Quantum computing is expected to offer solutions to computational problems that are otherwise computationally intractable. Although the core technology is still being developed, quantum-inspired computing already has been offering practical advantages for several computationally challenging problems. Despite the promising potential of both quantum computing and quantum-inspired computing, applications in civil engineering remain underexplored. This study aims to lay the foundation for future adoption by introducing the fundamental principles of quantum computing and quantum-inspired computing and by conducting a multivocal literature review. The review provides insights into the current research landscape in civil engineering and offers a detailed analysis of potential use cases and application areas. The findings are expected to serve as a foundation for guiding future research endeavors and practical deployments of quantum computing and quantum-inspired computing in civil engineering, as these technologies continue to mature. • Introduction to quantum and quantum-inspired computing (QC & QiC). • Identifying the pros and cons of QC & QiC given civil engineering (CE) requirements. • Presenting a multivocal literature review on QC & QiC for CE. • Recommending high-potential use cases and QC & QiC approaches in CE • Identifying research topics to advance QC & QiC in CE for real-world impact.

Mathematical formulas give concise representations of a document's key ideas in many natural sciences and engineering domains. The symbols that make up formulas carry semantic meaning that may differ by document or equation. What does ? mean in a given paper? Interpreting the symbols that comprise formulas requires identifying descriptions from the surrounding text. We approach this task of symbol description reading as an application of current AI technologies targeting the tuning of large language models for particular domains and automation of machine learning. Our pipeline integrates AI question answering and natural language processing to read symbol descriptions. We consider extractive and generative AI model variations and apply our pipeline on two example tasks of symbol description reading. Promising results provide motivation for wider deployment for which we describe a microservice architecture and related challenges.

This demo paper discusses a scalable platform for emerging Data-Driven AI Applications targeted toward predictive maintenance solutions. We propose a common AI software architecture stack for building diverse AI Applications such as Anomaly Detection, Failure Pattern Analysis, Asset Health Forecasting, etc. for more than a 100K industrial assets of similar class. As a part of the AI system demonstration, we have identified the following three key topics for discussion: Scaling model training across multiple assets, Joint execution of multiple AI applications; and Bridge the gap between current open source software tools and the emerging need for AI Applications. To demonstrate the benefits, AI Model Factory has been tested to build the models for various industrial assets such as Wind turbines, Oil wells, etc. The system is deployed on API Hub for demonstration.

The recent advances in Graph Neural Networks (GNN) are poised to improve machine learning of IoT systems at the edge. Particularly, GNNs allow modeling the topology of distributed systems, including their physical laws, from sensors data. However, one of the main limitations of using GNNs arises from their adjacency matrix. The adjacency matrix of GNNs needs to be defined a priori and represents the connectivity between the edges of a network. Usually, the adjacency matrix of GNNs consists of binary values that are equal to 1 when two edges are physically connected and 0 otherwise. This representation considers connectivity in terms of proximity and assumes that they are of equal significance. However, in certain applications, areas that are not physically connected can share more properties than physically connected areas. This necessitates new methods for devising the adjacency matrix and leads us to propose an efficient approach for determining the adjacency matrix of GNNs. Our approach extends GNNs in two ways. First, we employ a mechanism that utilizes the time series data at the edges to determine the eigenvalues and eigenvectors associated with each edge, allowing us to compute the proportion of variance. Subsequently, we use the proportion of variance to construct our adjacency matrix. Second, we utilize Dynamic Time Warping (DTW) to cluster related time series at the edge and construct our adjacency matrix. We then integrate the newly derived adjacency matrix into the GNN operating with a sequence to sequence learner to infer insights at the edges. Through extensive experiments, we demonstrate the strength and performance of our proposed GNN approach.

Efficient training of deep learning models from time series data for Internet of Things (IoT) systems requires a good understanding of the domain, particularly if the training is automated for large-scale applications. Heterogeneous graph neural networks (HGNNs) are a promising approach for incorporating domain knowledge into the modeling framework and consequently improving model performance. However, encoding domain knowledge into HGNNs is nontrivial for IoT systems and requires substantial manual effort. This complicates the adoption of HGNNs in practical settings. To overcome this drawback, we propose a framework for the automatic derivation of HGNN features by semantically parsing equations present in scientific and dedicated publications. We encode the derived features considering physical causation from these equations into an HGNN using an underlying Transformer for prediction and anomaly detection. We validate our approach using two IoT use cases, namely, the prediction of the remaining energy in the battery of an electric race car and the anomaly detection during pick and place operations in a robot workcell. We demonstrate that our approach significantly outperforms other competitive techniques.

