Complex 3D Structure Research Articles

Estimation of the Achilles tendon twist invivo by individual triceps surae muscle stimulation.

The Achilles tendon (AT) is composed of three distinct subtendons, each arising from one of the three heads of the triceps surae muscles: gastrocnemius medialis (GM), gastrocnemius lateralis (GL), and soleus (SOL). These subtendons exhibit a twisted structure, classified as low (Type I), medium (Type II), and high (Type III) twist, based on cadaveric studies. Nevertheless, the invivo investigation of AT twist is notably scarce, resulting in a limited understanding of its functional significance. The aim of this study was to give insights into the complex 3D AT structure invivo. A total of 30 healthy participants underwent individual stimulation of each of the triceps surae muscles at rest with the foot attached to the pedal of an isokinetic dynamometer. Ultrasound images were captured to concomitantly examine the displacement of the superficial, middle and deep AT layers. SOL stimulation resulted in the highest AT displacement followed by GM and GL stimulation. Independent of the muscle stimulated, non-uniformity within the AT was observed with the deep layer exhibiting more displacement compared to the middle and superficial layers, hence important inter-individual differences in AT displacement were noticeable. By comparing these individual displacement patterns during targeted stimulations with insights from cadaveric twist classifications on each subtendon area, our classification identified 19 subjects with a 'low' twist and 11 subjects with a 'high' twist. These findings enable us to move beyond cadaveric studies and relate the twisted microstructure of the AT invivo to its dynamic behaviour.

Self-healing injectable hydrogels incorporating hyaluronic acid and phytic acid: Rheological insights and implications for regenerative medicine

Eying the increasing impact of hyaluronic acid (HA) and its multifaceted applications, this study employs a non-toxic, one-pot strategy to develop injectable, self-healing hydrogels for biomedical applications. Phytic acid (PA), a plant-derived organic acid with high biocompatibility and numerous hydroxyl groups, can act as a cross-linking agent to form hydrogen-bonded networks with the HA chains. The study examined the optimal mass ratio of HA to PA to achieve superior hydrogel performance. Fourier transform infrared spectroscopy, rheological studies, and thermal analysis confirmed the successful formation of the hydrogels, which exhibited injectability, rapid self-healing, malleability, and elasticity. The investigation of different compositions revealed a sensitive influence of PA on the self-assembly phenomena of HA during flow. SEM cross-section images of the freeze-dried gels revealed a porous surface in the form of an interconnected network of microchannels. In addition, the hydrogel exhibits good tissue adhesion properties and promotes cell proliferation in biocompatibility tests on human gingival fibroblasts. The significance of this study lies in the ability of the proposed materials to be injected, to conform to the complex 3D structure of host tissues as well as their ability to recover after damage, indicating significant potential as scaffolds for wound healing.

Development of conductive carbon-based interdigitated electrode array integrated with microfluidic channel for blood glucose sensing

PurposeThis study aims to provide a microfluidic blood glucose sensing platform based on integrated interdigitated electrode arrays (IDEAs) on a flexible quartz glass substrate, adhering closely to pertinent electrochemical characterizations.Design/methodology/approachSensors are the key elements of the modern electronics era through which all the possible physical quantities can be detected and converted into their equivalent electrical form and processed further. But to make the sensing environment better, various types of innovative architectures are being developed nowadays and among them interdigitated electrodes are quite remarkable in terms of their sensing capability. They are a well-qualified candidate in the field of gas sensing and biosensing, but even their sensitivities are getting saturated due to their physical dimensions. Most of the thin film IDEAs fabricated by conventional optical lithographic techniques do not possess a high surface-to-volume ratio to detect the target specified and that reduces their sensitivity factor. In this context, a classic conductive carbon-based highly sensitive three dimensional (3D) IDEA-enabled biosensing system has been conceived on a transparent and flexible substrate to measure the amount of glucose concentration present in human blood. 3D IDEA possesses a way better capacitive sensing behavior compared to conventional thin film microcapacitive electrodes. To transmit the target biological analyte sample property for the detection purpose to the interdigitated array-based sensing platform, the design of a microfluidic channel is initiated on the same substrate. The complex 3D Inter Digital array structure improves the overall capacitance of the entire sensing platform and the reactive surface area as well. The manufactured integrated device displays a decent value of sensitivity in the order of 5.6 µA mM−1 cm−2.FindingsDevelopment of a low-cost array-based integrated and highly flexible microfluidic biochip to extract the quantity of glucose present in human blood.Originality/valuePotential future research opportunities in the realm of integrated miniaturized, low-cost smart biosensing systems may arise from this study.

