Construction Logistics, Equipment, and Robotics

This paper starts with a short introduction to the subject of construction logistics. It illuminates the differences between a high industry demand for skilled professionals for construction logistics tasks and the restrained offer of such courses in the academic environment. The contrast between construction logistics companies and the limited availability of related university courses backs them up. The authors suggest a lecture structure based on the “construction logistics” course at TUM, which combines theoretical sessions and a simulation part.

Further, the individual session headings are listed together with a brief content description. The study progress of the participants is evaluated via a group project derived from a real-world project. Besides a report of possible examination tasks, a short excursion toward the evaluation criteria is being made. In the end, limitations regarding the validity of course content depending on the region are proposed.

Digitalization in construction logistics ensures greater efficiency.

The starting point of the journey is Bruno Latour’s concept: “On not joining the dots” [1] - or as we claim “on finally joining the dots” - according to which everything tangible and intangible is connected. And connecting the disciplines through data-driven logistics is what this session is all about.

If the key to success is to eliminate redundancies and duplications and to overcome the boundaries within the entire value chain, which in turn means linking all available information and making it universally accessible to determine the most efficient production process - then smart logistics is that key. Data-driven logistics creates a model-based production process (BIM2LOG a “digital twin”) that leverages the “invisible” spaces off-site, which are provided in the form of data and “materialize” on-site.

If smart logistics is the key to success on the construction site and is itself based on a smart city infrastructure, then this infrastructure is critical to the success of a project.

In this paper we will analyze two dimensions in construction logistics: the dimension of time (joining the project stages) from early planning to execution and the dimension of space (joining the construction supply chain) from a consolidation center to the very production on site.

As climate change intensifies and materials become scarcer, there is increasing pressure on the construction industry to find more sustainable solutions for controlled deconstruction and the recovery of building components as a future source of secondary building products. The technical implementation for a robot-assisted deconstruction process of concrete elements is already being investigated. At present, however, there is no continuous flow of information between the data of existing buildings, from which components are removed, and new buildings, into which recovered components are integrated. For the testing process and the approval of the components for reuse, it is crucial to know where the elements come from, how they have been constructed and in which context they are to be reused afterwards. The establishment of a semantic process model to extend the Building Information Model (BIM) is the basis for connecting the information from the different buildings and intermediate inspection processes to enable the approval of the components.

Based on existing achievements, a semantic process model was conceptualised, which enables a linking of the information of the building component along the entire process chain. The process model not only connects the information of the existing building and the new building, but also enables the representation of the intermediate process, for example the testing and transport of the component. It can also be connected with the control system of the cutting robot, hence tool position data can be generated out of the process model. A holistic tracking of the component history, the testing and transport process up to the reinstallation in a new building is feasible.

Intelligence and complexity are increasingly demanded requirements during decision-making. In our former research a software framework has been developed, which enables implementation of process planning in a serious game environment. This method has an advantage in terms of increasing complexity and knowledge retention. Current paper first surveys most important features of serious gaming and points out a lack of the applications directly in planning. In the further part basics of out serious game concept is presented. The method’s applicability for construction logistic planning processes is presented via an example. This relates to a complex process of concrete construction, involving the sites, he concrete batching plant and he raw material supply.

With the advanced information and communications technologies (ICTs) in the construction claim management, such as ontology, data mining, and building information modeling (BIM), many aspects have been improved, including construction claim early identification, analysis and visualization. However, due to the complex and lengthy nature of construction claim procedure, how to execute and visualize it digitally with data traceability is not yet been studied. Integrating these technologies with blockchain and smart contracts, data integrity and process tracing can be ensured for construction claim management. This study proposes a framework to integrate BIM, blockchain, smart contracts and common data environment (CDE) focusing on construction claim procedure generation, execution and visualization. The framework is developed to help improve the traceability, transparency and automation of the construction claim procedure. A case study is illustrated to demonstrate and evaluate the current implementation of the proposed framework. Several aspects for improvement and future directions are discussed in the end.

Site schedules are usually developed by the rule of thumb based on the experience of on-site managers. While this approach can be suitable for smaller job sites, it is challenging to make good decisions for larger projects. Planning errors can result in massive delays and increasing costs. Significant improvements in other industries showed that data-driven productivity analysis of past processes advances the planning and execution of current and future projects. However, in the Architecture, Engineering & Construction (AEC) domain, automated productivity analysis of the construction phase has barely been investigated.

To overcome this deficiency, this paper presents a first approach for multi-level productivity analysis of shell constructions. We discuss several state-of-the-art vision-based technologies that serve as a foundation for large-scale evaluation of the progress on a construction site. A complete pipeline is introduced that uses different types of neural networks to extract productivity information from images at various levels of detail. The proposed workflow is demonstrated for the construction process of cast-in-place concrete pillars, implementing the first two layers. Finally, remaining challenges are discussed.