Time series behavior of gas consumption is highly irregular, non-stationary, and volatile due to its dependency on the weather, users' habits and lifestyle. This complicates the modeling and forecasting of gas consumption with most of the existing time series modeling techniques, specifically when missing values and outliers are present. To demonstrate and overcome these problems, we investigate two approaches to model the gas consumption, namely Generalized Additive Models (GAM) and Long Short-Term Memory (LSTM). We perform our evaluations on two building datasets from two different continents. We present each selected feature's influence, the tuning parameters, and the characteristics of the gas consumption on their forecasting abilities. We compare the performances of GAM and LSTM with other state-of-the-art forecasting approaches. We show that LSTM outperforms GAM and other existing approaches, however, GAM provides better interpretable results for building management systems (BMS).

Relatively tiny examples have demonstrated the potential of cognitive IoT (CIoT) in its full-stack, namely, semantic modeling, learning and reasoning over sensors data, and machine learning, to uncover and expose actionable insights via advanced user interfaces. In this paper, we make the case for the feasibility of CIoT in all of its dimensions. We devise a CIoT architecture that integrates thousands of sensors present in our buildings in order to learn the buildings' behavior and intuitively assist users in diagnosing and mitigating undesired events. With our architecture, we place emphasis on the scalability and flexibility that reduce the configuration effort. The solution shows the potential of CIoT to create highly scalable, adaptable and interactive IoT systems functioning for buildings and capable of addressing the challenges encountered in the realm of homes, Smart Cities and Industry 4.0.

The digital revolution in industry, buildings, and the Internet of Things will massively increase the number of sensor, actuator, and control devices in the near future. This will lead to the further automation of many tasks in the life cycle of the systems from design to commissioning and operation. Device and system models are a key enabler in this goal as they provide a holistic information pool that can be utilized by various tools and users along the life cycle. This paper analyzes the current state of the art of device and system models. It looks across different domains from industrial automation to building automation and sensor networks, defines the common tasks in their life cycle, and classifies the information required to execute and automate them. It compares 24 common device modeling approaches against these requirements and unveils how ready they are for the future and what they lack.

Today's operation of buildings is either based on simple dashboards that are not scalable to thousands of sensor data or on rules that provide very limited fault information only. In either case considerable manual effort is required for diagnosing building operation problems related to energy usage or occupant comfort. We present a Cognitive Building demo that uses (i) semantic reasoning to model physical relationships of sensors and systems, (ii) machine learning to predict and detect anomalies in energy flow, occupancy and user comfort, and (iii) speech-enabled Augmented Reality interfaces for immersive interaction with thousands of devices. Our demo analyzes data from more than 3,300 sensors and shows how we can automatically diagnose building operation problems.

Recent trends in the applications of batteries in the built environment are improving the efficiency of batteries and lowering costs. This paper introduces various types of battery technologies such as sodium sulfur, lithium ion, flow and lead acid batteries and discusses their models. Various applications of batteries such as adaptive battery systems, Battery Electrical Vehicles (BEVs), Battery Energy Storage Systems (BESS), the Internet of Things (IoT), and Smart Grid and Smart Environment applications are also discussed. In their selection and use of batteries, scholars are ultimately looking to maximize occupant comfort whilst keeping costs low and optimizing the energy efficiency of buildings. This paper will provide a review of current trends in this field.

The papers in this special section introduce various new works in the area of semantic technologies in industrial informatics. They address common problems in the state of the art and illustrate the benefit of semantic technologies to automation in many practical scenarios.