Stable Radicals in Dihydrophenazine Derivatives-Doped Epoxy Resin for High Photothermal Conversion.

Organic radicals exhibit great potential in photothermal applications, however, their innate high reactivity with oxygen renders the preparation of stable organic radicals highly challenging. In this work, a series of co-doped radical polymers ares prepared by doping dihydrophenazine derivatives (DPPs) into the epoxy resin matrix. DPPs can form radical species through the electron transfer process, which are further stabilized by the complex 3D network structure of epoxy resin. Experimental results show that the photothermal conversion efficiency is as high as 79.9%, and the temperature can quickly rise to ≈130°C within 60 s. Due to the excellent visible light transmittance and mechanical properties of co-doped systems, this study further demonstrates their practical applications in energy-saving solar windows and thermoelectric power generation.

Construction of coordination polymer [Ni(ppda) (tib) (H2O)]·H2O and photocatalytic degradation of indigo carmine

Semiconductor coordination polymers have garnered significant attention as photocatalysts for wastewater treatment owing to their adaptable structure, expansive specific surface area, and outstanding selectivity, stability, and sensitivity. In this study, coordination polymer [Ni(ppda) (tib) (H2O)]·H2O was synthesized utilizing flexible phenyldiacetic acid (H2ppda) and rigid 1,3,5-tris(1-imidazolyl)benzene (tib). Crystal structure analysis revealed that the three-linked tib ligands and the central Ni(II) ion create a wavy 2D surface. Additionally, cis-ppda2- ligands form a 1D wave chain alongside central Ni(II), which traverse through a 2D layer. Adjacent layers are interlaced via ppda2−, resulting in a complex 3D supramolecular structure. The fundamental framework of [Ni(ppda) (tib) (H2O)]·H2O exhibits stability up to 316 °C and remains stable over 5 cycles, with a band gap energy of 2.9 eV, rendering it a viable candidate for catalytic applications. In aqueous systems, [Ni(ppda) (tib) (H2O)]·H2O demonstrates efficient photocatalytic degradation of Indigo Carmine (IC), achieving a degradation rate of 98.5% and a reaction rate of 0.027 min−1, showcasing commendable selectivity and semiconductor properties. The primary photocatalytic degradation mechanism involves the synergistic formation of ·OH and ·O2- active species through the interaction between [Ni(ppda) (tib) (H2O)]·H2O and H2O2, facilitating the oxidative degradation of dye molecules.

A detailed 3D stoichiometric map of spinels and oxides in structurally critical passivation layers on Inconel 718 superalloy after heating up to 650 °C

This research presents a detailed structure and time evolution for forming the passivation layer during oxidation of Selective Laser Melted (SLM-IN718) and commercially cast (Comm-IN718) Inconel 718 at 650 °C. It reveals that this layer has a complex 3D structure, with a top layer of Fe-Ni Chromite spinels (NiFexCr(2−x)O4) and a sublayer of Cr2O3. Raman maps and statistical analysis estimate x in NiFexCr(2−x)O4 for different heating times. The heating process initially forms iron-rich NiFexCr(2−x)O4 with x≈2, decreasing to ≈1 after 72 h of heating for both Comm- and SLM-IN718. Interestingly, a slightly different result was observed for the heat-treated SLM sample (HTSLM-IN718), where x increased again to 1.2 and 1.4 after 72 h. Secondary Ion Mass Spectrometry (SIMS) results reveal the depth hierarchy of the passivation layer profile, with the chromite layer covering a thicker Cr2O3 layer forming above the niobium-rich intermetallic layer above the alloy. Passivation layers are critical to the endurance of superalloys during extreme thermal cycling conditions. Therefore, by precisely determining the complex stoichiometry and hierarchy of the layer, this study can guide the development of durable superalloys with improved resistance to extreme thermal cycling conditions.

