Each compactor on the construction site is represented as a software agent within the system. We used Petri nets and algebra of sets to model the internal state of an agent. Each agent holds a set of rolling patterns and is able to choose a pattern that is appropriate to the current situa- tion. The software agent knows about the kinematic characteristics of the compactor that it represents. The agent also holds a representation of the material characteristics subject to physical conditions of the envi- ronment notated as a function for estimating the core temperature of the asphalt regarding to the surface temperature, ambient temperature, wind speed, and incident solar radiation. This function is well-founded on empirical measurements [21]. The agent transforms perceived sen- sory inputs into driving instructions for human compactor operators considering its knowledge about rolling patterns as well as physical causes and effects. In the subsequent section we introduce our artefact
Fig. 4. Model of a software agent.
by presenting the internal architecture of our agents using Unified Modeling Language (UML) diagrams.
3. Framework and innovations of the SmartSite project
The SmartSite approach addresses all steps of the logistics chain starting from the asphalt batch plant to the very end of compaction. During the last step, human compactor operators are supported by an assistance system generating driving instructions based on real-time sensory inputs and computational intelligence.
SmartSite brings together interdependent, interacting value chains, each already proven to be successful inpidually. These value chains come from the fields of construction machines, site environment, con- nected work site as well as construction process monitoring. SmartSite innovatively connects these value chains in order to optimize the entire road construction process. The process and value chains involved are further detailed.
As equipment and plant manufacturers involved in road construc- tion are capable of providing sensor-, quality and process data along the entire process chain, they can also provide direct access to technical- ly sound control systems for asphalt rollers and pavers.
The data transfer between inpidual machines as well as between environment and machines and between machines and construction process control is technically possible. Collected data, especially data pertinent to construction quality, can be analyzed in real-time. The col- lected data includes data for every machine and plant along the entire value chain. Even environmental data like weather conditions and fore- casts as well as process data like work progress can be gathered and communicated real-time.
The collected data is used in project management by the end users (project consultants and general contractors) and for achieving a hitherto unachieved level of automation of the machine layer and the controlling logistics layers.
SmartSite includes upstream plants (i.e., batch plants), the construc- tion site environment (e.g., radio frequency identification tags embed- ded in asphalt) as well as external data (e.g., weather data and traffic data) into the data supply chain. This allows for a more forward- looking construction process control. The utilization of service oriented and cloud based architectures as well as mobile smart-technologies (see Fig. 5) facilitate location-independent access to control-related data for both human actors and machines. This, however, requires intelligent authentication and authorization.
The SmartSite platform integrates the machine control with the BIM planning data, the actual machine data and the digital, model-based construction process control. The aim is to facilitate dynamic, bidirec- tional and IP-based end-to-end communication between machines and construction process control. Existing information models have to be enriched in the quality dimension. Furthermore, SmartSite aims to R. Kuenzel et al. / Automation in Construction 71 (2016) 21–33 建筑自动化英文文献和中文翻译(5):http://www.youerw.com/fanyi/lunwen_204410.html