The detection and diagnosis of abnormal building behavior is key to further improve the comfort and energy efficiency in buildings. An increasing number of sensors can be utilized for this task but these lead to higher integration effort and the need to capture the sensor interactions. This paper presents a novel diagnostic approach for buildings with complex heating, ventilation, air-conditioning (HVAC) systems. It uses semantic graphs to automatically create the diagnostic model from the building's data points and to identify potential cause-effect-relationships based on past and current time series data. The approach is validated on various simulated examples of a multiroom HVAC control system. The experimental results show that it can diagnose multiple faults with and without delays with high accuracy.

This paper describes the creation of an IoT driven architecture to support the realization of an OpenBMS approach to managing blocks of buildings. The objective is to overcome the complexities of integration, operation and management of heterogeneous building systems by leveraging existing IoT approaches. The goal is to eliminate vertical data silos and enable the holistic management of energy across existing and new building blocks.

The Internet of Things (IOT) continues to transform the world, and in many countries is now an integral part of our everyday lives-influencing everything from the way that we intercommunicate to how we conduct business. Innovators continue to find ways to integrate IOT into uses as far flung as fashion to medicine. This survey looks at how IOT is currently being integrated into the built environment for the purpose of saving energy and improving occupants' livelihoods. In particular, it reviews three technologies that have received a lot of attention in the literature as the future of an IOT mediated built environment. Based on this literature, predictions are made of the likely trends in regards to the future scholarship on these technologies.

Understanding the operation of a building is key for improving it and reducing energy waste. However, today this is a mostly manual task for which domain experts use visual tools to analyze the large amounts of building data. We show how to automate this task by means of pattern extraction techniques. These allow human operators to simply consider well defined data patterns rather than vast amounts of data. Here the manual effort consists in identifying the context in which the patterns occur. In this paper we go one step further and show how we can automatically derive also the context in which building operation patterns occur by considering the influencing factor that govern building operation. We have evaluated our approach for two pattern extraction methods: matrix factorization and clustering. Our experimental results using real world data demonstrate the applicability of our work.

Sensor systems are constantly growing in all application areas and become elements of our environment. Semantic Sensor Networks (SSN) support this development and provide standardized semantic access for reasoning on this information. Unfortunately they do not model internal system knowledge or simple correlations between sensors and hence they cannot be used to automatically perform analytics tasks based on sensor data only. We show how SSN ontology can be extended and demonstrate its benefits for the task of diagnosing smart building problems using real-world data.

Diagnosis is the task of explaining abnormal behaviors of systems like telecommunication, transportation or energy systems. Given a sequence of observations the problem is to determine, online, all faults that are in line with these observations. Many approaches tackle this problem but they either require domain expertise or a formal description of how observations and faults are connected. This limits their scope to the diagnosis of well-understood faults. We address the problem of diagnosing faults that may occur for the first time and present a new diagnosis approach that integrates techniques for analyzing semantic descriptions of observations and faults.

Composing distributed automation systems from components is challenging due to large variety and number of available components on the market. Multiple criteria need to be considered such as functional coverage, price, interoperability, vendor homogeneity, and energy. The paper introduces a novel heuristic approach for selecting devices considering such criteria. The performance of the approach is compared against alternative approaches in practical relevant scenarios. The results show that the approach is able to identify good design solution candidates according to the criteria and outperforms alternative approaches in the combination of quality and computational performance.

The timely detection of abnormal energy usage is one of the major ad-hoc techniques to optimize energy efficiency. Typically an alarm is triggered either by a significant drift from the baseline consumption level or by a period of large variations. In this paper we propose a statistical predictive method for detecting anomalies both in mean and in variation. The criterion behind is based on the prediction intervals (PIs) of the baseline, which is estimated by the Generalized Additive Model (GAM), and of the variations of baseline, which is estimated by the Autoregressive Conditional Heteroscedastic Model (ARCH). Our proposal on systematically studying the time-dependent variations of energy consumption by ARCH is novel. This is of great importance to, technically, guarantee the resulting PIs of baseline is valid and, practically, to reduce the energy cost incurred by oscillation. As a key component of anomaly detection algorithm, we propose to use the residual based bootstrap for the construction of PIs to minimize the bias caused by imposing hypothetical distributions on observations. We illustrate the proposed method with a real-life example on building energy consumption throughout the paper and in addition, justify our approach is theoretically consistent.

