Large rectangular cross-section tunnel undercrossing urban road by micro pipe jacking and joint assembly structure (MPJ & JAS) method in soft soils.

With the continuous construction of urban traffic roads, more and more new roads are cut off by existing roads to form "dead end roads". There is an urgent need for a trenchless method suitable for urban ultra-shallow overburden to build the undercrossing tunnel. To solve this problem, this paper proposed the micro pipe jacking and joint assembly structure (MPJ & JAS) method, which has the characteristics of shallow burial depth, low cost, short construction time, flexible cross-section setting and high space utilization. The MPJ & JAS method construct a large cross-section tunnel through assembling small cross-section elements, quite different from traditional methods. Therefore, this paper designed a CT-shaped integrated joint, the mechanical performance of which was verified and clarified by tensile test. The bending test and finite element (FE) analysis proved the reliability of MPJ & JAS tunnel structure, and confirmed the structure performances such as the failure models, crack behaviors, load-deflection response and stress-strain distribution. Moreover, the influences of the steel plate thickness, concrete strength and shear connector spacing were determined by the FE analysis. On the basis of test results and reasonable assumptions, a theoretical design method considering the influence of the CT-shaped integrated joint was proposed, which can effectively predict the bending strength of the MPJ & JAS tunnel structure with an error of less than 10%. Finally, in view of the characteristics of the MPJ & JAS method, the suitable micro pipe jacking machine, soil reinforcement measure, hydraulic traction construction technology, high-precision guidance system and concrete construction quality detection method based on the phased array ultrasonic imaging technology were developed, supporting the accurate and efficient construction of the MPJ & JAS tunnel.

Risk assessment of pollen allergy in urban environments.

According to WHO, by 2050, at least one person out of two will suffer from an allergy disorder resulting from the accelerating air pollution associated with toxic gas emissions and climate change. Airborne pollen, and associated allergies, are major public health topics during the pollination season, and their effects are further strengthened due to climate change. Therefore, assessing the airborne pollen allergy risk is essential for improving public health. This study presents a new computational fluid dynamics methodology for risk assessment of local airborne pollen transport in an urban environment. Specifically, we investigate the local airborne pollen transport from trees on a university campus in the north of France. We produce risk assessment maps for pollen allergy for five consecutive days during the pollination season. The proposed methodology could be extended to larger built-up areas for different weather conditions. The risk assessment maps may also be integrated with smart devices, thus leading to decision-aid tools to better guide and protect the public against airborne pollen allergy.

Model predictive control based on artificial intelligence and EPA-SWMM model to reduce CSOs impacts in sewer systems.

Urbanization and an increase in precipitation intensities due to climate change, in addition to limited urban drainage systems (UDS) capacity, are the main causes of combined sewer overflows (CSOs) that cause serious water pollution problems in many cities around the world. Model predictive control (MPC) systems offer a new approach to mitigate the impact of CSOs by generating optimal temporally and spatially varied dynamic control strategies of sewer system actuators. This paper presents a novel MPC based on neural networks for predicting flows, a stormwater management model (SWMM) for flow conveyance, and a genetic algorithm for optimizing the operation of sewer systems and defining the best control strategies. The proposed model was tested on the sewer system of the city of Casablanca in Morocco. The results have shown the efficiency of the developed MPC to reduce CSOs while considering short optimization time thanks to parallel computing.

Modular Software Architecture for Local Smart Building Servers.

This paper presented the architecture and construction of a novel smart building system that could monitor and control buildings' use in a safe and optimal way. The system operates on a Raspberry local server, which could be connected via the cloud technology to a central platform. The local system includes nine modules that inter-communicate. The system detects sensor faults, and provides a friendly interface to occupants. The paper presented the software architecture IoT used for the building monitoring and the use of this system for the management of fifteen social housing units during a year. The system allowed the investigation of indoor comfort and both energy and hot water consumptions. Data analysis resulted in the detection of abnormal energy consumptions. The system could be easily used in buildings' management. It works in a plug-and-play mode.

Flood forecasting within urban drainage systems using NARX neural network.

Urbanization activity and climate change increase the runoff volumes, and consequently the surcharge of the urban drainage systems (UDS). In addition, age and structural failures of these utilities limit their capacities, and thus generate hydraulic operation shortages, leading to flooding events. The large increase in floods within urban areas requires rapid actions from the UDS operators. The proactivity in taking the appropriate actions is a key element in applying efficient management and flood mitigation. Therefore, this work focuses on developing a flooding forecast system (FFS), able to alert in advance the UDS managers for possible flooding. For a forecasted storm event, a quick estimation of the water depth variation within critical manholes allows a reliable evaluation of the flood risk. The Nonlinear Auto Regressive with eXogenous inputs (NARX) neural network was chosen to develop the FFS as due to its calculation nature it is capable of relating water depth variation in manholes to rainfall intensities. The campus of the University of Lille is used as an experimental site to test and evaluate the FFS proposed in this paper.

WEPP and ANN models for simulating soil loss and runoff in a semi-arid Mediterranean region.

This paper presents the use of both the Water Erosion Prediction Project (WEPP) and the artificial neural network (ANN) for the prediction of runoff and soil loss in the central highland mountainous of the Palestinian territories. Analyses show that the soil erosion is highly dependent on both the rainfall depth and the rainfall event duration rather than on the rainfall intensity as mostly mentioned in the literature. The results obtained from the WEPP model for the soil loss and runoff disagree with the field data. The WEPP underestimates both the runoff and soil loss. Analyses conducted with the ANN agree well with the observation. In addition, the global network models developed using the data of all the land use type show a relatively unbiased estimation for both runoff and soil loss. The study showed that the ANN model could be used as a management tool for predicting runoff and soil loss.