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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 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.

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.

Structural safety assessment of masonry structures is a crucial topic for architectural heritage conservation. Performance indicators are commonly adopted for civil structures and infrastructure made of reinforced/prestressed concrete or steel but are often overlooked in the context of historic structures. Notions such as safety, reliability, robustness, and resilience are accepted concepts in the world of historic buildings, but they rarely translate into quantifiable indicators applicable to structural rehabilitation. The complex mechanical behaviour of masonry, indeed, is affected by uncertainties in the characterization of the material’s properties. Furthermore, when considering damaged masonry structures, uncertainties also include the natural aging and degradation of the component materials. In this context, the proposed research intends to perform a preliminary safety assessment of a masonry building subjected to seismic loads considering a given damage level and its evolution over time. Damage is described by means of a performance parameter that is able to capture the decrease in the characteristic compressive strength related to the presence of a crack pattern and changes in the live loads. The method allows one to address in a probabilistic way the determination of a limit global safety factor, affected by this parameter, and of the time required to attain the failure condition.

In order to reduce damages caused by slope instability and landslide disasters, it is of great significance to find an efficient, accurate and time-saving method for slope stability analyses. This paper proposes a convolutional neural network based on digital twin models to predict the safety factor of a slope and be evaluate its stability state. In order to solve the problem of lack of the CNN training samples, the digital twin method is resorted to generate 4000 slope models from 10 real slopes by fine-tuning the geometric coordinates and material parameters of their soil layers. The finite element computation of the safety factor of these 4000 slope models were realized by using the parametric analysis of ABAQUS platform and 4000 slope datasets were obtained to serve as the CNN training samples. With the geometric coordinates and material parameters of the slopes as the CNN input and the slope safety factor as the CNN output, the slope safety factor can be effectively predicted. The results show that the prediction accuracy for the testing set reaches 96% and the root mean square error is 0.079. Compared with the finite element modeling time, the prediction time is greatly shortened. The evaluation accuracy of stability states for the 10 real slopes has reached 100%, which indicates that the CNN model has good generalization ability and prediction effect and has practical significance in engineering applications.

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.

As a new material, polyurethane polymer has been widely used in emergency and disaster relief engineering in recent years due to the excellent engineering mechanical properties. Based on the characteristics of this material, a multi pipe grouting micro anti-slide pile is proposed in slope reinforcement, which is formed by using polyurethane polymer slurry as grouting material. Compared with traditional anti-slide pile, the polyurethane polymer micro anti-slide pile has the advantages of strong applicability, no water reaction, small disturbance, fast construction, economy and durability, and it can be adapted to emergency reinforcement of dangerous landslide. As a flexible retaining structure, polyurethane polymer micro anti-slide piles can strengthen the slope by cooperating with the forces. However, there is no report on the reinforcement of slope by polyurethane polymer micro anti-slide piles at present. In this paper, a three-dimensional multi-row polyurethane polymer micro anti-slide piles model for slope reinforcement considering different embedded depth and pile location is established. Safety factor, thrust force of landslide behind pile, length of pile and Mises stress are taken as four factors to evaluate the feasibility and the reinforcement effect of reinforcing slope with polyurethane polymer micro anti-slide pile. The optimal reliability of polyurethane polymer micro anti-slide pile for slope reinforcement is evaluated by giving different weight values to each factor through multi-factor comprehensive evaluation method. The safety factor of slope (Fs), landslide thrust behind pile and Mises stress of pile are analyzed under different embedded depth (le) and pile position (px). The results show that polyurethane polymer micro anti-slide piles have excellent reinforcement effect under rescue and relief tasks. With the increase of embedded depth, the safety factor of slope gradually increases and then remains stable, the best embedded depth of micro-pile is about 1/8–1/12 LB; as the pile position is gradually away from the top of the slope, the safety factor of the slope reaches its maximum value in the middle and lower part of the slope, the optimum position of pile arrangement is 0.55–0.65 L from the top of the slope.

Fluctuating water levels are responsible for many reservoir slope failures. This work develops a novel slope analysis model (Y-slopeW) to evaluate the reservoir slope stability under water–rock coupling effect, based on the combined finite-discrete element method (FDEM). The transient fluid fields under water level fluctuations are first calculated, and then slope stability under water–rock interaction is evaluated in terms of the safety factor using the strength reduction method. Several benchmark tests are proposed to validate the present model. Stability analysis of an ideal slope under reservoir water level fluctuation is analyzed, where the effect of reservoir fluctuation rate and rock permeability coefficient on slope stability are discussed in detail. A practical slope case (Majiagou slope) in the Three Gorges Reservoir area is studied. Results show that the fluctuating reservoir water level plays an important role in slope stability, and a rapid drawdown is the most unfavorable condition to the slope stability. The work detailed herein proposes an efficient tool to better understand the failure mechanism and stability evolution for slopes under water level fluctuation.

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.

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.