This paper provides an overview of the architecture for self-organizing, co-operative and robust Building Automation Systems (BAS) proposed by the EC funded FP7 SCUBA1project. We describe the current situation in monitoring and control systems and outline the typical stakeholders involved in the case of building automation systems. We derive seven typical use cases which will be demonstrated and evaluated on pilot sites. From these use cases the project designed an architecture relying on six main modules that realize the design, commissioning and operation of self-organizing, co-operative, robust BAS.

Modern large buildings like office buildings include a great number of building automation systems like heating, cooling or lighting. To compose them, the design process should be automated, because no system designer have an overall knowledge about all the different available systems on the market. An important problem is the complexity of the optimization of the device selection: many different systems should be integrated, the costs and the number of integrated devices should be minimized, etc. Well-known population-based algorithms are suited to such complex multi-criterial optimization processes. But the most important drawback of these algorithms is their disadvantageous time performance by a high number of available devices or by many criteria. This paper gives a solution to handle this problem by restricting the device domain to provide a good scalability of the device selection.

There is a strong push towards smart buildings that aim to achieve comfort, safety and energy efficiency, through building automation systems (BAS) that incorporate multiple subsystems such as heating and air-conditioning, lighting, access control etc. The design, commissioning and operation of BAS is already challenging when handling an individual subsystem; however when introducing co-operation between systems the complexity increases dramatically. Balancing the contradictory requirements of comfort, safety and energy efficiency and coping with the dynamics of constantly changing environmental conditions, usage patterns, user needs etc. is a demanding task. This paper outlines an approach to the systematic engineering of cooperating, adaptive building automation systems, which aims to formalize the engineering approach in the form of an integrated tool chain that supports the building stakeholders to produce site-specific robust and reliable building automation.

When using product lines, whose variability models are based on derived features, e.g., Simulink variant objects, the dependencies among the features are only described implicitly. This makes it difficult to verify the mapping of the features to the solution space and to create a comprehensive overview of the feature dependencies like in a feature model. In this paper, an OWL-based approach is presented, which permits the automatic verification of the feature mapping and an automatic feature model synthesis for derived features using OWL reasoning and formal concept analysis.

Several ontologies exist that model aspects of home or building automation systems for specific use cases. However, no comprehensive approach exists, that models building automation systems in a modular way to be usable in various use cases and tools. The paper proposes the BASont that addresses various use cases over the life cycle of a building automation system from design, to commissioning, to operation, and refurbishment. The use of the BASont is demonstrated for a data retrieval and self-commissioning use case.

The future of ambient intelligence (AmI) brings new challenges in designing adequate control systems able to handle a diversity of sensors, and actuators. The paper analyzes on the example of a personalized climate control the classification of sensor, actuator and control approaches and derives a system architecture for an AmI-based control system.

Wireless building automation systems are gaining momentum as they promise an easy installation in old and new buildings. But, the design of wireless building automation systems is still an extensive manual process with little to no tool support. In result, the system commissioning ends in trial-and-error set-ups to identify interoperable devices, to solve issues with wireless signal propagation and to understand energy problems of nodes. This paper introduces a holistic design approach that addresses these common issues in wireless building automation system design. It takes up novel design concepts and tools from wired system design and combines them in a holistic tool environment that supports the engineer in his common work flow to efficiently design reliable wireless building automation systems.

This paper introduces a generic framework that is designed to solve complex system design problems by means of optimization approaches. The framework design is made up of components that are responsible for distinct tasks that communicate by extension points. These extension points are for problem specification and setup, different algorithm workflows, display of plots, optimization statistics, display and export of solutions based on a specified representation, and automated performance analysis. Due to the component-based design, the framework allows the definition of customized problem representations with corresponding problem-specific algorithm operations and different solving algorithms, algorithm operations with a flexible execution order by inheritance property. The framework is tested to solve the design problem of building automation systems with different algorithms and problem-specific algorithm operations to compute optimized system designs.

An ongoing topic in wireless sensor networks is the aim to save energy. This often requires optimized protocols with a reduced performance and robustness. One currently successful wireless technology with a very strict low-power design is EnOcean. This paper analyzes its protocol performance by deriving an analytical performance analysis model that expresses different protocol aspects and devices' behaviors in various scenarios. The model is validated against measurements and simulation results. The model and results presented in this paper can be used as guideline for network dimensioning and for separate analysis of specific network configurations.

