Seismic safety assessment of existing masonry infill structures in Nepal

Reinforced concrete (RC) buildings in Nepal are constructed with RC frames and masonry infill panels. These structures exhibit a highly non-linear inelastic behavior resulting from the interaction between the panels and frames. This paper presents an extensive case study of existing RC buildings in Nepal. Non-linear analyses were performed on structural models of the buildings considered as a bare frame and with masonry infill, in order to evaluate the influence of infill walls on the failure mechanisms. Five three-storey buildings with different structural configurations and detailing were selected. The effect of masonry infill panels on structural response was delineated by comparing the bare-framed response with the infill response. Seismic performance is evaluated with regard to global strength, stiffness, energy dissipation, inter-storey drift, and total deflection of the structure. A parametric analysis of structures with masonry infill is also performed. For this, the influence of different material properties is studied, namely diagonal compressive stress, modulus of elasticity and tensile stress of masonry infill panels. Study results show that masonry infill increases the global strength and stiffness of the structures; it decreases the inter-storey drift and hence the total displacement of the structure. The results quantify the influence of the infill panels on structural response and, in particular, the effect of the diagonal compressive strength of the masonry wall.     

ITQs under illegal fishing: An application to the red shrimp fishery in Chile

We study an individual transferable quota system with imperfect enforcement. We apply a model of individual fisherman behavior to the red shrimp (Pleuroncodes monodon) fishery in central-southern Chile. Simulation results suggest that illegal fishing could generate a 21% increase in fishing effort, resulting in a 13% increase in catch and a 2% lower quota price in comparison with the results of a system that operates under perfect compliance. The results are sensitive to changes in the level of fish abundance, total allowable catch, and the design of enforcement to induce compliance.     

Lithologic controls on mineralization at the Lagunas Norte high-sulfidation epithermal gold deposit, northern Peru

The 13.1-Moz high-sulfidation epithermal gold deposit of Lagunas Norte, Alto Chicama District, northern Peru, is hosted in weakly metamorphosed quartzites of the Upper Jurassic to Lower Cretaceous Chimú Formation and in overlying Miocene volcanic rocks of dacitic to rhyolitic composition. The Dafne and Josefa diatremes crosscut the quartzites and are interpreted to be sources of the pyroclastic volcanic rocks. Hydrothermal activity was centered on the diatremes and four hydrothermal stages have been defined, three of which introduced Au ± Ag mineralization. The first hydrothermal stage is restricted to the quartzites of the Chimú Formation and is characterized by silice parda, a tan-colored aggregate of quartz-auriferous pyrite–rutile ± digenite infilling fractures and faults, partially replacing silty beds and forming cement of small hydraulic breccia bodies. The δ³⁴S values for pyrite (1.7–2.2?‰) and digenite (2.1?‰) indicate a magmatic source for the sulfur. The second hydrothermal stage resulted in the emplacement of diatremes and the related volcanic rocks. The Dafne diatreme features a relatively impermeable core dominated by milled slate from the Chicama Formation, whereas the Josefa diatreme only contains Chimú Formation quartzite clasts. The third hydrothermal stage introduced the bulk of the mineralization and affected the volcanic rocks, the diatremes, and the Chimú Formation. In the volcanic rocks, classic high-sulfidation epithermal alteration zonation exhibiting vuggy quartz surrounded by a quartz–alunite and a quartz–alunite–kaolinite zone is observed. Company data suggest that gold is present in solid solution or micro inclusions in pyrite. In the quartzite, the alteration is subtle and is manifested by the presence of pyrophyllite or kaolinite in the silty beds, the former resulting from relatively high silica activities in the fluid. In the quartzite, gold mineralization is hosted in a fracture network filled with coarse alunite, auriferous pyrite, and enargite. Alteration and mineralization in the breccias were controlled by permeability, which depends on the type and composition of the matrix, cement, and clast abundance. Coarse alunite from the main mineralization stage in textural equilibrium with pyrite and enargite has δ³⁴S values of 24.8–29.4?‰ and $ {\delta^{18 }}{{\mathrm{O}}_{{\mathrm{S}{{\mathrm{O}}_4}}}} $ values of 6.8–13.9?‰, consistent with H₂S as the dominant sulfur species in the mostly magmatic fluid and constraining the fluid composition to low pH (0–2) and logfO₂ of ?28 to ?30. Alunite–pyrite sulfur isotope thermometry records temperatures of 190–260 °C; the highest temperatures corresponding to samples from near the diatremes. Alunite of the third hydrothermal stage has been dated by ⁴⁰Ar/³⁹Ar at 17.0?±?0.22 Ma. The fourth hydrothermal stage introduced only modest amounts of gold and is characterized by the presence of massive alunite–pyrite in fractures, whereas barite, drusy quartz, and native sulfur were deposited in the volcanic rocks. The $ {\delta^{18 }}{{\mathrm{O}}_{{\mathrm{S}{{\mathrm{O}}_4}}}} $ values of stage IV alunite vary between 11.5 and 11.7?‰ and indicate that the fluid was magmatic, an interpretation also supported by the isotopic composition of barite (δ³⁴S?=?27.1 to 33.8?‰ and $ {\delta^{18 }}{{\mathrm{O}}_{{\mathrm{S}{{\mathrm{O}}_4}}}} $ ?=?8.1 to 12.7?‰). The Δ³⁴S_py–alu isotope thermometry records temperatures of 210 to 280 °C with the highest values concentrated around the Josefa diatreme. The Lagunas Norte deposit was oxidized to a depth of about 80 m below the current surface making exploitation by heap leach methods viable.     

