for safe access to the mine workings. These protective coal pillars developed around the trunk roads have the longest life span in coal mines. Although these pillars are designed with high safety factors, their failures continue to occur especially in inclined coal mines. The acute corners of parallelepiped coal pillars are highly stressed and prone to failure. These failures may be attributed to the deterministic safety factor which does not consider field geotechnical uncertainties in their design parameters. This research work identified the geotechnical uncertainties in pillar designs and incorporated them in designing stable pillars in inclined coal seams. A probabilistic approach based on limit state function has been proposed for designing stable parallelepiped coal pillars and validated in an inclined coal mine. In this study, the working stresses of the inclined coal pillars are varied for evaluating their influence on pillar reliability using the three cases of the limit state functions namely, empirical, numerical average, and numerical maximum. The pillar reliabilities were estimated by Monte Carlo Simulation. The results indicate that the empirical and numerical average cases yielded stable pillars, whereas the numerical maximum case provided an unstable design. The correlation between safety factor and reliability has been established which can predict the reliability for a given safety factor of pillars with a similar range of design inputs. Further, the threshold values of pillar sizes, acute corner angles, and seam gradients for the reliable pillar design have been determined by sensitivity analysis. These findings can help in designing stable parallelopiped pillars, especially in inclined coal seams to reduce pillar failures and enhance mine safety.HighlightsKey geotechnical uncertainties in coal pillar stability parameters are identifiedA limit state function-based probabilistic design approach is proposed to include geotechnical uncertainties.The reliabilities of parallelepiped pillars in inclined coal seams are estimated using the Monte Carlo Simulation method.The correlation between pillar reliability and the safety factor of parallelepiped coal pillars is established.Threshold values of design parameters are determined for stable parallelepiped pillars using sensitivity analysis.

Resilience engineering depends on four abilities: the ability a) to respond to what happens, b) to monitor critical developments, c) to anticipate future threats and opportunities, and d) to learn from past experience - successes as well as failures. They provide a structured way of analysing problems and proposing practical solutions. This book is divided into four sections which describe issues relating to each of the four abilities. The section's chapters emphasise practical ways of engineering resilience, featuring case studies and real applications.

An important task of geotechnical engineering is a suitable design of safety factor (SF) of retaining wall under both static and dynamic conditions. This paper presents the advantages of both prediction and optimization of retaining wall SF through artificial neural network (ANN) and artificial bee colony (ABC), respectively. These techniques were selected because of their capability in predicting and optimizing science and engineering problems. To gain purpose of this research, a comprehensive database consisted of 2880 datasets of wall height, wall width, wall mass, soil mass and internal angle of friction as input parameters and SF of retaining wall as output was prepared. In fact, SF is considered as a function of the mentioned parameters. At the first step of modeling, several ANN models were constructed and the best one among them was selected. The coefficient of determination (R2) value of 0.998 for both training and testing datasets was obtained for the best ANN model which indicates an excellent accuracy level in predicting SF values. In the next step of modeling, the results of selected ANN model were used as an input for the optimization technique of ABC. In general, 11 models of ABC optimization with different strategies were built. As a result, by decreasing wall height value from 10 m to 8 m and 5.628 m and using almost constant values for the other input parameters, SF values were obtained as 2.142 and 5.628, respectively. Results of (8.003, 0.794, 0.667, 1800 and 2800) and (5.628, 0.763, 0.660, 1735 and 2679) were obtained for wall height, wall width, internal friction angle, soil mass and wall mass of the best models with 2.142 and 5.628 SF values, respectively.

This paper presents intelligent models for solving problems related to retaining walls in geotechnics. To do this, safety factors of 2800 retaining walls were modeled and recorded considering different effective parameters of retaining walls (RWs), i.e., height of the wall, wall thickness, friction angle, density of the soil, and density of the rock. Two intelligent methodologies including a pre-developed artificial neural network (ANN) and a combination of artificial bee colony (ABC) and ANN were selectively developed to approximate safety factors of RWs. In the new network, ABC was used to optimize weight and biases of ANN to receive higher level of accuracy and performance prediction. Many ANN and ABC–ANN models were built considering the most influential parameters of them and their performances were evaluated using coefficient of determination (R2) and root mean square error (RMSE) performance indices. After developing the mentioned models, it was found that the new hybrid model is able to increase network performance capacity significantly. For instance, R2 values of 0.982 and 0.985 for training and testing of ABC–ANN model, respectively, compared to these values of 0.920 and 0.924 for ANN model showed that the new hybrid model can be introduced as a capable enough technique in the field of this study for estimating safety factors of RWs.