A three-dimensional algorithm for precise measurement of human auricle parameters

Measurement of auricle parameters for planning and post-operative evaluation presents substantial challenges due to the complex 3D structure of the human auricle. Traditional measurement methods rely on manual techniques, resulting in limited precision. This study introduces a novel automated surface-based three-dimensional measurement method for quantifying human auricle parameters. The method was applied to virtual auricles reconstructed from Computed Tomography (CT) scans of a cadaver head and subsequent measurement of important clinically relevant aesthetical auricular parameters (length, width, protrusion, position, auriculocephalic angle, and inclination angle). Reference measurements were done manually (using a caliper and using a 3D landmarking method) and measurement precision was compared to the automated method. The CT scans were performed using both a contemporary high-end and a low-end CT scanner. Scans were conducted at a standard scanning dose, and at half the dose. The automatic method demonstrated significantly higher precision in measuring auricle parameters compared to manual methods. Compared to traditional manual measurements, precision improved for auricle length (9×), width (5×), protrusion (5×), Auriculocephalic Angle (5–54×) and posteroanterior position (23×). Concerning parameters without comparison with a manual method, the precision level of supero-inferior position was 0.489 mm; and the precisions of the inclination angle measurements were 1.365 mm and 0.237 mm for the two automated methods investigated. Improved precision of measuring auricle parameters was associated with using the high-end scanner. A higher dose was only associated with a higher precision for the left auricle length. The findings of this study emphasize the advantage of automated surface-based auricle measurements, showcasing improved precision compared to traditional methods. This novel algorithm has the potential to enhance auricle reconstruction and other applications in plastic surgery, offering a promising avenue for future research and clinical application.

A study of turbulent filaments in the edge plasma of the Wendelstein 7-X stellarator

Filaments are studied by examining fast camera images on the Wendelstein 7-X stellarator. Fast cameras offer a unique perspective, revealing the complex 3D structure of filaments in the entire poloidal cross-section of the plasma. By correlating individual pixels, their location, shape, and movement are analyzed in standard and high-ι configurations. The presence of filaments is not uniform poloidally around. The number of active areas matches the number of magnetic islands in both configurations. Filaments are found to extend to multiple toroidal turns in standard configuration. No time delay is observed between the different toroidal sections. Such behavior is not seen in high-ι configuration. Filaments are observed within and without the edge shear layer, indicated by the direction of their poloidal rotation. Inside the shear layer, their velocity scatters around 1.25 km s−1, accompanied by a lifetime between 80 and 120 µs. Outside, their velocity shows greater absolute values and variance, but still in a few km s−1 range. The similarities and differences between the two configurations are discussed and compared to previous results.

Diversity of 3D APAR and LAI dynamics in broadleaf and coniferous forests: Implications for the interpretation of remote sensing-based products

Forests substantially mediate the water and carbon dioxide exchanges between terrestrial ecosystems and the atmosphere. The rate of this exchange, including evapotranspiration (ET) and gross primary production (GPP), depends mainly on the underlying vegetation type, health state, and the composition of abiotic environmental drivers. However, the complex 3D structure of forest canopies and the inherent top-view perspective of optical and thermal remote sensing complicate remote sensing-based retrievals of biotic and abiotic factors that eventually determine ET and GPP. This study investigates the sensitivity of remote sensing approaches to 3D variation of abiotic and biotic environmental drivers. We use 3D virtual scenes of two structurally different Swiss forests and the radiative transfer model DART to simulate the 3D distribution of solar irradiance and reflected radiance in the forest canopy. These simulations, in combination with LiDAR data, are used to derive the absorbed photosynthetic active radiation (APAR) and the leaf area index (LAI) in 3D space. The 3D variation of both parameters was quantified and analyzed. We then simulated images of the top-of-canopy bi-directional reflectance factor (BRF) and compared them with the hemispheric-conical reflectance factor (HCRF) data derived from HyPlant airborne imaging spectrometer measurements. The simulated BRF data was used to derive APAR and LAI, and the results were compared to their respective 3D representations. We unravel considerable spatial differences between both representations. We discuss possible reasons for the disagreement, including a potential insensitivity of the inherent top-of-canopy view for the real 3D product dynamics and limitations of the processing of remote sensing data, especially the approximation of effective surface irradiance. Our results can help understanding sources of uncertainties in remote sensing based gas exchange products and defining mitigation strategies.

