A BIM-enabled Decision Support System to support large-scale energy retrofitting processes and off-site solutions for envelope insulation

The urgency of renewing the Architecture, Engineering and Construction related processes to increase quality standards and performances while reducing costs and operations time is widely discussed in literature. In this scenario, increasing the energy renovation rate of the existing European building stock is a key priority to support the EU's 2050 decarbonisation targets through innovative solutions. The introduction of prefabricated panels for building renovation – incorporating insulation, mechanical systems, and finishing – can provide the existing buildings with improved structural, thermal, acoustic, and architectural features. The higher quality and safety for the off-site activities, the faster on-site application and the reduction of waste are some advantages of this typology of Modern Methods of Construction (MMC). Several digital and informative tools have been introduced over the last years to customize and integrate the design of prefabricated panels on existing building envelopes (i.e. panelisation tools). However, the comparison of technological alternatives is left to the intuition of designers and managed through the use of several tools that are not interconnected and often downstream the design process. This paper presents a Panelisation Design Tool, which is a Decision Support System (DSS) to help decision-makers in the choice of technological solutions for retrofitting operations during the Early Design Stage. Thanks to BIM integration, some indicators related to different aspects (n Dimensions) are extracted from the model of the panelised building to compare different technologies in a systematic way. The Panelisation Design Tool is tested on a case study building located in the city of Monza, in Northern Italy, used as a pilot in the BIM4EEB European Project. The test aimed at demonstrating the effectiveness of the chosen parameters to evaluate multiple technological solutions in an integrated BIM approach

Optimal preliminary design of variable section beams criterion

The present paper discusses about optimal shape solution for a non-prismatic planar beam. The proposed model is based on the standard Timoshenko kinematics hypothesis (i.e., planar cross-section remains planar in consequence of a deformation, but it is able to rotate with respect to the beam center-line). The analytical solution for this type of beam is thus used to obtain deformations and stresses of the beam, under different constraints, when load is assumed as the sum of a generic external variable vertical one and the self-weight. The solution is obtained by numerical integration of the beam equation and constraints are posed both on deflection and maximum stress under the hypothesis of an ideal material. The section variability is, thus, described assuming a rectangular cross section with constant base and variable height which can be described in general with a trigonometric series. Other types of empty functions could also be analyzed in order to find the best strategy to get the optimal solution. Optimization is thus performed by minimizing the beam volume considering the effects of non-prismatic geometry on the beam behavior. Finally, several analytical and numerical solutions are compared with results existing in literature, evaluating the solutions’ sensibility to some key parameters like beam span, material density, maximum allowable stress and load distribution. In conclusion, the study finds a critical threshold in terms of emptying function beyond which it is not possible to neglect the arch effect and the curvature of the actual axis for every different case study described in this work. In order to achieve this goal, the relevance of beam span, emptying function level and maximum allowable stress are investigated

Estimating preferences for controlling beach erosion in Sicily

This study applied discrete-choice experiments to estimate preferences for a program aimed at reducing the retreatment of the sandy beach at "Lido di Noto", a renowned Sicilian bathing resort close to Noto (Italy). Econometric analysis of data was based on Multinomial Logit (MNL), Latent Class (LC) and Mixed Logit (MXL) models. Findings shown that users appreciated the advancement of the current coastline through nourishment, and negatively perceived the construction of emerged sea barriers. MXL and LC models revealed that preferences were heterogeneous

Enhanced Multi-Strategy Particle Swarm Optimization for Constrained Problems with an Evolutionary-Strategies-Based Unfeasible Local Search Operator

Nowadays, optimization problems are solved through meta-heuristic algorithms based on stochastic search approaches borrowed from mimicking natural phenomena. Notwithstanding their successful capability to handle complex problems, the No-Free Lunch Theorem by Wolpert and Macready (1997) states that there is no ideal algorithm to deal with any kind of problem. This issue arises because of the nature of these algorithms that are not properly mathematics-based, and the convergence is not ensured. In the present study, a variant of the well-known swarm-based algorithm, the Particle Swarm Optimization (PSO), is developed to solve constrained problems with a different approach to the classical penalty function technique. State-of-art improvements and suggestions are also adopted in the current implementation (inertia weight, neighbourhood). Furthermore, a new local search operator has been implemented to help localize the feasible region in challenging optimization problems. This operator is based on hybridization with another milestone meta-heuristic algorithm, the Evolutionary Strategy (ES). The self-adaptive variant has been adopted because of its advantage of not requiring any other arbitrary parameter to be tuned. This approach automatically determines the parameters’ values that govern the Evolutionary Strategy simultaneously during the optimization process. This enhanced multi-strategy PSO is eventually tested on some benchmark constrained numerical problems from the literature. The obtained results are compared in terms of the optimal solutions with two other PSO implementations, which rely on a classic penalty function approach as a constraint-handling method