Wall-slab joint parameter identification of a reinforced concrete structure using possibly corrupted modal data

In numerical models, the connections among component members are crucial for the prediction of structural behaviour under different types of solicitations. In reinforced structures, the connections are often assumed rigid, what may not be realistic in many practical cases. As alternative, a semi-rigid behaviour depending on a set of independent parameters can be proposed. In this case, a new difficulty arises, which is finding the appropriate values for those parameters. The present study proposes a numerical strategy for identification of the connection parameters based on the constitutive relation error (CRE). To include all available information, an augmented version (Modified CRE) is implemented. The parameters search is iterative and require large amount of system response analysis. To increase the computational efficiency, a reduced order model is adopted. The proposed approach shows low-sensitivity to limited lack of information and also to support condition variability, both of them verified numerically. In this work, experimental tests for a real 1:4 scale structure is utilized for finding the parameters corresponding to the first three modal shapes. A good agreement between numerical predictions and observations is verified, what highlights the accuracy and stability of the proposed numerical approach. The present study may also find applications in the domain of design of experiments.     

On the ecohydrology of the Yucatan Peninsula: Evapotranspiration and carbon intake dynamics across an eco‐climatic gradient

The hydrology and productivity of the ecosystems of the Yucatan Peninsula (YP) are highly constrained by two factors: (a) the lack of surface drainage networks due to the existence of a highly permeable and connected karstic aquifer roughly the size of the peninsula and (b) a climatic gradient that leads to a transition from seasonally dry deciduous and sub‐deciduous tropical forests, in the north‐western and central parts of the Peninsula, to evergreen forests, in the southern and eastern parts. As a result, surface water fluxes of the YP are restricted to evapotranspiration (ET) that are tightly coupled to ecosystems health and gross primary productivity (GPP). The magnitude and seasonal variation of these fluxes are sensitive to climatic variability and perturbations caused by extreme events such as droughts and tropical storms that are frequent in the YP. In this study, we assess the spatio‐temporal dynamics of ET and GPP above average dry and wet conditions through time series analyses of 15 years of remotely sensed data from both Moderate Resolution Imaging Spectroradiometer and Tropical Rainfall Measuring Mission satellite products. Our results show that ET and GPP follow a regional moisture and temperature gradient that highly controls the distribution of ecosystems within the peninsula. We observe that ET and GPP are in phase with the rainy season in the deciduous forests, but for the evergreen forests, only the GPP is in phase. Additionally, and with the exception of droughts on deciduous ecosystems of the northern part of the YP, the productivity of these ecosystems shows a legacy effect, responding more to a defined trajectory (wetting or drying on the previous years), rather than to punctual extreme climatic events. This has implications on the resilience of these ecosystems to natural perturbations of climate. Comparisons between deciduous and evergreen forest indicate that both types of ecosystems have different plant water use strategies in response to hydrologic variability.     