NAJADS: a self-contained framework for the direct determination of astrophysical J-factors

Cosmological simulations play a pivotal role in understanding the properties of the dark matter (DM) distribution in both galactic and galaxy-cluster environments. The characterization of DM structures is crucial for informing indirect DM searches, aiming at the detection of the annihilation (or decay) products of DM particles. A fundamental quantity in these analyses is the astrophysical J-factor. In the DM phenomenology community, J-factors are typically computed through the semi-analytical modelling of the DM mass distribution, which is affected by large uncertainties. With the scope of addressing and possibly reducing these uncertainties, we present NAJADS, a self-contained framework to derive the DM J-factor directly from the raw simulations data. We show how this framework can be used to compute all-sky maps of the J-factor, automatically accounting for the complex 3D structure of the simulated halos and for the boosting of the signal due to the density fluctuations along the line of sight. After validating our code, we present a proof-of-concept application of NAJADS to a realistic halo from the IllustrisTNG suite, and exploit it to make a thorough comparison between our numerical approach and traditional semi-analytical methods.

Bloch Point Quadrupole Constituting Hybrid Topological Strings Revealed with Electron Holographic Vector Field Tomography.

Topological magnetic (anti)skyrmions are robust string-like objects heralded as potential components in next-generation topological spintronics devices due to their low-energy manipulability via stimuli such as magnetic fields, heat, and electric/thermal current. While these 2D topological objects are widely studied, intrinsically 3D electron-spin real-space topology remains less explored despite its prevalence in bulky magnets. 2D-imaging studies reveal peculiar vortex-like contrast in the core regions of spin textures present in antiskyrmion-hosting thin plate magnets with S4 crystal symmetry, suggesting a more complex 3D real-space structure than the 2D model suggests. Here, holographic vector field electron tomography captures the 3D structure of antiskyrmions in a single-crystal, precision-doped (Fe0.63Ni0.3Pd0.07)3P (FNPP) lamellae at room temperature and zero field. These measurements reveal hybrid string-like solitons composed of skyrmions with topological number W = -1 on the lamellae's surfaces and an antiskyrmion (W = + 1) connecting them. High-resolution images uncover a Bloch point quadrupole (four magnetic (anti)monopoles that are undetectable in 2D imaging) which enables the observed lengthwise topological transitions. Numerical calculations corroborate the stability of hybrid strings over their conventional (anti)skyrmion counterparts. Hybrid strings result in topological tuning, a tunable topological Hall effect, and the suppression of skyrmion Hall motion, disrupting existing paradigms within spintronics.

Improved algorithm of finding the next volume in direct CAD method for fusion neutronics analysis

The magnetic confinement fusion device, Tokamak, has a complex 3D geometry structure. As a result, the fusion neutronics geometry modeling process will be time-consuming and labor-intensive. Some researchers have improved modeling methods by using CAD (Computer Aided Design) models, but there is still a need to convert CAD models to CSG (Constructive Solid Geometry) or pre-process CAD models. Based on the existing direct CAD Monte Carlo approach, this paper creates a new algorithm for finding the next volume, which greatly simplifies or even omits the preprocessing process. By forming a grouping bounding box acceleration algorithm, the improved finding next volume performance is adequate to ensure precision and computation speed for complex 3D models. The results are verified by comparing them with a commercial Monte Carlo code, cosRMC, and an example of neutron fluence per particle on real CAD models of the HL-3 first wall and divertor shows a better applicability of this new algorithm.

Laboratory based correlative cryo-soft X-ray tomography and cryo-fluorescence microscopy.

Cryo-soft X-ray tomography is the unique technology that can image whole intact cells in 3D under normal and pathological conditions without labelling or fixation, at high throughput and spatial resolution. The sample preparation is relatively straightforward; requiring just fast freezing of the specimen before transfer to the microscope for imaging. It is also possible to image chemically fixed samples where necessary. The technique can be correlated with cryo fluorescence microscopy to localize fluorescent proteins to organelles within the whole cell volume. Cryo-correlated light and soft X-ray tomography is particularly useful for the study of gross morphological changes brought about by disease or drugs. For example, viral fluorescent tags can be co-localized to sites of viral replication in the soft X-ray volume. In general this approach is extremely useful in the study of complex 3D organelle structure, nanoparticle uptake or in the detection of rare events in the context of whole cell structure. The main challenge of soft X-ray tomography is that the soft X-ray illumination required for imaging has heretofore only been available at a small number of synchrotron labs worldwide. Recently, a compact device with a footprint small enough to fit in a standard laboratory setting has been deployed ("the SXT-100") and is routinely imaging cryo prepared samples addressing a variety of disease and drug research applications. The SXT-100 facilitates greater access to this powerful technique and greatly increases the scope and throughput of potential research projects. Furthermore, the availability of cryo-soft X-ray tomography in the laboratory will accelerate the development of novel correlative and multimodal workflows by integration with light and electron microscope based approaches. It also allows for co-location of this powerful imaging modality at BSL3 labs or other facilities where safety or intellectual property considerations are paramount. Here we describe the compact SXT-100 microscope along with its novel integrated cryo-fluorescence imaging capability.