Function-block-based modeling is often used to develop embedded systems, particularly as system variants can be developed rapidly from existing modules. Generative approaches can simplify the handling and development of the resulting high variety of function-block-based models. But they often require the development of new generic models that do not utilize existing ones. Reusing existing models will significantly decrease the effort to apply generative programming. This work introduces an automatic approach to recognize variants in a set of models and identify the variation points and their dependencies within variants. As result it offers automatically generated feature models and ICCL content to regenerate the given variants.

In the building automation domain, many prefabricated devices from different manufacturers available in the market realize building automation functions by preprogrammed software components. For given design requirements, the existence of a high number of devices that realize the required functions leads to a combinatorial explosion of design alternatives at different price and quality levels. Finding optimal design alternatives is a hard problem to which we approach with a multi-objective evolutionary algorithm. By integrating problem-specific knowledge into variation operations, a promisingly high optimization performance can be achieved. To realize this, diverse variation operations related to goals are defined upon a classification for the exploration and convergence behavior, and applied in different strategies.

Wireless sensor and control networks are attractive for many embedded system applications mainly because they don't need wired connections for communication or energy supply. Therefore, energy efficiency is an elementary requirement of devices and concerns not only the hardware and communication protocols, but also the device applications. Adaptive sampling approaches promise to reduce the energy consumption of applications by adapting sampling and message transmissions. Selecting appropriate approaches and parameters is challenging as it strongly influences their performance. Therefore, this paper compares several adaptive sampling approaches in different realistic closed control loop scenarios from building automation, to develop a guideline for approach selection and parameterization.

Energy efficiency is an elementary requirement for battery-operated wireless sensor networks. Fulfilling this requirement concerns not only the device hardware and communication protocols, but also the device applications. Adaptive sampling approaches allow reducing the number of messages and can therefore be very relevant for sensor networks. This paper evaluates this relevance for different adaptive sampling approaches in two realistic closed control loop scenarios from building automation.

Network performance engineering can verify the design and dimensioning of large-scale control networks like CSMA-based building automation networks. It combines performance analysis with diagnosis methods to evaluate the network utilization and to detect design errors before installation and can therewith save the expenses of overdimensioning and redesign. This paper will develop a diagnosis model based on fault trees that is able to use the huge amount of performance analysis results to identify design errors and analyze their coherences. This enables not only a fast tracing back of fault causes and the derivation of solutions; it can also visualize the fault coherence to the user and help him to understand his design. Additional consulting tools implement best practice strategies, to support the user in parameterization.

Currently many companies that deliver automation equipment intend to grade up their products by providing enhanced e-services like remote maintenance, remote diagnosis or remote consulting. Software agents are an adequate approach to realize these tasks, however, available agent frameworks lack satisfactory support for this domain. In this paper we present the specific requirements for agents in industrial environments and derive a solution consisting of a specialized agent framework, and appropriate design methodology. Further, we provide an expert tool that allows to easily setup the framework for specific use cases, and thus allows instant and efficient deployment.

As shown in previous work, the duty cycle can be adapted to the traffic of the applications in higher OSI layers. In this paper, an analysis approach is shown to determine the static and dynamic behavior of the previously proposed adaptation algorithm. Therefore, we use two different mathematical tool sets: Markov chains and difference equations. Comparisons with simulations results show that the analytic approach delivers results that can properly approximate the adaptation behavior for static and analytic scenarios.

During the design of large technical systems, the use of analytic and simulative models to test and dimension the system before implementation is of practical importance for an efficient and reliable design process. However, setting up the necessary models is time-consuming and therefore often too expensive in practice. Usually most information for modeling is already available in the design tool used to develop such extensive systems and only needs to be extracted for automatic model building. This paper presents an automated modeling approach from an existing design database using the example of a network analysis for building automation fieldbuses. The analysis is based on an analytical decomposition approach that enables fast estimation of performance measures for large-scale networks. The combination of fast analytical algorithms with automatic model generation allows network performance engineering with minimized effort for model generation and analysis.