Abstract
Compaction management, also referred to as intelligent compaction (IC), is a real time automatic operation adjustment and continuous compaction control technology of soils or asphalt layers. It is essentially a technology for optimization and evaluation of the compaction process, being capable of adjusting the compaction energy applied to the material, increasing or decreasing compaction efficiency in the necessary areas according to an acceptance target value, thus attaining maximum stiffness, while preventing overcompaction and minimizing the total number of passes. This study seeks to assess in a case study the IC performance, in comparison with conventional compaction methods in terms of efficiency in compaction of a sandy soil. For this purpose, a specific experimental section was carried out in which the performance of an IC compactor was compared with a conventional heavier class compactor. Data was obtained and analysed by the IC continuous information, as well as by the application of several different conventional compaction control tests and methods. Results show that the IC technology presents a superior performance, as well as various advantages when compared to conventional compactors.

Abstract
The main challenges of earthwork management are to optimize, in terms of time, costs and sustainability, the integration of several different tasks involved in the process. These tasks have to deal mainly with the geomaterials management (influencing excavation and compaction equipment as well as its use as a construction material) and equipment productivity (excavation, transport and compaction), under real environment conditions. To solve this complex management problem, a theoretical integrated system has been developed and evaluated for a database available of a real construction site. This paper describes how the information and communication technologies available in equipment (GPS-sensors supported by tachymeter and laser scanning, derived volume of materials, intelligent compactors, maintenance time) can improve the dynamic optimization process. Indeed, it will be shown in cases where the monitored productivity for the available equipment falls short of or exceeds the estimates performed in design phase, how the necessary actions can be taken towards an optimal redistribution of resources. In summary, this paper presents a framework that combines information and communication technologies and dynamic optimization tools, in order to monitor and optimize the allocation of active equipment throughout a construction site in real-time, allowing savings and benefits for the environment (e.g., carbon emissions).

Abstract
Thin shells are crucially dependent on their shape in order to obtain proper structural performance. In this context, the optimal shape will guarantee performance and safety requirements, while minimizing the use of materials, as well as construction/maintenance costs. Thin shell design is a team-based, multidisciplinary, and iterative process, which requires a high level of interaction between the various parties involved, especially between the Architecture and Engineering teams. As a result of technological development, novel concepts and tools become available to support this process. On the one hand, concepts like Integrated Project Delivery (IPD) show the potential to have a high impact on multidisciplinary environments such as the one in question, supporting the early decision-making process with the availability of as much information as possible. On the other hand, optimization techniques and tools should be highlighted, as they fit the needs and requirements of both the shell shape definition process and the IPD concept. These can be used not only to support advanced design stages, but also to facilitate the initial formulation of shape during the early interactions between architect and structural engineer from an IPD point of view. This paper proposes a methodology aimed at enhancing the interactive and iterative process associated with the early stages of thin shell design, supported by an integrated framework. The latter is based on several tools, namely Rhinoceros 3D, Grasshopper, and Robot Structural Analysis. In order to achieve full integration of the support tools, a custom devised module was developed, so as to allow interoperability between Grasshopper and Robot Structural Analysis. The system resorts to various technologies targeted at improving the shell shape definition process, such as formfinding techniques, parametric and generative models, as well as shape optimization techniques that leverage on multi criteria evolutionary algorithms. The proposed framework is implemented in a set of fictitious scenarios, in which the best thin reinforced concrete shell structures are sought according to given design requirements. Results stemming from this implementation emphasize its interoperability, flexibility, and capability to promote interaction between the elements of the design team, ultimately outputting a set of diverse and creative shell shapes, and thus supporting the pre-design process.

Abstract

Abstract
Intelligent Compaction (IC), which is a part of compaction management, is a real-time automatic adjustment and continuous compaction control technology of geomaterials and asphalt layers. Adjustment of the compaction parameters by the equipment is conducted simultaneously to the compaction process, as well as the continuous measurement of a dynamic compaction value, which is an indicator of the material's degree of compaction. This chapter seeks to assess the advantages and disadvantages of IC, as well as formulating a comparison with conventional compaction methods in terms of efficiency. This goal was achieved through in situ application of various technologies to two distinct types of material: a soil-rockfill mixture and a sandy soil. Data was obtained and analysed by the IC continuous information, as well as by the application of several different conventional compaction control tests and methods. Results show that the IC technology presents a superior performance, as well as various advantages when compared to conventional compactors.

Abstract
Optimal and sustainable allocation of equipment in earthwork tasks is a complex problem that requires the study of several different aspects, as well as the knowledge of a large number of factors. In truth, earthworks are comprised by a combination of repetitive, sequential, and interdependent activities based on heavy mechanical equipment (i.e., resources), such as excavators, dumper trucks, bulldozers and compactors. In order to optimally allocate the available resources, knowledge regarding their specifications (e.g., capacity, weight, horsepower) and the work conditions to which they will be subjected (e.g., material types, required and available volumes in embankment and excavation fronts, respectively) is essential. This knowledge can be translated into the productivity (i.e., work rate) of each piece of equipment when working under a specific set of conditions. Moreover, since earthwork tasks are inherently sequential and interdependent, the interaction between the allocated equipment must be taken into account. A typical example of this is the need for matching the work rate of an excavator team with the capacity of a truck team to haul the excavated material to the embankment fronts. Given the non-trivial characteristics of the earthwork allocation problem, conventional Operation Research (e.g., linear programming) and blind search methods are infeasible. As such, a potential solution is to adopt metaheuristics - modern optimization methods capable of searching large search space regions under a reasonable use of computational resources. While this may address the issue of optimizing such a complex problem, the lack of knowledge regarding optimization parameters under different work conditions, such as equipment productivity, calls for a different approach. Bearing in mind the availability of large databases, including in the earthworks area, that have been gathered in recent years by construction companies, technologies like data mining (DM) come forward as ideal tools for solving this problem. Indeed, the learning capabilities of DM algorithms can be applied to databases embodying the productivity of several equipment types when subjected to different work conditions. The extracted knowledge can then be used to estimate the productivity of the available equipment under similar work conditions. Furthermore, as previously referred, since earthwork tasks include the material hauling from excavation to embankment fronts, it also becomes imperative to analyze and optimize the possible transportation networks. In this context, the use of geographic information systems (GIS) provides an easy method to study the possible trajectories for transportation equipment in a construction site, ultimately allowing for a choice of the best paths to improve the workflow. This paper explores the advantages of integrating the referred technologies, among others, in order to allow for a sustainable management of earthworks. This is translated in the form of an evolutionary multi-criteria optimization system, capable of searching for the best allocation of the available equipment that minimizes a set of goals (e.g., cost, duration, environmental impact). Results stemming from the validation of the resulting system using real-world data from a Portuguese construction site demonstrate the potential and importance of using this kind of technologies for a sustainable management and optimization of earthworks.