Transportation mode-based segmentation and classification of movement trajectories

The knowledge of the transportation mode used by humans (e.g. bicycle, on foot, car and train) is critical for travel behaviour research, transport planning and traffic management. Nowadays, new technologies such as the Global Positioning System have replaced traditional survey methods (paper diaries, telephone) because they are more accurate and problems such as under reporting are avoided. However, although the movement data collected (timestamped positions in digital form) have generally high accuracy, they do not contain the transportation mode. We present in this article a new method for segmenting movement data into single-mode segments and for classifying them according to the transportation mode used. Our fully automatic method differs from previous attempts for five reasons: (1) it relies on fuzzy concepts found in expert systems, that is membership functions and certainty factors; (2) it uses OpenStreetMap data to help the segmentation and classification process; (3) we can distinguish between 10 transportation modes (including between tram, bus and car) and propose a hierarchy; (4) it handles data with signal shortages and noise, and other real-life situations; (5) in our implementation, there is a separation between the reasoning and the knowledge, so that users can easily modify the parameters used and add new transportation modes. We have implemented the method and tested it with a 17-million point data set collected in the Netherlands and elsewhere in Europe. The accuracy of the classification with the developed prototype, determined with the comparison of the classified results with the reference data derived from manual classification, is 91.6%.     

Can climate variability information constrain a hydrological model for an ungauged Costa Rican catchment?

Long‐term hydrological data are key to understanding catchment behaviour and for decision making within water management and planning. Given the lack of observed data in many regions worldwide, such as Central America, hydrological models are an alternative for reproducing historical streamflow series. Additional types of information—to locally observed discharge—can be used to constrain model parameter uncertainty for ungauged catchments. Given the strong influence that climatic large‐scale processes exert on streamflow variability in the Central American region, we explored the use of climate variability knowledge as process constraints to constrain the simulated discharge uncertainty for a Costa Rican catchment, assumed to be ungauged. To reduce model uncertainty, we first rejected parameter relationships that disagreed with our understanding of the system. Then, based on this reduced parameter space, we applied the climate‐based process constraints at long‐term, inter‐annual, and intra‐annual timescales. In the first step, we reduced the initial number of parameters by 52%, and then, we further reduced the number of parameters by 3% with the climate constraints. Finally, we compared the climate‐based constraints with a constraint based on global maps of low‐flow statistics. This latter constraint proved to be more restrictive than those based on climate variability (further reducing the number of parameters by 66% compared with 3%). Even so, the climate‐based constraints rejected inconsistent model simulations that were not rejected by the low‐flow statistics constraint. When taken all together, the constraints produced constrained simulation uncertainty bands, and the median simulated discharge followed the observed time series to a similar level as an optimized model. All the constraints were found useful in constraining model uncertainty for an—assumed to be—ungauged basin. This shows that our method is promising for modelling long‐term flow data for ungauged catchments on the Pacific side of Central America and that similar methods can be developed for ungauged basins in other regions where climate variability exerts a strong control on streamflow variability.     

Semi-empirical prediction method for monthly precipitation prediction based on environmental factors and comparison with stochastic and machine learning models

ABSTRACT

Precipitation prediction is central in hydrology and water resources planning and management. This paper introduces a semi-empirical predictive model to predict monthly precipitation and compares its predictive skill with those of machine learning (ML) methods. The stochastic method presented herein estimates monthly precipitation with one-step-ahead prediction properties. The ML predictive skill of the algorithms is evaluated by predicting monthly precipitation relying on the statistical association between precipitation and environmental and topographic factors. The semi-empirical predictive model features non-negative matrix factorization (NMF) for investigating the influence of multiple predictor variables on precipitation. The semi-empirical predictive model’s parameters are optimized with the hybrid genetic algorithm (GA) and Levenberg-Marquardt algorithm (LM), or GALMA, yielding a validated model with high predictive skill. The methodologies are illustrated with data from Hubei Province, China, which comprise 27 meteorological station datasets from 1988–2017. The empirical results provide valuable insights for developing semi-empirical rainfall prediction models.