Improving the energy efficiency of the building sector has become an increasing concern in the world, given the alarming reports of greenhouse gas emissions. The management of building energy systems is considered an essential means for achieving this goal. Predicting indoor temperature constitutes a critical task for the management strategies of these systems. Several approaches have been developed for predicting indoor temperature. Determining the most effective has thus become a necessity. This paper contributes to this objective by comparing the ability of seven machine learning algorithms (ML) and the thermal gray box model to predict the indoor temperature of a closed room. The comparison was conducted on a set of data recorded in a room of the Laboratory of Civil Engineering and geo-Environment (LGCgE) at Lille University. The results showed that the best prediction was obtained with the artificial neural network (ANN) and extra trees regressor (ET) methods, which outperformed the thermal gray box model.

The type and the properties of the soil can potentially intensify the internal forces on buildings during seismic loads. To predict the effects of the soil parameters on the soil–structure interaction of buildings, it is necessary to consider the soil–structure interaction (SSI) in the modeling process. Therefore, this document aims to evaluate the seismic effect on the maximal displacement and inter-story drift, and evaluate the behavior of buildings under the second-order effect known in the literature as the P-delta effect. For this purpose, three cases of buildings with 5, 10 and 15 stories were modelled using a FLAC 2D finite-difference element calculation software with infinite soil conditions, including five types of base with four types of soil (one cohesive soil and three non-cohesive soils) considering the soil–structure interaction and a fixed base (without soil–structure interaction). According to the results for the above-mentioned boundary, as the height of the building increases and due to the weak properties of the soil, we notice that the maximal displacements and inter-story drift increase considerably. To that purpose, we recommend considering the second-order effect in seismic design, especially for non-cohesive soil.

Natural and man-made disasters constitute a considerable threat to humans, especially when intertwined with complex geopolitical situations. Effective decision-making and management during post-disaster reconstruction projects should be based on an effective assessment of damages caused by disasters by considering social, economic, and environmental factors. An analysis of prior research on post-disaster assessment reveals a predominant focus on physical indicators. However, recognizing the crucial role of socio-economic factors in the post-disaster reconstruction process, this paper introduces a comprehensive methodology for evaluating disaster-related damages by considering both physical and socio-economic factors. The proposed method is initiated by identifying relevant physical and socio-economic indicators. These indicators are then synthesized based on the local context and experts’ opinions to derive the Physical Priority Index (PPI) and Socio-Economic Priority Index (SEPI). These indices subsequently guide the prioritization of reconstruction efforts, aligning with the decision-makers’ strategic vision. This method was employed to assess damages stemming from the Beirut port disaster, utilizing three physical indicators and nine socio-economic indicators. The examination of the PPI and SEPI of a major area of Beirut port did not unveil a straightforward correlation between these two indices. The low correlation between these indices increases the complexity of decision-making. However, given the profound socio-economic challenges in Lebanon, this paper recommends placing a higher emphasis on SEPI in the decision-making process. Nevertheless, stakeholders retain the flexibility to tailor their approach by combining PPI and SEPI indicators according to their policies. This adaptive approach ensures a nuanced and contextually relevant decision-making framework.

The last seismic events showed that tunnel lining may suffer extensive damage. Employing numerical modeling has a great importance in predicting the seismic performance of tunnels. This paper tests the tunnel lining of the Zaouit Ait Mellal (ZAM) twin tunnels located between the cities of Marrakesh and Agadir in Morocco. Dynamic analysis was adopted by FLAC 2D software using the finite-difference elements. Four soil cross-sections were chosen, with different support devices installed along the twin tunnels, such as rock bolts and steel ribs. The seismic signals introduced as input were obtained from three different earthquakes: Al Hoceima 2004 in Morocco, EL Centro 1940 in the USA, and Kobe 1995 in Japan. The numerical results show that the deformation of the tunnel lining is more noteworthy in the sections using steel ribs compared to those using rock bolts, which is observed by the large values of relative displacement, reaching 1020 (mm) and 2.29 × 105 (N.m/m) of maximum bending moment. The analysis indicates that these sections present higher vulnerability during an earthquake, which should be considered when looking at the overall safety of the tunnel.

Façades built with integrated passive solar systems hold great promise for improving the energy performance of buildings and achieving indoor comfort conditions. Among these techniques, solar air preheating systems with different configurations have proven their ability to reduce the energy consumption of buildings during the heating season. In this study, we propose a ventilated solar wall (VSW) with a thermal storage unit intended for preheating ventilation air. The final aim of this study is to determine the thermal performance of the studied VSW over a significant time period (during the heating season) under various climatic conditions when it is integrated into the building envelope. Therefore, for this purpose, a simplified model was developed to be coupled to a building energy simulation (BES) code. The results from the detailed steady-state 2D computational fluid dynamics (CFD) model show that the thermal efficiency of the VSW ranged from 55% to 70% as the air mass flow rate increased from 0.008 kg/s to 0.02 kg/s for a surface of 2.15 m². These results were used to evaluate the convective heat transfer coefficients in the two air cavities and to validate the simplified model. The results indicate good agreement between the two models.