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Residents and visitors in today's Seattle would barely recognize the landscape that its founding settlers first encountered. As the city grew, its leaders and inhabitants dramatically altered its topography to accommodate their changing visions. InToo High and Too Steep, David B. Williams uses his deep knowledge of Seattle, scientific background, and extensive research and interviews to illuminate the physical challenges and sometimes startling hubris of these large-scale transformations, from the filling in of the Duwamish tideflats to the massive regrading project that pared down Denny Hill. In the course of telling this fascinating story, Williams helps readers find visible traces of the city's former landscape and better understand Seattle as a place that has been radically reshaped. Watch the trailer: https://www.youtube.com/watch?v=af51FU8hHLI

Machine learning (ML) algorithms have been widely applied to analyze geotechnical engineering problems due to recent advances in data science. However, flexible ML models trained with limited data can exhibit unexpected behaviors, leading to low interpretability and physical inconsistency, thus, reducing the reliability and robustness of ML models for risk forecasting and engineering applications. As input features for geotechnical engineering applications often represent physical parameters following intrinsic and often monotonic relationships, incorporating monotonicity into ML models can help ensure the physical realism of model outputs. In this study, monotonicity was introduced as a soft constraint into artificial neural network (ANN) models, and their results were compared with several benchmark ML models. During the training process, data augmentation and point-wise gradient were used to evaluate the monotonicity of model predictions, and monotonicity violations were minimized through a modified loss function. A compilation of slope stability case histories from the literature was used for model development, benchmarking their performance, and evaluating the effects of monotonicity constraints. Cross-validation procedures were used for all model performance evaluations to reduce bias in sample selections. Results showed that unconstrained ML models produced predictions that violate monotonicity in many parts of the input space. However, by adding monotonicity constraints into ANN models, monotonicity violations were effectively reduced while maintaining relatively high performance, thus providing a more robust and interpretable prediction. Using slope stability prediction as a proxy, the methods developed in this study to incorporate monotonicity constraints into ML models can be applied to many geotechnical engineering applications. The proposed approach enhances the reliability and interpretability of ML models, resulting in more accurate and consistent outcomes for real-world applications.

Shallow slope failures that occur as a result of a decrease in matric suction after water infiltration from intense rains (unsaturated soil conditions) are the main causes of slope instability in tropical and subtropical regions. This study analyzes the occurrence of shallow landslides in a highway cutting of residual soil originating from aeolian sandstone. Characterization of failure mechanisms and stability analyses have been carried out in different scenarios and supported by surface and subsurface investigations, instrumentation and monitoring of rainfall, matric suction, and water level, field tests, and laboratory tests. The results indicate that the reduction in matric suction induced by rainwater infiltration is the triggering mechanism of slope failure. Conventional slope stability analyses and analyses incorporating unsaturated seepage models present results compatible with the hydrological and geotechnical data collected in the study. On the basis of these analyses, and considering the frequent rainfall events in the study area, a critical geometric configuration is proposed for the triggering of landslides in highway cutting slopes of residual soils of aeolian sandstone.

The number of pit-in-pit foundation pit is increasing quickly because of the continuous utilization of underground spaces in urban areas. Based on the co-construction project of Shanghai Museum of Natural History foundation pit and Shanghai Metro Line 13 foundation pit in Shanghai, China, the deformation characteristics of pit-in-pit foundation pit are researched by field observation and centrifugal model tests. The lateral wall displacement of inner foundation pit includes global deformation caused by the outer foundation pit excavation and deflection caused by the excavation of itself. The effect of inner foundation pit excavation on the lateral wall displacement of outer foundation pit and ground-surface settlement is smaller. The two factors affecting the deformation characteristics of pit-in-pit foundation pit, the distance between inner and outer foundation pits ( D ) and the excavation width of inner foundation pit ( W in ), are analyzed by centrifugal model tests. The result shows that the maximum lateral wall displacements of inner and outer foundation pits decrease nearly linearly with the increase of D , but increase with the increase of W in .

During the past decade’s exploitation of coal seams in Muchengjian Mine in Jingxi Coalfield, there were nearly thirty rock burst events, which hindered the safe and efficient coal production. Two typical mining areas were selected for analysis where almost half of rock burst events occurred. The research was aimed at finding connections between the occurrence of rock bursts and geological characteristics. The temporal and spatial characteristics of rock bursts were described in detail, the geological characteristics were investigated carefully, and the possible reasons for rock bursts were analyzed. The details documented in these cases not only provide an essential reference value for understanding the development mechanism of rock bursts, but also provide a basis for selecting control measures and optimizing related technical parameters during tunneling or mining under complicated geological conditions.