In Silico Characterization of Plant Salt Tolerance Promoting KDP Proteins from Alcaligenes xylosoxydans

Soil salinity stress is a detrimental factor in crops production. Conventional methods of soil management and reclamation have been proved useless. On the contrary, exploiting the inherent genes and mechanisms of halotolerant bacteria can bring revolution in agriculture. Present study was designed to characterize ATPase dependent protein complexes kdpFABC and kdpDE in a salt tolerant bacterium Alcaligenes xylosoxydans. This complex enables plants to endure the saline environmental conditions through enhancing the K+ ions influx. For characterization, protein sequences of three isoforms of kdpA, four of kdpB, two of kdpC and one of kdpE were retrieved from Uniprot database. These were analyzed via ProtParam tool, AlphaFold protein database and HDOCK server. Highest affinity for ATP molecule was observed in kdpB confirming its reported function of ATP hydrolysis. All documented proteins were found polar (except kdpE), alkaline (except one isoform of each kdpA and kdpB), thermostable, to exhibit complex 3D structure (except for kdpC and E) and in vitro stability. These properties of subunit proteins can be exploited to engineer the complex and produce osmotolerant transgenic plants

A Study on Film Properties Control of Atomic Layer Deposition Amorphous Carbon for PRAM Electrode Application

As the amount of data usage increases with the development of technologies such as artificial intelligence, Internet of Things (IoT), and big data, the need for development of high-performance memory devices and improvement of density is increasing. Phase change memory is a next-generation memory device that stores and erases information by the resistance difference generated by reversibly and rapidly changing the crystal structure of the memory cell. Recently, research on the material engineering aspect to increase thermal efficiency by using various electrode materials and the structural aspect to increase the degree of integration by making a 3D structure is attracting attention. As an electrode material to increase the thermal efficiency of PRAM devices, carbon, which has various resistances (conductor (graphite, sp2 bond) – insulator (diamond, sp3 bond)) depending on the state of atomic bonding is an attractive material. Therefore, research using carbon as an electrode by using PVD and CVD processes has been conducted, but there is a problem that it is difficult to use it for devices with a complex 3D structure with the existing process. Therefore, in this study, an a-C ALD process that can be uniformly deposited in a 3D structure was developed using the CBr4 precursor. And by analyzing the bonding state and physical properties of the deposited thin film, the mechanism of how the a-C thin film grows according to the thin film deposition conditions is studied.

PhaseFIT: live-organoid phase-fluorescent image transformation via generative AI

Organoid models have provided a powerful platform for mechanistic investigations into fundamental biological processes involved in the development and function of organs. Despite the potential for image-based phenotypic quantification of organoids, their complex 3D structure, and the time-consuming and labor-intensive nature of immunofluorescent staining present significant challenges. In this work, we developed a virtual painting system, PhaseFIT (phase-fluorescent image transformation) utilizing customized and morphologically rich 2.5D intestinal organoids, which generate virtual fluorescent images for phenotypic quantification via accessible and low-cost organoid phase images. This system is driven by a novel segmentation-informed deep generative model that specializes in segmenting overlap and proximity between objects. The model enables an annotation-free digital transformation from phase-contrast to multi-channel fluorescent images. The virtual painting results of nuclei, secretory cell markers, and stem cells demonstrate that PhaseFIT outperforms the existing deep learning-based stain transformation models by generating fine-grained visual content. We further validated the efficiency and accuracy of PhaseFIT to quantify the impacts of three compounds on crypt formation, cell population, and cell stemness. PhaseFIT is the first deep learning-enabled virtual painting system focused on live organoids, enabling large-scale, informative, and efficient organoid phenotypic quantification. PhaseFIT would enable the use of organoids in high-throughput drug screening applications.

