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Automation in construction toward resilience : robotics, smart materials & intelligent systems
\"This book presents all aspects of automation in construction pertaining to the use of information technologies in design, engineering, construction technologies, and maintenance and management of constructed facilities. The broad scope encompasses all stages of the construction life cycle from initial planning and design, through the construction of the facility, its operation, and maintenance, to the eventual dismantling and recycling of buildings and engineering structures\"-- Provided by publisher.
BOOK
AI-big data analytics for building automation and management systems: a survey, actual challenges and future perspectives
In theory, building automation and management systems (BAMSs) can provide all the components and functionalities required for analyzing and operating buildings. However, in reality, these systems can only ensure the control of heating ventilation and air conditioning system systems. Therefore, many other tasks are left to the operator, e.g. evaluating buildings’ performance, detecting abnormal energy consumption, identifying the changes needed to improve efficiency, ensuring the security and privacy of end-users, etc. To that end, there has been a movement for developing artificial intelligence (AI) big data analytic tools as they offer various new and tailor-made solutions that are incredibly appropriate for practical buildings’ management. Typically, they can help the operator in (i) analyzing the tons of connected equipment data; and; (ii) making intelligent, efficient, and on-time decisions to improve the buildings’ performance. This paper presents a comprehensive systematic survey on using AI-big data analytics in BAMSs. It covers various AI-based tasks, e.g. load forecasting, water management, indoor environmental quality monitoring, occupancy detection, etc. The first part of this paper adopts a well-designed taxonomy to overview existing frameworks. A comprehensive review is conducted about different aspects, including the learning process, building environment, computing platforms, and application scenario. Moving on, a critical discussion is performed to identify current challenges. The second part aims at providing the reader with insights into the real-world application of AI-big data analytics. Thus, three case studies that demonstrate the use of AI-big data analytics in BAMSs are presented, focusing on energy anomaly detection in residential and office buildings and energy and performance optimization in sports facilities. Lastly, future directions and valuable recommendations are identified to improve the performance and reliability of BAMSs in intelligent buildings.
Journal Article
Lighting Control Including Daylight and Energy Efficiency Improvements Analysis
Energy used for lighting is one of the major components of total energy consumption in buildings. Nowadays, buildings have a great potential to reduce their energy consumption, but to achieve this purpose additional efforts are indispensable. In this study, the need for energy savings evaluation before the implementation of lighting control algorithms for a specified building is highlighted. Therefore, experimental tests have been carried out in a university building with laboratories and other rooms, equipped with KNX building automation system. A dimmable control strategy has been investigated, dependent on daylight illuminance. Moreover, a relationship between external and internal daylight illuminance levels has been evaluated as well. Based on the experimental results, the authors proposed a method for the rough estimation of electrical energy savings. Since, according to the EN 15232 standard, Building Automation and Control Systems (BACS) play an important role in buildings’ energy efficiency improvements, the BACS efficiency factors from this standard have been used to verify the experimental results presented in the paper. The potential to reduce energy consumption from lighting in non-residential buildings by 28% for offices and 24% for educational buildings has been confirmed, but its dependence on specific building parameters has been discussed as well.
Journal Article
Design and Implementation of a Cascade Control System for a Variable Air Volume in Operating Rooms Based on Pressure and Temperature Feedback
This research presents the design and implementation of a cascade Proportional–Integral (PI) controller tailored for a Variable Air Volume (VAV) system that was specially created and executed particularly for hospital operating rooms. The main goal of this work is to make sure that the temperature and positive pressure stay within the limits set by ASHRAE Standard 170-2017. This is necessary for patient safety, surgical accuracy, and system reliability. The proposed cascade design uses dual-loop PI controllers: one loop controls the temperature based on user-defined setpoints by local control touch screen, and the other loop accurately modulates the differential pressure to keep the pressure of the environment sterile (positive pressure). The system works perfectly with Building Automation System (BAS) parts from Automated Logic Corporation (ALC) brand, like Direct Digital Controllers (DDC) and Web-CTRL software with Variable Frequency Drives (VFDs), advanced sensors, and actuators that give real-time feedback, precise control, and energy efficiency. The system’s exceptional responsiveness, extraordinary stability, and resilient flexibility were proven through empirical validation at the Korean Iraqi Critical Care Hospital in Baghdad under a variety of operating circumstances. Even during rapid load changes and door openings, the control system successfully maintained the temperature between 18 and 22 °C and the differential pressure between 3 and 15 Pascals. Four performance scenarios, such as normal (pressure and temperature), high-temperature, high-pressure, and low-pressure cases, were tested. The results showed that the cascade PI control strategy is a reliable solution for critical care settings because it achieves precise environmental control, improves energy efficiency, and ensures compliance with strict healthcare facility standards.