Experimental Investigation of the Static and Dynamic behaviors of 3D-Printed Shell Structures

Over the last years, several optimization strategies were conducted to find the optimal shape minimizing internal stress or total weight (volume) of shell structures. In recent times, this structure typology gained a great importance among researchers and the scientific community for the renowed interest in the form-findind optimization of column-free space solution for large span roofing constructions. In the present paper, a form-finding of a shallow grid shells was introduced basing on the multy-body rope approach (MRA) for the definitions of vault shapes and different hole percentage. In order to obtain an experimental validation, a physical model was reproduced at the laboratory scale performing ad hoc measurements to compare the observed respect to the simulated behaviour. A 3D printing procedure based on the Fuse Deposition Modeling (FDM) technique in polylactide (PLA) material was used to realise form-works of the cement based blocks of the scaled prototype. Several static and dynamic load configurations are investigated, collecting into a sensitivity analysis the parameters which mainly affect the structural behaviour. To simulate earthquake ground motion an assigned frequency range as dynamic input to the structure was provided by a shaking table. Finally, some preliminary considerations of the dynamic response of the model were provided testing the robustness of the form-finding approach when horizontal load are taken into account

FRCM retrofitting techniques for masonry walls: a literature review and some laboratory tests

The experimental characterisation of externally bonded composite materials as strengthening solutions for masonry structures, such as basalt textile reinforced mortar (BTRM) or fiber reinforced concrete (FRC), has been receiving increasing attention due to their outstanding mechanical performance. Several studies have been demonstrated the efficiency of this retrofitting solution for increasing the mechanical strength and the displacement capacity of masonry material. In this paper the state-of-art of the most relevant achievements in the experimental investigations and numerical analysis of retrofitted masonry wall have been critically reviewed. Firstly, a detailed collection of several experimental tests using different textile reinforced mortar and/or fiber reinforced mortar has been conducted. Special focus has been given to the test set-up and load configuration type adopted for experiments. Subsequently, several modelling techniques have been treated in order to detect the best approach simulating the interaction between reinforcement system and masonry ranging from macro and micro modelling, concentrated and diffused plasticity model and diverse constitutive laws. Finally, an overview of some original experimental outcomes from laboratory tests is presented. This results will play a major role in for the validation of the numerical models for the prediction of the shear strength and the ductile behavior of reinforced masonry that will be developed in a further step of this research

ADVANCED DEEP LEARNING COMPARISONS FOR NON-INVASIVE TUNNEL LINING ASSESSMENT FROM GROUND PENETRATING RADAR PROFILES

Innovative, automated, and non-invasive techniques have been developed by scientific community to indirectly assess structural conditions and support the decision-making process for a worthwhile maintenance schedule. Nowadays, machine learning tools are in the spotlight because of their outstanding capabilities to deal with data coming from even heterogeneous sources and their ability to extract information from the structural systems, providing highly effective, reliable, and efficient damage classification tools. In the current study, a supervised multi-level damage classification strategy has been developed regarding Ground Penetrating Radar (GPR) profiles for the assessment of tunnel lining conditions. In previous research, the authors firstly considered a convolutional neural network (CNN), adopting the quite popular ResNet-50, initialized through transfer learning. In the present work, further enhancements have been attempted by adopting two configurations of the newest state-of-art advanced neural architectures: the neural transformers. The foremost is the original Vision Transformer (ViT), whose core is an encoder entirely based on the innovative self-attention mechanism and does not rely on convolution at all. The second is an improvement of ViT which merges convolution and self-attention, the Compact Convolution Transformer (CCT). In conclusion, a critical discussion of the different pros and cons of adopting the above-mentioned different architectures is finally provided, highlighting the actual powerfulness of these technologies in the future civil engineering paradigm nevertheless

Are protected areas covering important biodiversity sites? An assessment of the nature protection network in Sicily (Italy)

GIS spatial analysis of three indicators (vegetation value, faunal richness and landscape heterogeneity) was used to detect and map High-Value Biodiversity Areas (HVBAs), estimate the coverage of biodiversity in the Sicilian protected areas network, and identify new priority areas that could improve long-term biodiversity conservation outcomes. Findings indicated that only 32% of HVBAs are currently covered by the protected areas network. Hotspot analysis revealed that a modest expansion (less than 1%) in the current extent of protected areas would include a disproportionate amount (56%) of biodiversity hotspots, and identified prioritized candidates HVBAs for designation of new protected areas. © 2018 Elsevier Lt