Panon Latcharote

This study experimentally evaluates sustainable self-compacting steel fibre-reinforced rubcrete (SCSFRR), incorporating recycled crumb rubber (up to 20% replacement of fine aggregates) and steel fibres (up to 0.75% by volume). Phase 1 assessed flowability/workability (slump flow, V-funnel, L-box, U-box) and hardened properties (compressive/splitting/flexural strengths, density, elastic modulus, brittleness index, and stress–strain behaviour). Phase 2 evaluated deep-beam performance under static and cyclic loading, focusing on load–deflection, shear ductility, and energy absorption. An exploratory analysis showed strong monotonic trends between compressive strength and density, tensile strength, flexural strength, and elastic modulus (R² ≈ 0.90–0.93). However, the limited dataset precludes design-level correlations. A novel shear ductility index is introduced to quantify improvements in deformation capacity due to crumb rubber and steel fibres. Results indicated that crumb rubber significantly enhanced ductility and energy absorption but reduced mechanical strengths, which steel fibres effectively mitigated. An optimal blend comprising 10% crumb rubber and 0.50% steel fibres achieved the best balance, increasing shear ductility by ≈ 22% and nearly doubling energy absorption while maintaining comparable strength to conventional concrete. A cradle-to-gate life-cycle assessment showed that replacing 20% of sand with rubber reduced embodied CO₂ per performance index by up to 42%. These findings indicate that SCSFRR can enable sustainable, resilient structural applications, particularly under seismic, cyclic, and low-rate dynamic loading.

Evaluating the structural performance of low-rise RC buildings with infill walls is an essential issue in Thailand, as most infill walls were not designed for lateral load resistance. The purpose of this study was to predict the structural behavior and illustrate the effects of infill walls. Residential, commercial, and educational buildings were selected as representative buildings with different patterns of infill walls. Based on the results, infill walls contributed to considerable strength and stiffness. Most of the infill walls that affected the low-rise buildings were at the ground floor level. The behavior of the buildings that had a contribution of infill walls was found to be brittle until the infill walls collapsed, and then the buildings became ductile. Some patterns in which infill walls were placed improperly led to a torsional effect, resulting in columns in the affected areas reaching failure criteria more than those without this effect. Considering the NLRHA procedure, only infill walls on the ground floor contributed to the building being subjected to a ground motion. The fully infilled frame tended to reach the infill crack before the other patterns. For the UMRHA procedure, only the first vibration mode was adequate to predict seismic responses, such as roof displacement and top-story drift.

Multiple earthquakes have been felt in high-rise buildings in Bangkok despite the epicenters being far away. Seismic wave recordings in the Bangkok basin show a low-frequency peak. This study uses horizontal-to-vertical spectral ratio (HVSR) analysis and array analysis of ambient vibration data to find the predominant period of the ground and the shear wave velocity profiles at five sites in Bangkok. The accuracy of the accelerometer used for the ambient data recording was verified by comparing results with velocity-meter results. The estimated predominant period was within 0.68–0.86 s. From the array records, dispersion curves of the Rayleigh-wave phase velocity were extracted and inverted for the deep layers. The results show that the shear-wave velocity of the top clay layer is low (82–120 m/s) at depths of 11–14.3 m. The low-frequency peak in the HVSR of the earthquake data, and the sediment layer with low shear-wave velocity implies that Bangkok is at risk of amplification of long-period earthquake waves.

Solvent polarity is important data being used in solvent selections for preliminary engineering design of chemical processes. In this work, a predictive model is proposed for estimating the solvatochromic polarity of electronic transition energy (E_T) of Reichardt indicator for aqueous mixtures. To validate the model, the E_T values of eighteen aqueous mixtures collected from the literature were used. The predictive model provided a good estimation of E_T values with an overall deviation of 2.1%, compared with an ideal model (5.1%) from the mole fraction average. The linear relationship of the contribution factor of hydrogen bond donor interactions (CF^HBD) in the predictive model with Kamlet–Taft acidity was newly proposed in order to extend the model for other aqueous mixtures. The predictive model is applicable to many aqueous mixtures and simply requires three properties of pure components as: (i) E_T values, (ii) gas-phase dipole moment and (iii) Kamlet–Taft acidity.

The estimation of seismic damage to buildings is complicated due to the many sources of uncertainties. This study aims to develop a new approach using an artificial intelligence system called adaptive neuro-fuzzy inference system (ANFIS) model to predict the damage of buildings at urban scale considering input uncertainties. First, the study performed seismic damage evaluation of buildings utilizing the capacity spectrum method (CSM) to obtain a set of 57,648 training data from a combination of three main parameters, i.e., 6 earthquake magnitudes, 8 structural types, and 1,201 distances. Next, the data was used to develop a practical ANFIS model for the seismic damage prediction. The variables of the fuzzy system are earthquake magnitudes, structural types, and distance between epicenter and building. To validate the applicability of the proposed model, analyses of spatial seismic building damage under five possible earthquakes in Chiang Mai Municipality were performed by using the proposed methodology. From the comparison of the damaged urban area, small discrepancies between the CSM and the ANFIS results could be observed. It should be noted that the proposed ANFIS model can predict the seismic building damage reasonably well compared with the CSM. Using the method proposed herein, it is possible to create damage scenarios for earthquake-prone areas where only a few seismic data are available, such as developing countries.