Assessment of input data selection methods for BOD simulation using data-driven models: a case study

2018 ◽  
Vol 190 (4) ◽  
Author(s):  
Azadeh Ahmadi ◽  
Zahra Fatemi ◽  
Sara Nazari
Keyword(s):  
Input Data ◽  
Data Selection ◽  
Data Driven ◽  
Selection Methods ◽  
Using Data

A case study of PavMD a new tool to identify good performing pavements in New Zealand

2021 ◽  
Author(s):  
Jacobus Daniel van der Walt ◽  
Eric Scheepbouwer ◽  
Bryan Pidwerbesky ◽  
Brian Guo ◽  
Max Ferguson ◽  
...  
Keyword(s):  
New Zealand ◽  
Performance Measures ◽  
Data Structures ◽  
Digital Technology ◽  
Data Driven ◽  
Pavement Performance ◽  
Using Data ◽  
Additional Value

With the advancement of digital technology, the collection of pavement performance data has become commonplace. The improvement of tools to extract useful information from pavement databases has become a priority to justify expenditures. This paper presents a case study of PaveMD, a tool that integrates multi-dimensional data structures with a data-driven fuzzy approach to identify good performing pavement sections. Combining this tool with an innovative paradigm where the focus is on repeating success can bring additional value to existing pavement databases. The case study shows that PaveMD can identify pavement sections that are performing well by comparing performance measures for the New Zealand context. In this paper, PaveMD's development is described, and its implementation is showcased using data from the New Zealand Long-Term Pavement Performance (LTPP) database. It is recommended that this approach be further developed and extended to other infrastructure databases internationally.

scholarly journals Measuring Occupants’ Behaviour for Buildings’ Dynamic Cosimulation

2018 ◽  
Vol 2018 ◽  
pp. 1-17 ◽  
Author(s):  
Federica Naspi ◽  
Marco Arnesano ◽  
Francesca Stazi ◽  
Marco D’Orazio ◽  
Gian Marco Revel
Keyword(s):  
Energy Use ◽  
Control Strategies ◽  
Simulation Software ◽  
Data Driven ◽  
Human In The Loop ◽  
Behavioural Approach ◽  
Loop Design ◽  
Using Data ◽  
Human Component

Measuring and identifying human behaviours are key aspects to support the simulation processes that have a significant role in buildings’ (and cities’) design and management. In fact, layout assessments and control strategies are deeply influenced by the prediction of building performance. However, the missing inclusion of the human component within the building-related processes leads to large discrepancies between actual and simulated outcomes. This paper presents a methodology for measuring specific human behaviours in buildings and developing human-in-the-loop design applied to retrofit and renovation interventions. The framework concerns the detailed building monitoring and the development of stochastic and data-driven behavioural models and their coupling within energy simulation software using a cosimulation approach. The methodology has been applied to a real case study to illustrate its applicability. A one-year monitoring has been carried out through a dedicated sensor network for the data recording and to identify the triggers of users’ actions. Then, two stochastic behavioural models (i.e., one for predicting light switching and one for window opening) have been developed (using the measured data) and coupled within the IESVE simulation software. A simplified energy model of the case study has been created to test the behavioural approach. The outcomes highlight that the behavioural approach provides more accurate results than a standard one when compared to real profiles. The adoption of behavioural profiles leads to a reduction of the discrepancy with respect to real profiles up to 58% and 26% when simulating light switching and ventilation, respectively, in comparison to standard profiles. Using data-driven techniques to include the human component in the simulation processes would lead to better predictions both in terms of energy use and occupants’ comfort sensations. These aspects can be also included in building control processes (e.g., building management systems) to enhance the environmental and system management.

scholarly journals Optimization of gully erosion susceptibility using data-driven statistical combined methods (case study: Toroud-Najar Abad basin)

2019 ◽  
Vol 10 (1) ◽  
pp. 18-38
Author(s):  
alireza arabameri ◽  
khalil rezaei ◽  
mojtaba yamani ◽  
kourosh shirani
Keyword(s):  
Gully Erosion ◽  
Data Driven ◽  
Using Data ◽  
Combined Methods

scholarly journals Examining the Core Dilemmas Hindering Big Data-related Transformations in Public-Sector Organisations

2019 ◽  
Vol 12 (2) ◽  
pp. 131-156 ◽  
Author(s):  
Päivikki Kuoppakangas ◽  
Tony Kinder ◽  
Jari Stenvall ◽  
Ilpo Laitinen ◽  
Olli-Pekka Ruuskanen ◽  
...  
Keyword(s):  
Big Data ◽  
Risk Taking ◽  
Service Provision ◽  
Data Driven ◽  
The Core ◽  
Management Approaches ◽  
Using Data ◽  
Public Organisations ◽  
Managerial System

AbstractThis study examines public organisations planning big data-driven transformations in their service provision. Without radical structural change or managerial system changes, leaders face dilemmas: simply bolting on big data makes little difference. This study is based on a qualitative empirical case study using data collected from the cities of Helsinki and Tampere in Finland. The three core dilemma pairs detected and connected to the big data-related organisational changes are: (1) repetitive continuity vs. visionary change, (2) risk-taking vs. security-seeking and (3) technology-based development vs. human-based development. This study suggests that organisational readiness involves not only capabilities; instead, readiness involves absorbing knowledge, making decisions, handling ambiguities, managing dilemmas. Thus, big data-related transformations in public organisations require embracing the world of dilemmas, since selected and cancelled experiments may each have valuable outcomes. The capability to act on intentions is a prerequisite for readiness; however, a preparedness to detect and address dilemmas is central to big data-related transformations. Thus, the ability to make dilemma decisions is a more complicated characteristic of readiness. In conclusion, our data analysis suggests that traditional public organisational and chance management approaches produce unsolved dilemmas in big data-related organisational changes.

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