The changes that occur to the physicomechanical features of rocks during freeze–thaw cycles are crucial to research on the stability of slope engineering in cold regions. In this study, granite specimens underwent freeze–thaw cycling and uniaxial compression testing. The mechanics of the freeze–thaw deterioration were analyzed based on the changes that occurred in the uniaxial compression strength, stress–strain curve, freeze–thaw coefficient, and degree of weathering of the rocks during freeze–thaw cycles. The results were applied in an analysis of the slope stability of a rock mass in an open-pit mine, and the safety factors of the slope before and after freeze–thaw cycling were computed with the Hoek–Brown empirical criterion. The results show that the mass of the granite increased and its uniaxial compression strength decreased after freeze–thaw cycling. The safety factor of the slope decreased due to the freeze–thaw cycling. This research thus shows the importance of studying the mechanics of slope engineering deterioration in cold regions.

From a mechanical viewpoint, petioles of foliage leaves are subject to contradictory mechanical requirements. High flexural rigidity guarantees support of the lamina and low torsional rigidity ensures streamlining of the leaves in wind. This mechanical trade-off between flexural and torsional rigidity is described by the twist-to-bend ratio. The safety factor describes the maximum load capacity. We selected four herbaceous species with different body plans (monocotyledonous, dicotyledonous) and spatial configurations of petiole and lamina (2-dimensional, 3-dimensional) and carried out morphological-anatomical studies, two-point bending tests and torsional tests on the petioles to analyze the influence of geometry, size and shape on their twist-to-bend ratio and safety factor. The monocotyledons studied had significantly higher twist-to-bend ratios (23.7 and 39.2) than the dicotyledons (11.5 and 13.3). High twist-to-bend ratios can be geometry-based, which is true for the U-profile of Hosta x tardiana with a ratio of axial second moment of area to torsion constant of over 1.0. High twist-to-bend ratios can also be material-based, as found for the petioles of Caladium bicolor with a ratio of bending elastic modulus and torsional modulus of 64. The safety factors range between 1.7 and 2.9, meaning that each petiole can support about double to triple the leaf’s weight.

Vegetation is an effective and environmental-friendly approach to improve slope stability. However, due to the lack of reasonable calculation methods for vegetated slopes, the analysis theory of ecological slope protection lags behind the engineering application. Based on the shallow translational failure model considering the boundary effects, the dimensionless seismic stability number of shallow overburden slopes with or without vegetation protection was deduced by using the upper limit method of limit analysis. A series of stability charts for slopes with or without vegetation protection under seismic excitations with different amplitudes were provided. The results show that the underestimation degree of the safety factor calculated from the infinite slope model increases gradually with the increase of the thickness ratio of the shallow overburden. Compared with the slope without vegetation protection, the seismic stability number of the slope with vegetation protection is nonlinear. The proposed stability charts show good performance for rapidly obtaining the safety factor with calculation errors of less than 9%, which is useful in assessing the safety of the shallow overburden slopes with vegetation protection in the preliminary design.

Circular failure can be seen in weak rocks, the slope of soil, mine dump, and highly jointed rock mass. The challenging issue is to accurately predict the safety factor (SF) and the behavior of slopes. The aim of this study is to offer advanced and accurate models to predict the SF of slopes through machine learning methods improved by optimization algorithms. To this view, three different methods, i.e., trial and error (TE) method, gravitational search algorithm (GSA), and whale optimization algorithm (WOA) were used to investigate the proper control parameters of least squares support vector machine (LSSVM) method. In the constructed LSSVM-TE, LSSVM-GSA and LSSVM-WOA methods, six effective parameters on the SF, such as pore pressure ratio and angle of internal friction, were used as the input parameters. The results of the error criteria indicated that both GSA and WOA can improve the performance prediction of the LSSVM method in predicting the SF. However, the LSSVM-WOA method, with root mean square error of 0.141, performed better than the LSSVM-GSA with root mean square error of 0.170.