The development of digital twins (DT) for construction processes requires adequate replication of real-world spaces. Therefore, the use of Building/Civil-Construction Information Modeling (BIM/CIM) for the creation of a digital representation of the physical process and asset plays a vital role. The construction process considered for this research study is shotcrete application and surface finishing during the construction and finishing phases. The research presents the role of adaptive BIM/CIM models for the digital replication of automated shotcreting of civil infrastructure projects. For a digital twin, simulations, and visualizations are essential for the process monitoring and diagnostics alongside the control of the physical asset in real-time through real-world data synchronization. Hence this paper proposes adaptive modeling of civil infrastructures (physical assets) and their associated requirements to facilitate the simulations and visualizations during the digital twining of the related asset and process. The proposed approach takes into account the creation of adaptive BIM/CIM models at the initial stage such as modeling in parts instead of a single element to facilitate the purpose of the visualizations of the digital model in the later stages of the creation of DT. Additionally, the use of an IFC-based hierarchy is prioritized for the purpose of linking the 3D object elements to the corresponding sensor data and simulations. Other aspects taken into consideration are the registration of robots with GIS measurements and the integration of IoT sensors.

Rapid growth of digital technology has facilitated industry progress, while industrial CO₂ emissions are a major issue to be confronted. Digital Twins can play a major role but so far, they have no common norms, standards, or models yet. On top of this, the majority in literature uses the term Digital Twin, but only a few sources are really describing a Digital Twin, whereby it describes a bidirectional data transfer between the real model and the software model. Until now, Digital Twins focus on a single area of interest and do not consider the broader challenge of CO₂ emissions. This study gives an example how to predict CO₂ emissions for the operation of a production site by merging three Digital Models (Building, Production, and Energy Model). This approach demonstrates how CO₂ emissions can be reduced during operation by selecting an appropriate production scenario and a specific energy source mix in the planning phase. The core task is to enable energy demand reduction by simulating different production scenarios and to identify the best energy source mix with the resulting CO₂ emissions visible. The case study shows that by merging the three Digital Models, it is possible to create an overview of the expected CO₂ emissions which can be used as a basis for further developments for Digital Twins. However, the case study has shown that only manual data exchange between the models was possible. Further developments enabling a common data exchange and the connection of the interdisciplinary digital models through a shared language are urgently needed to speed up developments for Digital Twins and shape an interdisciplinary industry approach.

Within this paper, an evaluation of possibilities to connect light construction equipment operating in real-world scenarios to the Industrial Internet of Things (IIoT) is conducted. The focus is on implementing data acquisition and transmission technologies to acquire equipment usage data. Hereby, special attention is given to differences in transmission technologies before the backdrop of their suitability for the use case, focusing on parameters such as energy consumption, cost efficiency, and robustness. The main findings include that Bluetooth Low Energy (BLE) and Low Power Wide Area Networks (LoRaWANs) are well suited for the remote monitoring of light construction equipment. Additionally, creating a data pipeline where devices use already existing infrastructure on larger construction equipment is recommended. Finally, cellular transmission technologies such as NB-IoT and LTE-M are advanced as solutions when the transmission’s reliability and independence from existing infrastructure are focused upon.

Construction sites require improved information logistics in order to obtain an efficient overview of the construction machine and attachment tools available on the construction site as well as their maintenance states. To enable data transfer to the central back office, an information technology integration of the machine and its tools is necessary. OPC UA is considered a potential solution to connect the devices and gather the information required. In this paper, a communication architecture using OPC UA is proposed to connect construction machine and attachment tools on the construction site with the back office. To evaluate the runtime performance of the communication architecture proposed, an experimental setup, which covers the communication on construction sites, is designed to measure the transmission time and data loss of the communication considering different PLC cycle times and data sizes. This paper contributes an order of magnitude for the transmission time and data loss in dependence of the performance class and cycle time of hardware devices.

Activity recognition in construction helps operators and managers by providing information about current and past use of machines and tools. At the same time, excavator attachments enable excavators to perform other physical tasks in addition to earthmoving like screening or compacting. This work addresses the prototype of an activity recognition for excavator attachments independent of the carrier machine. Activity recognition was implemented for buckets, compactors, grabs, and screeners. The recognition is based on acceleration data and angular velocities collected by an inertial measurement unit on the attachment. Decision trees are used for classification. The activity classes “Operation”, “Transport”, and “Rest” were defined as target classes for the activity recognition. The decision trees achieved accuracies comparable to other machine learning algorithms. The results indicate that activity recognition of attachments should distinguish between different attachment classes like buckets and crabs.

