Evaluation of Land Suitability for Urban Land‐Use Planning: Case Study Dhaka City

The aim of this research is evaluation of land suitability for urban land‐use planning. Four factors and fourteen criteria were selected for suitability analysis and land‐use planning. Factors and criteria were defined based on literature survey, experts’ opinions, local contexts and availability of data. GIS‐based Analytical Hierarchy Process (AHP) was used as a Multi‐criteria Decision Making model. The study was conducted on a selected area of Dhaka city, which is one of the fastest growing mega‐cities of the world. The research result shows that highly suitable area (13%) should be used for urban residential zone; moderately suitable area (35%) should be designated as mixed use zone; low suitable area (42%) should be reserved for agricultural use and open spaces; and not suitable area (10%) should be protected from any types of activities except agriculture. The research approached an urban land‐use planning at a regional scale.     

An Agent‐Based Simulation of Residential Location Choice of Tenants in Tehran,Iran

Residential location choice modeling is one of the substantial components of land use and transportation models. While numerous aggregated mathematical and statistical approaches have been developed to model the residence choice behavior of households, disaggregated approaches such as the agent‐based modeling have shown interesting capabilities. In this article, a novel agent‐based approach is developed to simulate the residential location choice of tenants in Tehran, the capital of Iran. Tenants are considered as agents who select their desired residential alternatives according to their characteristics and preferences for various criteria such as the rent, accessibility to different services and facilities, environmental pollution, and distance from their workplace and former residence. The choice set of agents is limited to their desired residential alternatives by applying a constrained NSGA‐II algorithm. Then, agents compete with each other to select their final residence among their alternatives. Results of the proposed approach are validated by comparing simulated and actual residences of a sample of tenants. Results show that the proposed approach is able to accurately simulate the residence of 59.3% of tenants at the traffic analysis zone level.     

Analysis of anomalies in ADS-B and its GPS data

Traditionally, the surveillance component of the air traffic management system has been based on radar, which consists of two separate systems: primary radar and secondary radar, which both enable the measurement of the aircraft range and bearing to the radar station. Primary radar is based on signals emitted by a ground station simply being reflected off an object and detected by a ground-based receiver. Secondary radar also emits signals, but relies upon a transponder onboard the aircraft to emit a signal itself, modulated among others by a four-digit aircraft identity (Mode A), aircraft altitude (Mode C) and/or 24-bit unique address (Mode S). Typical accuracies of secondary radar are of the order of 0.03 NM in range and 0.07° in azimuth. However, no position integrity report is provided. Air traffic density is expected to significantly increase in the future. In order to maintain or enhance air travel efficiency, while maintaining safety, more accurate surveillance systems, with the required integrity, will be required. Automatic dependent surveillance–broadcast (ADS-B) is a new aviation surveillance system, envisioned to overcome the limitations of radar and to enhance surveillance performance and thereby increase airspace capacity. However, its high dependence on external systems such as onboard navigation and communication systems also increases the number of potential points of failure. It is important to understand and mitigate these failure modes before the system can reliably be implemented. The present study emerged as an exploratory research as part of a safety assessment framework development for the ADS-B system. It reviews the ADS-B failure modes, data collection and analysis of ADS-B and its corresponding onboard GPS data. The study identifies a set of failures common to certain aircraft models, with consistent error patterns. A key failure mode was found to be associated with the navigation data from the onboard GPS. We discuss the identified failure modes and investigate the nature and causes of these failures. The findings highlight some of the deficiencies of the current ADS-B system, which will need to be addressed before the ADS-B system can reliably be implemented.     

Behavior of Shallow Strip Footing on Twin Voids

This paper numerically examines the bearing capacity and failure mechanism of a shallow strip foundation constructed above twin voids. The voids may refer to caves, caverns, underground aqueduct or tunnels due to water seepage, chemical reaction or deliberately excavated in soil deposit. The ability of numerical model to accurately predict the system behavior is evaluated by performing verification analyses on existing researches. Subsequently, a parametric study carried out to reveal the influence of size of footing/voids and their location (i.e. depth, spacing, eccentricity) on the bearing capacity of footing. To clarify the failure mechanism, the distribution of shear strain in the soil for different scenarios is assessed. The parametric study provided a new framework to determine the bearing capacity and the mode of failure for footings on voids. Based on the results, a criterion can be issued to avoid collapse of footing/voids regarding the shape, location and size of voids. The results can also be used to design construction of a footing on existing voids while the acquired failure mechanisms can be appointed to develop analytical solutions for this problem. Results demonstrated that a critical depth for voids and a critical distance between them exist where the influence on the ultimate bearing capacity of footing disappears.     

Assessment and Prediction of Liquefaction Potential Using Different Artificial Neural Network Models: A Case Study

Soil liquefaction as a transformation of granular material from solid to liquid state is a type of ground failure commonly associated with moderate to large earthquakes and refers to the loss of strength in saturated, cohesionless soils due to the build-up of pore water pressures and reduction of the effective stress during dynamic loading. In this paper, assessment and prediction of liquefaction potential of soils subjected to earthquake using two different artificial neural network models based on mechanical and geotechnical related parameters (model A) and earthquake related parameters (model B) have been proposed. In model A the depth, unit weight, SPT-N value, shear wave velocity, soil type and fine contents and in model B the depth, stress reduction factor, cyclic stress ratio, cyclic resistance ratio, pore pressure, total and effective vertical stress were considered as network inputs. Among the numerous tested models, the 6-4-4-2-1 structure correspond to model A and 7-5-4-6-1 for model B due to minimum network root mean square errors were selected as optimized network architecture models in this study. The performance of the network models were controlled approved and evaluated using several statistical criteria, regression analysis as well as detailed comparison with known accepted procedures. The results represented that the model A satisfied almost all the employed criteria and showed better performance than model B. The sensitivity analysis in this study showed that depth, shear wave velocity and SPT-N value for model A and cyclic resistance ratio, cyclic stress ratio and effective vertical stress for model B are the three most effective parameters on liquefaction potential analysis. Moreover, the calculated absolute error for model A represented better performance than model B. The reasonable agreement of network output in comparison with the results from previously accepted methods indicate satisfactory network performance for prediction of liquefaction potential analysis.