This paper focus on the drought monitoring and forecasting for semi-arid region based on the various machine learning models and SPI index. Drought phenomena are crucial role in the agriculture and drinking purposes in the area. In this study, Standardized Precipitation Index (SPI) was used to predicted the future drought in the upper Godavari River basin, India. We have selected the ten input combinations of ML model were used to prediction of drought for three SPI timescales (i.e., SPI -3, SPI-6, and SPI-12). The historical data of SPI from 2000 to 2019 was used for creation of ML models SPI prediction, these datasets was divided into training (75% of the data) and testing (25% of the data) models. The best subset regression method and sensitivity analysis were applied to estimate the most effective input variables for estimation of SPI 3, 6, and 12. The improved support vector machine model using sequential minimal optimization (SVM-SMO) with various kernel functions i.e., SMO-SVM poly kernel, SMO-SVM Normalized poly kernel, SMO-SVM PUK (Pearson Universal Kernel) and SMO-SVM RBF (radial basis function) kernel was developed to forecasting of the SPI-3,6 and 12 months. The ML models accuracy were compared with various statistical indicators i.e., root mean square error (RMSE), mean absolute error (MAE), relative absolute error (RAE), root relative squared error (RRSE), and correlation coefficient (r). The results of study area have been showed that the SMO-SVM poly kernel model precisely predicted the SPI-3 (R2 = 0.819) and SPI-12 (R2 = 0.968) values at Paithan station; the SPI-3 (R2 = 0.736) and SPI-6 (R2 = 0.841) values at Silload station, respectively. The SMO-SVM PUK kernel is found that the best ML model for the prediction of SPI-6 (R2 = 0.846) at Paithan station and SPI-12 (R2 = 0.975) at the Silload station. The compared with SVM-SMO poly kernel and SVM-SMO PUK kernel was observed, these models are best forecasting of drought (i.e. SPI-6 and SPI-12), while SVM-SMO poly kernel is good for SPI-3 prediction at both stations. The results have been showed the ability of the SVM-SMO algorithm with various kernel functions successfully applied for the forecasting of multiscale SPI under the climate changes. It can be helpful for decision making in water resource management and tackle droughts in the semi-arid region of central India.

Cyclones endanger life and cause great financial impact on interior and coastal regions through the destruction of buildings and land. Governments need to have a way of estimating the chance of different regions being impacted by a cyclone. The goal of this paper is to use big data to better predict future cyclone impacts. Large cyclone data sets from the CMA Tropical Cyclone Data Center are used in the analysis. By using big data analysis techniques, long-term patterns in cyclone locations and size can be revealed. The Hausdorff distance is used to determine overall changes in cyclone positions decade by decade. Monte Carlo techniques estimate the probability of a region being impacted by a cyclone any given year. This is done by creating random data sets that mimic long-term patterns in cyclone position and radii. It will be shown that any region can be assigned a probability of cyclone impact purely on large historical data sets.

Assessment of the seismic damage and usability of the building stock started a few days after the L’Aquila earthquake in order to evaluate the safety conditions of the buildings concerned. Several ordinances of the Prime Minister were issued to regulate the reconstruction process. In particular, based also on damage level, the procedures for repair, strengthening or demolition/reconstruction of residential buildings were established with the definition of relevant state funding. For each damaged building, practitioners engaged by property owners designed repair and strengthening interventions and then computed the corresponding costs. These projects were the technical basis for funding applications that owners submitted to the government. Technical and financial information collected during the approval procedure of such applications allowed compilation of a database regarding 5775 residential buildings damaged by the L’Aquila earthquake. The present study examines the restoration policy and the procedures regulating the reconstruction process of residential property outside city centres. In particular, the data related to the first phase of the reconstruction process (the so-called “light damage” reconstruction) to recover the usability of slightly damaged buildings are illustrated. The discussion focuses on the time-to-approval of funding applications and on the public contributions granted for repair and local strengthening works.

Humanity has long sought to explain and understand why environmental processes and phenomena contribute to and interfere with development processes, frequently through the terms and concepts of ‘vulnerability’ and ‘resilience’. Many proven ideas and approaches from development and disaster risk reduction literature are not fully considered by contemporary climate change work. This chapter describes the importance of older vulnerability and resilience research for contemporary investigations involving climate change, suggesting ways forward without disciplinary blinkers. Vulnerability and resilience as processes are explored alongside critiques of the post-disaster ‘return to normal’ paradigm. The importance of learning from already existing literature and experience is demonstrated for ensuring that complete vulnerability and resilience processes are accounted for by placing climate change within other contemporary development concerns.