New technologies and digital solutions are currently rapidly entering the construction machinery sector. These include (partial) automation solutions for machines and the implementation of the first autonomous construction machines, which no longer require machine operators. The first of these can help relieve the personnel shortage in the future, but qualified skilled workers will still be needed to operate the construction machines. The qualification requirements will increase massively due to the increasing complexity of machine technology and the use of new technologies. Efficient and, above all, safe handling of the new machines and construction processes will be achieved not only by integrating new technologies, functions and machine types into the training and qualification content, but also by adapting the content of these training and qualification programs to ensure a high level of acceptance among operators.

This publication deals with the changes that operators are facing as a result of the digitalization of their occupation and analyzes the resulting challenges for the training and further education of machine operators on the basis of an exemplary activity.

The increasing functionality of automation software in complex mechatronic systems such as construction machinery is a major challenge for companies to remain competitive. A major difficulty is that the software development in construction machinery often involves employees from different disciplines who have technological expertise about the process but little software background. Low-code platforms allow software to be developed intuitively without extensive programming knowledge. However, in mechatronics, the resulting programs are often facing the so-called scaling-up problem that occurs in case highly complex technical processes are implemented using graphical programming languages. This paper thus presents an assistant that supports the programming of automation software on low-code platforms to reduce the complexity of the resulting code. Static code analysis and machine learning are combined to enable predictions about software blocks to be used. For the example of the low-code platform eDesign, a graphical programming platform developed by HAWE Hydraulik SE, it is shown how users of the platform can be assisted in creating maintainable, reusable automation software in the construction machinery sector.

The transportation of construction materials is a crucial part of the construction material supply chain and a major contributor to greenhouse gas emissions from transportation. In Austria, for example, around 11% of the goods transported in 2020 were mineral products, such as glass, cement, lime, and plaster - much of which are demanded by the construction industry. Some of those goods are bulk materials that are well suited for high-capacity means of transport, e.g., trains. However, several system characteristics of the railroad severely limit its use on the last mile to the customer. Here, materials need to be delivered in a timely and efficient manner to ensure that projects stay on schedule and within budget. An eligible solution for this is intermodal transportation, which couples the benefits of efficient rail haulage with flexible road haulage. Nevertheless, conventionally used 30-foot silo containers hinder high utilization of trains due to weight limit excess of trucks. Therefore, a novel 22.5-foot container design for the transportation of cement was introduced recently that enables a high-capacity utilization of trucks and trains. In this article, we present the environmental impact of its use in construction material transportation by quantifying greenhouse gas emissions of an exemplary use case in the Austrian construction industry. Results show emission mitigation potentials of 75% to 93%, depending on several parameters. This article contributes to the scientific literature by bringing evidence on emission reduction potentials in the construction material supply chain and elaborating on the determining factors.

This paper investigates the embedding of Radio Frequency Identification (RFID) transponders in modular textile floor coverings and their integration into the Building Information Model (BIM). The aim of the research was to embed passive RFID tags into modular textile floor coverings (e.g. carpets) and to identify the suitable embedding methods for the RFID tags in order to verify the performance, especially the signal strength and reading performance of the tags.

The RFID reader has a Bluetooth interface and thus enables read IDs to be passed on to Bluetooth-enabled devices such as smartphones or tablets. As a novel approach, BIM models were wirelessly connected to physical building materials such as floor coverings by using RFID tags in a cross-platform application. Application areas include material tracking, warehouse inventory management, transport planning and real-time model-based indoor navigation systems. By embedding of RFID tags into the modular textile floor materials, the information associated with the materials can be more easily identified and retrieved throughout the lifecycle. These tags can be used for the information transfer of product data, e.g. properties that are stored in the product information system, instructions for cleaning and recycling of the materials.

Construction robots aim to automate manual construction processes and to relieve construction workers from physically difficult and monotonous tasks. The field of construction robotics is currently very dynamic, which is reflected in a large number of different systems and prototypes that have been developed in recent years. There are often large differences between the individual robotic systems in terms of economic efficiency, practicality and conformity with applicable standards and laws. The objective of this paper is to identify suitable use cases for the development and deployment of construction robots, to analyze the current state of the art and to clarify the legal framework under which the deployment of highly automated and autonomous systems on construction sites is possible. Based on an analysis and evaluation of industry-specific work processes that are carried out in the construction and expansion of buildings, use cases with a particularly high automation potential are selected. The evaluation of the processes takes into account various aspects, including the complexity of the processes, the potential hazards caused by them and the cost-effectiveness of automation. Furthermore, an overview of technological readiness level as well as the level of automation of current robotic systems is given. Finally, applicable standards, regulations and laws are presented and the conditions under which construction robotics systems can be used on construction sites are explained.