Hydrotalcite supported cobalt and tungsten catalysts for valorization of lignin into valuable phenolics

Designing of an efficient catalytic system to break down the sub-aromatic units from the lignin complex 3D structure is a challenging task in biorefinery. Herein, we have designed and prepared the cobalt-impregnated hydrotalcite HT500 basic catalysts for the valorization of an industrial lignin. Catalysts were synthesized using base (e.g., NH3, NaOH) and without the use of the base. The 10 wt% Co/HTNaOH catalyst showed excellent liquefaction tendency by yielding maximum bio-oil (72 wt%) together with high selectivity of acetosyringone under mild reaction conditions (70 °C/3h). This may be owing to the efficient breakage of β-O-4 linkages together with the activation of the Ar-Cα-OH bonds. With the incorporation of W (Co-W/HTNaOH), the selectivity towards acetosyringone was enhanced remarkably (65 area%). The 1H, 13C, and 2D-NMR analysis of raw lignin and bio-oil validate the cleavage of Ar-Cα-OH and ether linkages (β-O-4) without disturbing C–C linkages present in sub-aromatic units of lignin e.g., syringyl and guaiacyl. The Co-W/HTNaOH showed good catalytic performance up to two successive cycles upon regeneration. The XRD, BET, TEM, SEM, XPS, and FT-IR characterization of fresh and regenerated catalysts did not show any significant loss in catalytic active sites, indicating good catalyst regeneration.

Drawing 3D fluid patterns in beverages using a robotic nozzle

Existing three-dimensional (3D) printing technologies assume the production of solid structures on a platform, regardless of whether they are edible. In contrast, we establish a technology that enables the construction of a 3D structure made of liquid into a liquid matrix, where both liquids are edible as beverages. The blur caused by the diffusion of ink is suppressed by a constituent particle size design without resorting to macroscopic plasticity, whereas sedimentation or floating is prevented by tuning the mass density. Mixing by the drawing motion is suppressed by the design of the robotic nozzle motion, where the dynamic positioning and orientation of the nozzle are controlled alongside the injection flow rate of the edible ink. As a demonstration, we draw a complex 3D structure of a fish in tea within 40 s, and the pattern maintained apparent clarity over several hours.

Three-dimensional visualization of the vascular bundle in a branched bamboo node

Bamboo is a natural vascular bundle (VB) reinforced composite material used in more than 10 fields such as construction and furniture. The nodes in bamboo are crucial to its mechanical properties, but understanding of its performance is limited by lack of knowledge about the three-dimensional (3D) structure of the node. This work aimed to non-destructively identify the multi-dimensional characteristics of the VB in a bamboo branched node (BN) using X-ray microtomography (µCT). The VB was segmented from the BN using deep learning combined with the Watershed algorithm. The 3D model reconstruction and characterization of the VB were also conducted. It was found that the structure of VBs showed significant changes along the height of the BN. The VBs formed a complex 3D structure, VBs of the culm are connected with those of the branch, and the connectivity of the conducting tissue and fibers was 88.91% and 99.95%, respectively. The conducting tissue and the fibers had similar shapes but varying thicknesses, which enabled VBs to perform both water transport and mechanical support functions. The volumes fraction of parenchyma, fibers, and conducting tissue in the BN were 61.3%, 35.3%, and 3.4%, respectively, but the tissue proportion of the different heights of the BN varied from each other. The nodal ridge was a mechanical weak point of the BN, with a maximum fibers proportion of 43.8%. This study contributes to understanding the relationship of VBs between the branch and the culm. It provides a structural perspective for understanding the mechanical properties of BN and a theoretical basis for optimizing bamboo utilization efficiency.

Augmented deep neural network architecture for assessing damage severity in 3D concrete buildings under temperature fluctuations based on K-means optimization

This research paper introduces a novel approach for forecasting the severity of damage in column elements within a complex 3D concrete structure. The paper presents two innovative methods that have not been previously proposed. Firstly, a new equation is suggested to enhance the sensitivity of the dataset used for training and learning in machine learning techniques. This equation incorporates two crucial dynamic characteristics, namely frequencies and mode shapes. The second method involves a new technique for optimizing the architecture of a deep neural network (DNN) using K-means optimizer (KO), referred to KODNN. By employing KO, the optimal values for the DNN architecture and learning rate are determined. To assess the effectiveness of the proposed method, KODNN is employed to develop a model for predicting the compressive strength of concrete using benchmark datasets. A comparison is made between the obtained results from this example and those obtained using the original DNN model to demonstrate the performance improvement achieved. Finally, KODNN is utilized to predict the severity of damage in elements of a 3D concrete building under various temperature conditions, considering two specific damage cases. The results indicate that the proposed method achieves a high level of accuracy and reliability.