Journal Article
A Hybrid Approach in Design of Building Energy Management System with Smart Readiness Indicator and Building as a Service Concept
Improving energy efficiency and increasing the level of intelligence are two main factors determining the current development trends for new and modernized buildings. They are especially important in the perspective of development of prosumer installations and local microgrids. A key tool to achieve these goals is a well-designed and implemented Building Automation and Control System (BACS). This paper presents a new hybrid approach to the design and technical organization of BACS based on the provisions of the EN 15232 standard and the guidelines of the Smart Readiness Indicator (SRI) defined in the Energy Performance of Buildings Directive 2018 (EPBD 2018). The main assumptions of this hybrid approach along with examples of functional BACS designs for small prosumer installations organized according to them are provided. Potential impact on building energy performance is discussed as well. Finally, a SWOT analysis of the possibility of merging the EN 15232 standard guidelines and the SRI assessment methodology to develop uniform technical guidelines for the BACS functions design and evaluation of their impact on the buildings’ energy efficiency are discussed.
Journal Article
Energy and Water Management in Smart Buildings Using Spiking Neural Networks: A Low-Power, Event-Driven Approach for Adaptive Control and Anomaly Detection
The growing demand for energy efficiency and sustainability in smart buildings necessitates advanced AI-driven methods for adaptive control and predictive maintenance. This study explores the application of Spiking Neural Networks (SNNs) to event-driven processing, real-time anomaly detection, and edge computing-based optimization in building automation. In contrast to conventional deep learning models, SNNs provide low-power, high-efficiency computation by mimicking biological neural processes, making them particularly suitable for real-time, edge-deployed decision-making. The proposed SNN based on Reward-Modulated Spike-Timing-Dependent Plasticity (STDP) and Bayesian Optimization (BO) integrates occupancy and ambient condition monitoring to dynamically manage assets such as appliances while simultaneously identifying anomalies for predictive maintenance. Experimental evaluations show that our BO-STDP-SNN framework achieves notable reductions in both energy consumption by 27.8% and power requirements by 70%, while delivering superior accuracy in anomaly detection compared with CNN, RNN, and LSTM based baselines. These results demonstrate the potential of SNNs to enhance the efficiency and resilience of smart building systems, reduce operational costs, and support long-term sustainability through low-latency, event-driven intelligence.
Journal Article
Digital Twin-Based Simulation of Smart Building Energy Performance: BIM-Integrated MATLAB/Simulink Framework for BACS and SRI Evaluation
The increasing role of automation systems in energy-efficient buildings creates a need for simulation approaches that support standardized assessment already at the design stage. This paper presents a digital twin-based simulation framework that integrates building information modeling (BIM)-derived building data with MATLAB/Simulink models to enable regulation-oriented evaluation of building automation and control strategies. The proposed approach targets scenario-based analysis of automation maturity levels, covering conventional, advanced, and predictive configurations aligned with EN ISO 52120 and the Smart Readiness Indicator (SRI). A representative academic building model is used to demonstrate how the framework supports reproducible modeling of heating, ventilation, and air conditioning (HVAC), lighting, and shading control functions and enables consistent comparison of their energy-related behavior under unified boundary conditions. The results show that the framework effectively captures performance trends associated with increasing automation sophistication and reveals interaction effects between control subsystems that are not accessible in conventional energy simulation tools. The proposed methodology provides a practical and extensible foundation for early-stage, regulation-aligned evaluation of smart building solutions and for the further development of predictive and artificial intelligence (AI)-assisted control concepts.
Journal Article
A cyber-physical system for building automation and control based on a distributed MPC with an efficient method for communication
This paper introduces a cyber-physical system for building automation and control that is developed based on a distributed model predictive control. The implemented distributed method significantly reduces computation overhead with respect to the centralized methods. However, continuous data transfer between subsystems, which are often far from each other, is required when using this method. Information transmission between subsystems is very often subject to the limitations of transmission bandwidth and/or short communication range resulting in significant communication overhead. This causes significant time latency between making measurements and applying control commands, which adversely affects the control performance. Therefore, the distributed method used in this paper implements a two-level communication architecture to reduce the communication overhead. In order to avoid collision in communication inside neighborhood using this method, the TDMA-OFDMA scheme is used for wireless communication between distributed devices. Under these assumptions, the communication overhead is calculated. Then, a novel algorithm for finding the size of neighborhoods resulting in the lowest time latency between making measurements and applying control commands is presented for a typical office building. Finally, the satisfactory performance of the proposed cyber-physical system for the temperature control of a typical office building in the presence of disturbance and model inaccuracy is illustrated using computer simulations.
Journal Article