Digitalization and automation are among the most important development trends in the construction machinery industry. Despite the many individual solutions in these areas, the efficient operation of highly automated construction machines on the basis of mixed fleets requires a holistic view of the construction eco system. In this paper, the different levels of digital communication interfaces are classified in a structured manner and transferred to the current state of the art for construction machines. Relevant communication standards and protocols are presented. In order to address the requirements for connected and automated construction machines, two communication standards are presented that have been developed and validated in the joint research project Bauen4.0.

Artificial Intelligence (AI) is one of the most auspicious technologies in the mobile machine domain. It promises to optimize the machine operation to reduce energy consumption or provide an assistant function to support the operator in challenging machine movements. A large amount of machine data is required to train and build AI models. These data sets are often not available due to missing or faulty sensors in the machine. However, construction machines are partly equipped with temporary sensors for data collection so that small data sets are available. Nevertheless, these data sets are very small and must be extended with more realistic data. Generating synthetic data to enrich real data is a promising approach to overcome the obstacle of small data sets. This paper presents a data generator to produce synthetic, physically-informed data for the pendulum trajectory of a flexible attachment tool on a construction machine. The data generator calculates a reference trajectory based on a physical model of the machine. This reference trajectory is generated by solving an optimization problem to cover the machine movement that an experienced machine operator would drive. Reasonable deviations of these trajectories are generated by varying machine characteristics and adding external forces to the physical model to simulate rough environmental conditions. The data generator is implemented for the grab system movement of a civil engineering machine.

This research introduces a multi-agent-based logistics system for collaborative construction robotics to address challenges in efficiency, sustainability, and labour shortages. The system consists of and integrates human and robotic agents, creating redundancy and scalability within the construction process. By leveraging digital twin technology and Building Information Modelling (BIM), the system streamlines the entire logistics chain, from material arrival to construction activities. A key innovation is the integration of a modular space concept, in the present case construction container for material preparation and organization. The multi-agent-based approach controls complex logistics, gathering data from BIM and utilizing the data exchange platform Speckle, ensuring seamless collaboration between human and robotic agents. This research has the potential to significantly impact the construction industry by increasing efficiency, reducing waste, and improving project outcomes. The fully integrated workflow enables a high degree of automation in task planning. The proposed method offers a promising outlook for reshaping construction processes and contributing to a more sustainable and resilient built environment.

The tunnelling performance can only be predicted with limited reliability, since many subjective assumptions must be made (e.g., regarding the soil layers). Therefore, the production and logistic processes must be continuously measured, evaluated, and adjusted. These include the advance rate of the tunnel boring machine (TBM) and the duration of the ring construction. During the planning phase, simulation models are used to estimate the tunneling project duration depending on information from previous projects, geotechnical properties of the soil, and assumptions regarding possible delays and downtimes. The next level of deploying simulation models in the decision-making process during construction is to create real-time simulation models, which can adopt real-time data as inputs at different time points of the project to update the prediction of the performance. In this paper, we present an approach combining the benefits of the two fields of artificial intelligence and simulation modeling to create a real-time simulation model and use the real-time recorded data of a TBM to train machine-learning models. These models predict the advancing speed, ring building duration, and feed this prediction continuously to the simulation model to get the best estimation of the TBM performance in the short term and the project performance in general.

Construction robotics applications are challenged by the unstructured and dynamic nature of the site environment. The technological challenges to realize automation systems in construction can be alleviated by considering modern approaches that exploit digital representations of physical and functional characteristics of buildings, such as Building Information Modeling (BIM). Our proposed approach allows one to ease the deployment of a construction robotics application through the use of BIM. We exploit BIM-data to enable a user-friendly definition of robot operations on-site through a software library which interfaces BIM with the robot-control software. We demonstrate the application of our approach on a robotic spray-painting case study, exploiting the BIM information to achieve path planning of the cartesian trajectory for spray-painting avoiding areas that must not be colored.

Information-driven management and control of physical systems have emerged over the past decade in multiple industrial sectors and more recently also in construction. Such models are called “Digital twins”. However, in the domain of construction, and in particular in its specialty discipline safety, a digital twin (DT) remains rather undefined. Little or no consensus exists among researchers and practitioners of two essential aspects: (a) the connection between the physical reality of a construction site (the “physical” twin) and the corresponding computer model (the “digital” twin) and (b) the most effective selection and exploitation of real-life data for supporting safe design, planning, and execution of construction. This paper outlines the concept for a Digital Twin for Construction Safety (DTCS), defining four essential steps in the DT workflow: (1) safe design and planning for hazard prevention, (2) conformance checking for ensuring compliance, (3) risk monitoring and control for proactive prediction and alerting, and (4) continuous performance improvement for personalized- or project-based learning. DTCS should be viewed as a system-based approach enhancing the overall safety performance rather than exclusively integrating sensing information or safety knowledge in Building Information Modeling (BIM) for safety purposes. The result is an outline of our vision of the DTCS and a description of its modules in essential safety applications. Additionally, we point towards future research and development on this topic.